+
+
+
diff --git a/docs/articles/cheese_files/figure-html/unnamed-chunk-8-1.png b/docs/articles/cheese_files/figure-html/unnamed-chunk-8-1.png
index 32aba45..493ebc9 100644
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diff --git a/docs/articles/getstarted.html b/docs/articles/getstarted.html
index 745bbf0..8eaa280 100644
--- a/docs/articles/getstarted.html
+++ b/docs/articles/getstarted.html
@@ -102,6 +102,7 @@ Results +
+For example, when we filter for Actinomycetota (Actinobacteria) as +the functional group, we see that there are no edges connecting to group +10 and group 3- the clusters that have the most features associated with +Actinomycetoa for Cheese sample A (Fig 1.3.A).
+
Fig 1: Dashboard showing Actinomycetota filtered
+network (A) with enrichment pattern for Cheese Sample-A (B) and Cheese
+Sample-C (C); Cluster pattern for Group 9, which also is enriched for
+Type IV secretion genes (D).
Looking at the pattern traces of these groups, (Fig 1.3.B), there is +a peak in samples A4 (week 9) and A5 (week 13), which mirrors the 16S +rRNA results of Saak et al. Since these two clusters do not have edges +connecting them to other groups, this suggests that the Actinomycetoa +features found in these groups follow distinct longitudinal succession +patterns that are independent. When looking at Actinomycetoa within +Cheese Sample C we see a different pattern. Groups 2 and 5, have the +most features associated with Actinomycetoa, but they are highly +connected to the other groups (Fig 1.3.A). From these results, we can +hypothesize that though Actinomycetoa features are more abundant in +later time points for both cheese samples, their dynamics are +differentially influenced.
+The authors found that Type VI secretion was enriched in +Pseudomonadota bacteria (specifically, Psychrobacter), and hypothesized +this enrichment was due to the importance of physical species +interactions that occur with this habitat. Using MolPad, we searched for +other secretion systems associated genes, to understand their dynamics +within the community. Focusing on KEGG annotated Type IV secretion +genes, we found that Group 9 contained 12/13 of these genes. Within this +group, features that cluster are ones that peak in Cheese sample C5 +(week 13, Fig 1.3.D). This is also the most taxonomically diverse +sample. From this, we can hypothesize that increased taxonomic diversity +is also associated with increases in genes that are related to +competitive species interactions.
Installation
library(MolPad)
+
Before Input
@@ -136,6 +137,7 @@ Tips for normalization and imputa
of your analysis to increase the quality of the following
steps.
+
@@ -175,16 +177,18 @@ Multi-omics: Yes or No
Data Format and Pre-process
-
+
Fig 1: Reshape a list of datasets into a
standard format.
+
In the figure, A, B, and C can represent datasets from different
sources or different aspects of measurements. Below are two examples of
what the input data might look like and how to transform it into the
required format through pre_process() modules.
+
If your input is a list of datasets:
@@ -293,61 +297,63 @@ If your input is a list of datasets
#> 8 1.2235141 -1.2241931 0.6790383 -0.28487806 -0.74817082 DataSource_A
#> 9 1.1466328 -1.4265286 0.5357111 -0.51799361 -0.52413804 DataSource_A
#> 10 1.1813273 -1.2944121 0.6417621 -0.46906150 -0.66447461 DataSource_A
+
If your input is a single dataset:
Here is a dataset that already includes a labeled ‘type’ column:
-#> ID Day_1 Day_2 Day_3 Day_4 Day_5 Day_6
-#> 1 1 NA NA 0.104083550 -1.07999900 1.3416214 -1.733564471
-#> 2 2 NA NA 0.882578509 0.08440181 0.7165712 0.004811372
-#> 3 3 0.40203917 NA 0.675076590 0.97017740 -0.8210872 0.659073443
-#> 4 4 1.02859068 0.30178418 0.412617771 0.39306149 -0.3427640 0.837009714
-#> 5 5 -2.14036218 -0.06315709 0.656982200 -0.18301850 -1.3967562 -0.123327229
-#> 6 6 -2.04354192 1.30960484 1.371349945 -1.67403067 -1.2946150 0.760315504
-#> 7 7 0.31164367 2.26615374 0.972843085 -0.03956293 1.0190965 1.180680430
-#> 8 8 -0.79692858 -0.86038791 -3.417872877 0.62118867 -2.1983197 -0.104229763
-#> 9 9 -0.12033042 1.17585682 -0.001075426 0.01122359 0.3430992 -0.189234532
-#> 10 10 0.02592915 0.48551867 0.841254390 -0.46430744 0.6891800 0.751766756
-#> Day_7 Day_8 type
-#> 1 0.4638261 2.1318716 peptide
-#> 2 -0.2949926 -0.9310538 peptide
-#> 3 -0.2520195 0.7809977 peptide
-#> 4 -1.0120371 -0.1162361 peptide
-#> 5 -2.0085970 -0.8959521 peptide
-#> 6 0.7874222 -0.5106016 peptide
-#> 7 -0.7719437 -1.4686258 peptide
-#> 8 0.3541200 0.8224521 lipid
-#> 9 0.5674059 -0.9313882 lipid
-#> 10 -0.4097464 NA metabolite
+#> ID Day_1 Day_2 Day_3 Day_4 Day_5 Day_6
+#> 1 1 NA NA -0.82919412 0.08237778 1.30438699 0.4875914
+#> 2 2 NA NA -0.41606256 1.42523475 -0.44792066 1.1425154
+#> 3 3 1.0354708 NA 0.78841026 -1.49585251 -0.41361867 -0.3271995
+#> 4 4 -0.3005323 0.6998150 -1.86018783 1.14167433 -1.04204735 -2.4930409
+#> 5 5 -0.1307903 0.7839983 0.25834857 0.62421147 -0.07202124 -0.9214203
+#> 6 6 -0.9910321 1.8059465 -1.25395397 -0.45260649 -0.57303842 0.9990113
+#> 7 7 -0.9160734 1.4475673 -3.10452531 -0.06560360 -1.27374744 -0.5703149
+#> 8 8 -0.7381729 -0.4844302 1.10281675 0.12049188 0.20892638 -0.3115689
+#> 9 9 -0.2581716 -0.5906591 0.09794986 2.44249606 1.13135657 0.4886958
+#> 10 10 1.8831483 -1.2490571 0.55981559 -2.31731640 0.97323395 -0.7994677
+#> Day_7 Day_8 type
+#> 1 -1.4632070 0.05832042 peptide
+#> 2 -0.4292379 -0.79047610 peptide
+#> 3 -0.3508094 -0.34995898 peptide
+#> 4 -0.3011731 0.67869535 peptide
+#> 5 1.1761504 -0.86303628 peptide
+#> 6 -0.8993647 -0.44801227 peptide
+#> 7 1.1593812 -0.90149427 peptide
+#> 8 0.5463595 -1.51687039 lipid
+#> 9 -0.5016034 -0.08241911 lipid
+#> 10 -0.9442708 NA metabolite
- after
pre_process():
x1 <- pre_process(x, replaceNA = TRUE,scale = TRUE)
head(x1,10)
-#> ID Day_1 Day_2 Day_3 Day_4 Day_5 Day_6
-#> 1 1 -0.12499488 -0.1249949 -0.04022866 -1.00455207 0.9676290 -1.53681943
-#> 2 2 -0.10246242 -0.1024624 1.46237249 0.04718422 1.1680372 -0.09393173
-#> 3 3 0.16412393 -0.4940273 0.61109508 1.09418472 -1.8381738 0.58489741
-#> 4 4 1.27432720 0.1728189 0.34079228 0.31115385 -0.8040231 0.98397755
-#> 5 5 -1.35954882 0.7001734 1.41425156 0.58132077 -0.6222015 0.64050965
-#> 6 6 -1.35505505 1.0595199 1.10398207 -1.08897287 -0.8157586 0.66398092
-#> 7 7 -0.10291149 1.5438734 0.45418630 -0.39882285 0.4931574 0.62930098
-#> 8 8 -0.06748422 -0.1105542 -1.84632390 0.89499455 -1.0186109 0.40265173
-#> 9 9 -0.36947382 1.7376966 -0.17560476 -0.15561064 0.3839091 -0.48148903
-#> 10 10 -0.41003145 0.4704748 1.15201231 -1.34925296 0.8606601 0.98056711
+#> ID Day_1 Day_2 Day_3 Day_4 Day_5 Day_6
+#> 1 1 0.05451890 0.05451890 -0.9508450 0.1543986 1.6360347 0.64570353
+#> 2 2 -0.07554744 -0.07554744 -0.5950354 1.7039742 -0.6348129 1.35097605
+#> 3 3 1.49874204 0.17759695 1.1835204 -1.7309438 -0.3501343 -0.23987305
+#> 4 4 0.10376561 0.87801613 -1.1033792 1.2200072 -0.4701534 -1.59319589
+#> 5 5 -0.31530188 0.89803359 0.2008348 0.6860993 -0.2373532 -1.36395879
+#> 6 6 -0.71746086 1.90776216 -0.9642373 -0.2120986 -0.3251351 1.15037885
+#> 7 7 -0.27002806 1.37506283 -1.7931878 0.3218978 -0.5189688 -0.02938062
+#> 8 8 -0.74469269 -0.43190500 1.5246885 0.3137802 0.4227931 -0.21881953
+#> 9 9 -0.58919072 -0.91616395 -0.2389755 2.0666865 0.7772922 0.14528989
+#> 10 10 1.56649401 -0.74805373 0.5886154 -1.5374455 0.8941115 -0.41582898
#> Day_7 Day_8 type
-#> 1 0.2527477 1.6112132 peptide
-#> 2 -0.6254920 -1.7532453 peptide
-#> 3 -0.9065915 0.7844915 peptide
-#> 4 -1.8183370 -0.4607098 peptide
-#> 5 -1.2288927 -0.1256124 peptide
-#> 6 0.6835002 -0.2511966 peptide
-#> 7 -1.0158949 -1.6028889 peptide
-#> 8 0.7137346 1.0315924 lipid
-#> 9 0.7485573 -1.6879846 lipid
-#> 10 -1.2447220 -0.4597079 metabolite
+#> 1 -1.7195596 0.1252300 peptide
+#> 2 -0.6114859 -1.0625212 peptide
+#> 3 -0.2699967 -0.2689116 peptide
+#> 4 0.1032697 0.8616699 peptide
+#> 5 1.4181669 -1.2865208 peptide
+#> 6 -0.6314225 -0.2077865 peptide
+#> 7 1.1744856 -0.2598810 peptide
+#> 8 0.8387456 -1.7045902 lipid
+#> 9 -0.8285852 -0.4163531 lipid
+#> 10 -0.5228315 0.1749388 metabolite
+
@@ -375,6 +381,7 @@ Choose Your AnnotationgAnnotation() function.
Annotation_path_taxon <- gAnnotation(annotations,"phylum","class")
+
Network Generation
@@ -388,12 +395,13 @@ Network Generation
Fig 2: Each cluster centroid is predicted from
all the other cluster centroids.
+
To determine which feature clusters are predictive of a given
trajectory, the Mean Decrease Accuracy of a subset of top predictors
whose expression directly influences the expression of the target
@@ -405,8 +413,11 @@
Network Generationnetwork_result <- cluster_result |>
gNetwork(ntop = 3)
-gNetwork_view(network_result)
+gNetwork_view(network_result)
+#> Warning: Removed 1 rows containing missing values (`geom_segment()`).
+#> Warning: Removed 1 rows containing missing values (`geom_point()`).
+
Dashboard Navigation
@@ -421,17 +432,19 @@ Dashboard Navigationnetwork_result,
id_colname = "ko_term" ,
id_type = "KEGG")
-
+
Fig 3: gDashboard() function
+
Once you’ve launched the Shiny dashboard, you can zoom in or make
adjustments to explore interesting findings within your data. To
effectively navigate the dashboard generated by MolPad, you’ll follow
three main steps:
-Choose a Primary Functional Annotation and Adjust Edge Density
+1. Choose a Primary Functional Annotation and Adjust Edge
+Density
Start by selecting a primary functional annotation from the available
options. Then, fine-tune the edge density by adjusting the threshold
@@ -440,14 +453,14 @@
Choose a
annotation.
-Explore the Network
+2. Explore the Network
Brushing on the network unveils patterns of taxonomic composition and
typical trajectories. You can also zoom into specific taxonomic
annotations by applying filters.
-Investigate Feature Details and Related Function Annotations
+3. Investigate Feature Details and Related Function Annotations
Delve into the feature table to examine the specifics of the features
within the selected clusters. Explore additional related function
@@ -457,11 +470,13 @@
Investigat
to answer specific questions, such as comparing pattern distributions
between two functions or identifying functionally important community
members metabolizing a feature of interest.
+
Fig 4: MolPad Dashboard Overview.
+
@@ -472,19 +487,47 @@ Related Visualization Outputs
Network Plot
+The network plot is a powerful visualization tool that displays the
+relationships between different groups or features within your data. In
+MolPad, the network plot helps to identify clusters of features that
+share similar patterns, revealing underlying connections that might not
+be immediately obvious. By visualizing these connections, users can gain
+a clearer understanding of the structure within their data, making it
+easier to pinpoint significant associations and trends.
+
Stack Bar Plot
+The stacked bar plot provides a detailed view of the composition of
+each cluster or group in your dataset. By stacking different categories
+on top of each other within a single bar, this plot allows for a quick
+comparison of relative proportions across multiple groups. This is
+particularly useful in microbiome experiments where understanding the
+distribution of taxa across different conditions or time points is
+crucial. The stacked bar plot makes it easy to see how these
+distributions change between experimental conditions, facilitating
+deeper insights.
+
Ribbon Plot
+The ribbon plot is designed to visualize changes over time, making it
+an ideal tool for tracking longitudinal data. In MolPad, the ribbon plot
+illustrates how the abundance or expression levels of features vary
+across different time points or conditions. The smooth, flowing design
+of the ribbons helps to emphasize trends and patterns, enabling users to
+quickly identify periods of significant change or stability. This plot
+is particularly advantageous when comparing multiple groups, as it
+clearly shows overlapping trends and divergences, providing a
+comprehensive view of temporal dynamics in the data.
diff --git a/docs/articles/getstarted_files/figure-html/unnamed-chunk-14-1.png b/docs/articles/getstarted_files/figure-html/unnamed-chunk-14-1.png
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diff --git a/docs/articles/getstarted_files/figure-html/unnamed-chunk-16-1.png b/docs/articles/getstarted_files/figure-html/unnamed-chunk-16-1.png
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diff --git a/docs/articles/whymolpad.html b/docs/articles/whymolpad.html
index 851cf81..d9fd93d 100644
--- a/docs/articles/whymolpad.html
+++ b/docs/articles/whymolpad.html
@@ -87,17 +87,22 @@
Why MolPad?
-With the increasing multi-omics data and longitudinal designs integrated
-into microbiome experiments, there is a growing need to present the
-network, especially with complex variations across biological
-modalities. Network perspective helps detect the underlying
+With the increasing multi-omics data and longitudinal designs
+integrated into microbiome experiments, there is a growing need to
+present the network, especially with complex variations across
+biological modalities. Network perspective helps detect the underlying
co-occurrence among microbiome samples, allowing for high-level insights
into the global structure. Yet when it comes to experimental data that
records time series for 100,000 features, the network will collapse into
-some entangled clumps and therefore unable to read.
-
+some entangled clumps and therefore unable to read.
+
+
+
+
+Improvements
+
For the aim of network interpretation, MolPad shows improvements in 3
important aspects:
@@ -117,6 +122,7 @@ Why MolPad?
Key Features
@@ -147,16 +153,21 @@ Key Features
What does it look like?
+Now, let’s see what you can get from the dashboard. We’ll start with
+an overview and then demonstrate how to discover patterns within your
+data.
MolPad Dashboard Overview
+
Above is the overview of the MolPad Dashboard. To explore the
dashboard effectively, you can start by following the sequence A-B-C-D.
This approach will guide you through the cluster-level network,
@@ -170,6 +181,7 @@
Discovering related patterns
+
Here is a short example of discovering related patterns using the
network plot: The shade of the edges represents the proximity of nodes.
In the brushed area, Groups 1-7-8 (circled by solid black lines) and
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
index 24820a2..106fea2 100644
--- a/docs/pkgdown.yml
+++ b/docs/pkgdown.yml
@@ -6,7 +6,7 @@ articles:
cheese: cheese.html
getstarted: getstarted.html
whymolpad: whymolpad.html
-last_built: 2024-08-15T06:34Z
+last_built: 2024-08-15T07:35Z
urls:
reference: https://kaiyanm.github.io/MolPad/reference
article: https://kaiyanm.github.io/MolPad/articles
diff --git a/docs/reference/figures/cheesecase.png b/docs/reference/figures/cheesecase.png
deleted file mode 100644
index d91f265..0000000
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diff --git a/docs/reference/figures/cheesecase1.png b/docs/reference/figures/cheesecase1.png
new file mode 100644
index 0000000..6d4d8aa
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diff --git a/docs/reference/gClusters-1.png b/docs/reference/gClusters-1.png
index 993d2a4..463b550 100644
Binary files a/docs/reference/gClusters-1.png and b/docs/reference/gClusters-1.png differ
diff --git a/docs/reference/gClusters.html b/docs/reference/gClusters.html
index 28642a8..566cc47 100644
--- a/docs/reference/gClusters.html
+++ b/docs/reference/gClusters.html
@@ -102,70 +102,70 @@
Examplesreslist <- gClusters(test_data_processed)
# k-means result
reslist[[1]]
-#> K-means clustering with 20 clusters of sizes 7, 6, 7, 10, 8, 2, 3, 5, 4, 6, 10, 3, 3, 4, 4, 6, 4, 3, 2, 3
+#> K-means clustering with 20 clusters of sizes 6, 3, 6, 5, 5, 8, 6, 5, 5, 6, 4, 11, 3, 3, 3, 2, 4, 2, 2, 11
#>
#> Cluster means:
-#> T1 T2 T3 T4 T5 T6
-#> 1 2.22789830 0.02950883 -0.75836275 -0.2619747 -0.5953255 -0.70974025
-#> 2 2.77349939 -0.21724464 -0.35972402 -0.4172516 -0.5000122 -0.11678355
-#> 3 0.93534606 -0.39771771 0.31238452 1.1531782 1.1995301 -1.05203742
-#> 4 -0.27715339 -0.39760069 -0.06466866 1.3995407 2.1316423 -0.28068841
-#> 5 0.11559709 -0.19000668 -0.25629794 -0.1487697 -0.4383348 2.58301481
-#> 6 -0.27847690 -0.82258408 -0.82258408 -0.8225841 1.2338761 -0.82258408
-#> 7 0.26998015 -0.08094677 -0.28926312 -0.7813127 -0.6065051 -0.14945230
-#> 8 -0.81355388 -0.84696982 -0.32573017 1.1218534 2.0955812 -0.56207309
-#> 9 1.45173820 0.26236463 1.31600581 -0.2531012 0.2004547 -1.12389420
-#> 10 0.34700978 0.13673201 0.82464908 0.2571569 1.2656031 0.63541302
-#> 11 -0.19020083 -0.69654961 -0.66836949 -0.4657663 -0.3851487 -0.55904213
-#> 12 1.62582942 1.51619556 -0.35316244 0.2978109 -0.1777508 0.02299732
-#> 13 -0.09187847 -0.87257293 -0.87257293 -0.8725729 -0.5696817 2.05668333
-#> 14 0.85679806 -0.37993358 -0.46099690 0.3934155 0.5857582 1.85027957
-#> 15 1.12205113 0.09185659 -0.89815993 -0.7233153 -0.5717924 -0.86037548
-#> 16 2.09193480 -0.33320285 -0.50786563 -0.5579659 -0.5579659 1.54559773
-#> 17 1.56599593 -0.49119311 -0.83000857 -0.6160199 -0.2092753 1.19151568
-#> 18 0.89463785 0.01698143 -0.57023879 -1.1815834 -1.1815834 1.18824796
-#> 19 -0.13543375 -0.90710305 -0.66248003 -0.4178570 -0.6624800 1.37010464
-#> 20 1.31631350 -0.76059916 -0.76059916 1.5193444 0.6006058 0.84385791
-#> T7 T8 T9 T10
-#> 1 -0.567081109 0.132060988 0.03929621 0.46372005
-#> 2 -0.357532815 -0.298895212 -0.29119673 -0.21485863
-#> 3 -1.222209771 -0.334925678 -0.36853535 -0.22501291
-#> 4 -0.548494719 -0.646992821 -0.66392148 -0.65166279
-#> 5 -0.006327004 -0.327677972 -0.66559889 -0.66559889
-#> 6 -0.822584085 1.233876127 1.23387613 0.68976894
-#> 7 -0.763902351 -0.412975440 0.39220455 2.42217307
-#> 8 -0.651292750 -0.498839228 0.42446013 0.05656427
-#> 9 -0.987867401 -0.340013670 -0.33085684 -0.19483004
-#> 10 -1.004339966 -0.620506186 -0.71949086 -1.12222690
-#> 11 -0.566522777 0.002653353 1.73917982 1.78976662
-#> 12 -0.770498738 -0.620423681 -0.77049874 -0.77049874
-#> 13 0.882337606 0.086536293 0.12686085 0.12686085
-#> 14 -0.526130916 -0.896529515 -0.89652951 -0.52613092
-#> 15 -0.822591022 0.091471523 1.00908283 1.56177203
-#> 16 -0.425513174 -0.557965933 -0.30571480 -0.39133827
-#> 17 0.774154665 0.196302112 -0.62663627 -0.95483532
-#> 18 0.894637851 -0.836550610 0.41343688 0.36201421
-#> 19 -0.907103054 -0.417856999 1.37010464 1.37010464
-#> 20 -0.666108041 -0.666108041 -0.66610804 -0.76059916
+#> T1 T2 T3 T4 T5 T6
+#> 1 2.0919348 -0.33320285 -0.50786563 -0.5579659 -0.5579659 1.5455977
+#> 2 0.8277105 1.32036794 -0.09261332 1.2325986 0.4199176 -0.9576634
+#> 3 0.5077879 -0.24564283 0.53503708 0.2363183 1.0793829 1.3583848
+#> 4 0.2914366 -0.41308350 -0.76541563 -0.9724353 -0.9724353 1.6040887
+#> 5 1.2081037 -0.66790409 -0.66790409 -0.9275526 -0.4204221 1.2719890
+#> 6 0.1155971 -0.19000668 -0.25629794 -0.1487697 -0.4383348 2.5830148
+#> 7 1.2574004 -0.64914997 -0.87502695 0.9353966 0.4760273 1.2681050
+#> 8 0.9389212 -0.86278238 0.43887930 1.0009533 1.3733646 -1.0423264
+#> 9 -0.8135539 -0.84696982 -0.32573017 1.1218534 2.0955812 -0.5620731
+#> 10 -0.3802219 -0.36840199 0.02254271 1.6459653 1.9633618 -0.3246455
+#> 11 -0.1225506 -0.44139873 -0.19548572 1.0299038 2.3840629 -0.2147528
+#> 12 2.5784883 -0.02446156 -0.60355041 -0.2547486 -0.5120230 -0.3947043
+#> 13 0.9922755 0.32399254 -0.94565021 -0.7125240 -0.5104935 -0.9456502
+#> 14 0.2699801 -0.08094677 -0.28926312 -0.7813127 -0.6065051 -0.1494523
+#> 15 1.7947637 -0.47746004 -0.52783931 -0.7635958 -0.7635958 -0.6438957
+#> 16 0.1789247 1.26157166 0.82115649 0.3899368 1.2707671 -0.2614905
+#> 17 1.4517382 0.26236463 1.31600581 -0.2531012 0.2004547 -1.1238942
+#> 18 -0.2784769 -0.82258408 -0.82258408 -0.8225841 1.2338761 -0.8225841
+#> 19 2.1235865 1.05868538 -0.39467611 0.1315587 -0.1315587 0.3946761
+#> 20 -0.2262821 -0.73107613 -0.66098092 -0.4323193 -0.4035074 -0.3836848
+#> T7 T8 T9 T10
+#> 1 -0.425513174 -0.557965933 -0.30571480 -0.391338269
+#> 2 -1.160107459 -0.295057401 -0.64757654 -0.647576540
+#> 3 -1.006282312 -0.766188425 -0.64803067 -1.050766722
+#> 4 0.301678216 -0.371684852 0.66435231 0.633498705
+#> 5 1.209152596 -0.095960244 -0.32347149 -0.586030729
+#> 6 -0.006327004 -0.327677972 -0.66559889 -0.665598891
+#> 7 -0.354972016 -0.601904415 -0.82778139 -0.628094550
+#> 8 -1.159101258 -0.345888528 -0.27147562 -0.070544200
+#> 9 -0.651292750 -0.498839228 0.42446013 0.056564270
+#> 10 -0.647007074 -0.667438225 -0.63229313 -0.611861976
+#> 11 -0.400726188 -0.616324716 -0.71136401 -0.711364008
+#> 12 -0.526022073 -0.185303954 -0.06989626 -0.007778173
+#> 13 -0.945650212 0.121962031 1.19430596 1.427432164
+#> 14 -0.763902351 -0.412975440 0.39220455 2.422173070
+#> 15 -0.260645101 0.389799714 -0.08327821 1.335746686
+#> 16 -0.912917717 -0.481698036 -1.13312530 -1.133125299
+#> 17 -0.987867401 -0.340013670 -0.33085684 -0.194830041
+#> 18 -0.822584085 1.233876127 1.23387613 0.689768939
+#> 19 -0.795567972 -0.795567972 -0.79556797 -0.795567972
+#> 20 -0.612869917 -0.006483243 1.70560793 1.751595921
#>
#> Clustering vector:
#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
-#> 11 16 6 10 10 11 13 12 4 8 3 7 15 2 3 8 5 11 9 1
+#> 20 1 18 16 3 20 5 19 10 9 8 14 15 12 2 9 6 20 17 12
#> 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40
-#> 17 11 5 16 2 8 20 1 5 4 17 4 10 6 4 10 3 15 11 12
+#> 7 20 6 1 12 9 7 12 6 11 5 11 16 18 10 3 8 13 20 2
#> 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
-#> 5 16 11 11 14 11 5 14 16 5 1 17 18 15 5 17 13 4 1 4
+#> 6 1 20 20 7 20 6 3 1 6 15 5 4 13 6 5 4 10 12 10
#> 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80
-#> 3 14 20 15 10 16 2 14 9 9 7 8 5 12 7 4 3 4 3 20
+#> 8 3 7 13 3 1 12 7 17 17 14 9 6 19 14 10 2 11 8 7
#> 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100
-#> 3 19 1 13 19 16 9 2 18 2 4 18 4 1 11 10 8 2 11 1
+#> 8 20 15 4 4 1 17 12 4 12 10 5 11 12 20 3 9 12 20 12
#>
#> Within cluster sum of squares by cluster:
-#> [1] 13.8467580 2.0713631 15.4102772 5.9461649 8.0412334 0.9921913
-#> [7] 3.1842269 3.4617088 9.9418120 17.6305488 8.0515257 4.9390800
-#> [13] 4.0041862 7.3057948 4.7723673 3.0260293 7.3290085 5.6033175
-#> [19] 0.9549143 2.4583646
-#> (between_SS / total_SS = 83.4 %)
+#> [1] 3.0260293 5.0357352 13.2343206 13.1118010 10.3695240 8.0412334
+#> [7] 11.7251902 8.3255313 3.4617088 2.4693324 1.6353685 13.0645655
+#> [13] 2.9416715 3.1842269 5.0413438 2.4669004 9.9418120 0.9921913
+#> [19] 0.9833964 12.4042363
+#> (between_SS / total_SS = 83.1 %)
#>
#> Available components:
#>
diff --git a/docs/reference/gNetwork-1.png b/docs/reference/gNetwork-1.png
index efb4385..14d64f3 100644
Binary files a/docs/reference/gNetwork-1.png and b/docs/reference/gNetwork-1.png differ
diff --git a/docs/reference/gNetwork.html b/docs/reference/gNetwork.html
index dc8f058..f0279e4 100644
--- a/docs/reference/gNetwork.html
+++ b/docs/reference/gNetwork.html
@@ -83,12 +83,12 @@ Examplesnetworkres <- gNetwork(test_cluster, ntop = 3)
head(networkres)
#> weight IncNodePurity var_names from
-#> 1 1.6429045 0.7371260 Group_3 Group_1
-#> 2 0.4588961 0.9310697 Group_5 Group_1
-#> 3 -0.7833169 0.6761286 Group_2 Group_1
-#> 4 5.0076415 0.8821582 Group_5 Group_2
-#> 5 4.1618215 1.1976058 Group_4 Group_2
-#> 6 2.5714583 1.2820391 Group_1 Group_2
+#> 1 1.4215243 1.1776012 Group_5 Group_1
+#> 2 0.7911266 0.7011049 Group_3 Group_1
+#> 3 -1.9891284 0.5811186 Group_2 Group_1
+#> 4 5.8098014 1.1107592 Group_4 Group_2
+#> 5 5.4000389 0.8744358 Group_5 Group_2
+#> 6 1.2492288 1.1816976 Group_1 Group_2
gNetwork_view(networkres)
diff --git a/docs/reference/make_the_graph-1.png b/docs/reference/make_the_graph-1.png
index 061c537..76d9b9c 100644
Binary files a/docs/reference/make_the_graph-1.png and b/docs/reference/make_the_graph-1.png differ
diff --git a/docs/search.json b/docs/search.json
index 782a610..33c4d18 100644
--- a/docs/search.json
+++ b/docs/search.json
@@ -1 +1 @@
-[{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"CC0 1.0 Universal","title":"CC0 1.0 Universal","text":"CREATIVE COMMONS CORPORATION LAW FIRM PROVIDE LEGAL SERVICES. DISTRIBUTION DOCUMENT CREATE ATTORNEY-CLIENT RELATIONSHIP. CREATIVE COMMONS PROVIDES INFORMATION “-” BASIS. CREATIVE COMMONS MAKES WARRANTIES REGARDING USE DOCUMENT INFORMATION WORKS PROVIDED HEREUNDER, DISCLAIMS LIABILITY DAMAGES RESULTING USE DOCUMENT INFORMATION WORKS PROVIDED HEREUNDER.","code":""},{"path":"https://kaiyanm.github.io/MolPad/LICENSE.html","id":"statement-of-purpose","dir":"","previous_headings":"","what":"Statement of Purpose","title":"CC0 1.0 Universal","text":"laws jurisdictions throughout world automatically confer exclusive Copyright Related Rights (defined ) upon creator subsequent owner(s) (, “owner”) original work authorship /database (, “Work”). 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Affirmer disclaims responsibility clearing rights persons may apply Work use thereof, including without limitation person’s Copyright Related Rights Work. , Affirmer disclaims responsibility obtaining necessary consents, permissions rights required use Work. Affirmer understands acknowledges Creative Commons party document duty obligation respect CC0 use Work.","code":""},{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/articles/FAQ.html","id":"how-can-i-tell-if-my-dataset-is-suitable-for-using-this-dashboard","dir":"Articles","previous_headings":"Common Questions:","what":"How can I tell if my dataset is suitable for using this dashboard?","title":"FAQs","text":"lot longitudinal data least two descriptive columns (like types), match minimal needs starting dashboard. addition, data specific web-ID columns, MolPad make convenient createing links databases automatically.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/FAQ.html","id":"how-can-i-customize-my-dashboard","dir":"Articles","previous_headings":"Common Questions:","what":"How can I customize my dashboard?","title":"FAQs","text":"can edit title within gDashboard() function. additional personalization, adjusting colors sizes figures, can fork repository, create copy, make modifications dashboard.css file.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/FAQ.html","id":"what-should-i-do-if-i-only-have-a-few-time-points5","dir":"Articles","previous_headings":"Common Questions:","what":"What should I do if I only have a few time points(<5)?","title":"FAQs","text":"Firstly, strongly recommend extending length longitudinal samples. can achieve increasing number measurements two days incorporating simulated data. options don’t suit needs, can also consider using “linear” method gNetwork() function.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Case Study: Cheese Communities","text":"vignette provides comprehensive guide using MolPad case study, including data pre-processing, network generation, result analysis.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"washed-rind-cheese-microbial-communities","dir":"Articles","previous_headings":"","what":"Washed-Rind Cheese Microbial Communities","title":"Case Study: Cheese Communities","text":"Cheese making ancient craft involves coagulation milk proteins form curds, separated liquid whey. curds processed, shaped, aged develop desired texture flavor. Various factors, type milk, specific cultures bacteria, aging conditions, contribute unique characteristics cheese. One crucial step process aging practice, regular washing brine solution plays significant role. process producing cheese, regular washing brine solution aging practice can homogenize microbial communities cheese’ surface facilitate intermicrobial interactions. following parts, analyze longitudinal data set three washed-rind cheese communities collected cheese ripening.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"data","dir":"Articles","previous_headings":"","what":"Data","title":"Case Study: Cheese Communities","text":"analysis, use data set contained study Smith et al. (2022) microbial communities cheese. original research investigated successional dynamics occur within cheese rind microbial communities using combination 16S rRNA amplicon, Illumina, PacBio sequencing. functionally taxonomically annotate (using eggNOG (21) MMseqs2 (22)) contigs generated Illumina reads, demonstrate utility MolPad using single-omic. Specifically, focus Cheese Sample Cheese Sample C. detailed information attached data, please check documentation.","code":"data(\"cheese\") str(cheese) #> tibble [106,239 × 18] (S3: tbl_df/tbl/data.frame) #> $ ID : chr [1:106239] \"1\" \"2\" \"3\" \"4\" ... #> $ A_1 : int [1:106239] 38 23 24 3 58 12 1 14 1 3 ... #> $ A_2 : int [1:106239] 23 6 5 2 14 9 1 7 1 1 ... #> $ A_3 : int [1:106239] 27 4 37 4 45 14 0 14 3 5 ... #> $ A_4 : int [1:106239] 5 0 10 2 13 4 1 4 4 0 ... #> $ A_5 : int [1:106239] 11 9 19 16 32 13 0 4 1 1 ... #> $ C_1 : int [1:106239] 13 21 3 56 82 2 4 17 7 2 ... #> $ C_3 : int [1:106239] 1 1 0 1 3 0 0 0 0 1 ... #> $ C_4 : int [1:106239] 3 0 1 7 8 1 0 1 2 2 ... #> $ C_5 : int [1:106239] 0 2 4 17 3 4 0 1 8 6 ... #> $ GO_ID : chr [1:106239] NA NA NA NA ... #> $ KEGG_ID: chr [1:106239] NA NA NA NA ... #> $ domain : chr [1:106239] NA NA NA NA ... #> $ phylum : chr [1:106239] NA NA NA NA ... #> $ class : chr [1:106239] NA NA NA NA ... #> $ order : chr [1:106239] NA NA NA NA ... #> $ family : chr [1:106239] NA NA NA NA ... #> $ genus : chr [1:106239] NA NA NA NA ... str(annotations) #> tibble [86,156 × 9] (S3: tbl_df/tbl/data.frame) #> $ ID : chr [1:86156] \"9\" \"10\" \"11\" \"12\" ... #> $ GO_ID : chr [1:86156] \"-\" \"-\" \"-\" \"-\" ... #> $ KEGG_ID: chr [1:86156] \"-\" \"-\" \"-\" \"-\" ... #> $ domain : chr [1:86156] \"Bacteria\" \"Bacteria\" \"Bacteria\" \"Bacteria\" ... #> $ phylum : chr [1:86156] \"Pseudomonadota\" \"Pseudomonadota\" \"Pseudomonadota\" \"Pseudomonadota\" ... #> $ class : chr [1:86156] \"Alphaproteobacteria\" \"Alphaproteobacteria\" \"Alphaproteobacteria\" \"Alphaproteobacteria\" ... #> $ order : chr [1:86156] \"Caulobacterales\" \"Caulobacterales\" \"Hyphomicrobiales\" \"Hyphomicrobiales\" ... #> $ family : chr [1:86156] \"Caulobacteraceae\" \"Caulobacteraceae\" \"Bartonellaceae\" \"Brucellaceae;-_Brucella/Ochrobactrum group\" ... #> $ genus : chr [1:86156] \"Caulobacter\" \"Caulobacter;-_unclassified Caulobacter\" \"Bartonella\" \"Brucella\" ..."},{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"data-and-annotations","dir":"Articles","previous_headings":"Data > Pre-process","what":"Data and Annotations","title":"Case Study: Cheese Communities","text":"select ‘type’ column phylum describe characteristic cheese data. Also, columns phylum class taken tags elemental composition. section, introduce data preparing steps analysis. annotations dataset contains various columns describe characteristics properties samples. First, select ‘type’ column phylum provide broad categorization microbial communities present cheese surface. categorization helps understanding overall composition diversity microbes high taxonomic level. run pre_process() function clean standardize data. annotate dataset, also use columns phylum class tags elemental composition microbial communities. phylum column represents major taxonomic rank, giving us broad overview microbial distribution. class column provides detailed information, allowing us delve deeper specific types microbes present. pre-processing, two datasets put dashboard look like:","code":"cheesedata <- cheese |> select(ID, A_1:C_5, phylum) |> rename(type=phylum) |> pre_process() pathchee <- gAnnotation(annotations,\"phylum\",\"class\") # data cheesedata[112:115,] #> # A tibble: 4 × 11 #> ID A_1 A_2 A_3 A_4 A_5 C_1 C_3 C_4 C_5 type #> #> 1 112 0.943 -0.471 -0.471 -0.471 -0.471 2.36 -0.471 -0.471 -0.471 Other #> 2 113 0.786 -0.124 0.126 -0.623 -0.637 2.33 -0.667 -0.593 -0.593 Other #> 3 114 1.22 -1.09 -0.430 -0.829 -0.170 1.43 -1.30 0.455 0.715 Other #> 4 115 2.67 -0.333 -0.333 -0.333 -0.333 -0.333 -0.333 -0.333 -0.333 Other # annotation pathchee[112:115,] #> # A tibble: 4 × 9 #> ID GO_ID KEGG_ID domain Pathway taxonomic.scope order family genus #> #> 1 145 - ko:K00004 Bacte… Pseudo… Unknown NA NA NA #> 2 147 - ko:K00004 Bacte… Actino… Actinomycetes NA NA NA #> 3 148 - ko:K00004 Bacte… Bacill… Bacilli Baci… Bacil… Lysi… #> 4 149 - ko:K00004,ko:K0… Bacte… Actino… Actinomycetes NA NA NA"},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"cluster-input","dir":"Articles","previous_headings":"Data","what":"Cluster Input","title":"Case Study: Cheese Communities","text":"section, generate clusters first dataset using gClusters function. function takes cheese dataset (cheesedata) input generates clusters based specified parameters. , set number clusters 10 (ncluster = 10) specify maximum number clusters consider determining optimal number clusters (elbow.max=15).","code":"cluschee <- gClusters(cheesedata,ncluster = 10,elbow.max=15)"},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"network-input","dir":"Articles","previous_headings":"Data","what":"network input","title":"Case Study: Cheese Communities","text":"generating clusters major patterns, proceed obtain network results clusters. Taking cluster centroids nodes, prediction process edges divided individual regression tasks, cluster centroid independentally predicted expression cluster centroids, using random forests. pick top 3 related predictors cluster centroid save network output future use. achieved using gNetwork() function. gain insight network results, can visualize details using gNetwork_view() function, shown .","code":"networkchee <- gNetwork(cluschee,ntop = 3) gNetwork_view(networkchee)"},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"run-dashboard","dir":"Articles","previous_headings":"","what":"Run Dashboard","title":"Case Study: Cheese Communities","text":"clusters network results obtained, can proceed run dashboard. involves declaring annotations executing dashboard using gDashboard() function. , pass cheese dataset (cheesedata), cluster results (cluschee), network results (networkchee), specify column names types annotation identifiers.","code":"gDashboard(cheesedata, cluschee, pathchee, networkchee, id_colname = c(\"GO_ID\",\"KEGG_ID\"), id_type = c(\"GO\",\"KEGG\"))"},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"installation","dir":"Articles","previous_headings":"","what":"Installation","title":"Get started","text":"can either install MolPad devtools, clone repository local computer (example, onto ./Github) installing: , load package:","code":"# Install the package in R: install.packages(\"devtools\") library(devtools) install_github(\"KaiyanM/MolPad\") # Download through Github and install the package in R: setwd(\"./GitHub\") install(\"MolPad\") library(MolPad)"},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"before-input","dir":"Articles","previous_headings":"","what":"Before Input","title":"Get started","text":"recommend carefully reviewing data considering applying normalization (standardization, quantile normalization, log transformation, …) imputation (KNN, PMM, Mean/Median/Mode, …) address issues related library size missing data beforehead, otherwise pre_processing() function default converting NAs 0s.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"tips-for-normalization-and-imputation","dir":"Articles","previous_headings":"Before Input","what":"Tips for normalization and imputation","title":"Get started","text":"choose normalization method, first consider data distribution. Use Z-score normalization data normally distributed, log transformation robust scaling ’s skewed. analysis requires data within specific range, like [0, 1], opt Min-Max normalization. datasets outliers, robust scaling rank normalization better choices. selecting imputation method, assess amount pattern missing data. small amount missing data, simple methods like mean, median, mode imputation effective. missing data related variables, consider sophisticated approaches like Predictive Mean Matching k-NN imputation. computational resources concern, simpler imputation methods might practical, resources allow, advanced methods can improve data quality. ’s hard decide, can always experiment different methods use cross-validation compare effectiveness. Let choice guided domain knowledge specific requirements analysis increase quality following steps.","code":""},{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"multi-omics-yes-or-no","dir":"Articles","previous_headings":"Data Format","what":"Multi-omics: Yes or No","title":"Get started","text":"visualization pipeline starts pre-processing module includes built-functions reshape data inputs standard format streamline data preparation. Depending nature datasets, two primary conditions consider: research object includes multi-omics measurements, assume provide list dataframes, collected different omics type. example, might datasets peptides, metabolites, lipids. combine together standard long dataset row represents feature, simply run pre_process() function, conduct data preparation yield standard input format demonstrated . Datasets multi-omics, can still utilize dashboard long ensuring data inputs reformatted standard longitudinal format. situation, can manually assign type column category label describe major groups data, input data pre_processing() module data preparation . case study, utilized “Kingdom” type label column cheese data. datasets multi-omics, can simply apply dashboard reformatting data standard longitudinal format. case, manually assign category label type column represent major groups. can still input data pre_processing() module cleaning preparation. case study, used “Kingdom” type label cheese datasets.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"data-format-and-pre-process","dir":"Articles","previous_headings":"Data Format","what":"Data Format and Pre-process","title":"Get started","text":"figure, , B, C can represent datasets different sources different aspects measurements. two examples input data might look like transform required format pre_process() modules.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"if-your-input-is-a-list-of-datasets","dir":"Articles","previous_headings":"Data Format > Data Format and Pre-process","what":"If your input is a list of datasets:","title":"Get started","text":"list datasets, following format: ID column identical column names. pre_process():","code":"str(fungal_dlist) #> List of 3 #> $ :'data.frame': 149786 obs. of 12 variables: #> ..$ ID : chr [1:149786] \"grp1_1\" \"grp1_2\" \"grp1_6\" \"grp1_7\" ... #> ..$ D0_01 : num [1:149786] -0.33 -0.33 1.591 1.106 0.659 ... #> ..$ D1_02 : num [1:149786] 0.628 0.628 3.087 3.618 2.843 ... #> ..$ D2_03 : num [1:149786] -1.223 -1.223 0.846 1.178 1.435 ... #> ..$ D3_04 : num [1:149786] -2.411 -1.808 1.239 1.753 0.258 ... #> ..$ D4_05 : num [1:149786] -1.41 -1.18 2.05 1.64 1.21 ... #> ..$ D5_06 : num [1:149786] -2.245 -2.309 0.83 1.64 0.395 ... #> ..$ D6_07 : num [1:149786] -0.784 -1.125 2.758 2.888 2.516 ... #> ..$ D7_08 : num [1:149786] -2.61197 -2.68724 0.42236 0.92846 0.00892 ... #> ..$ D8_09 : num [1:149786] -0.951 -1.3 2.417 2.454 1.418 ... #> ..$ D9_10 : num [1:149786] -1.91 -2.61 1.33 1.8 2.25 ... #> ..$ D10_11: num [1:149786] -2.871 -3.417 1.101 0.619 1.275 ... #> $ :'data.frame': 150 obs. of 12 variables: #> ..$ ID : chr [1:150] \"grp2_1\" \"grp2_2\" \"grp2_3\" \"grp2_4\" ... #> ..$ D0_01 : num [1:150] 0.947 1.012 0.156 0.31 0.138 ... #> ..$ D1_02 : num [1:150] -1.616 -0.917 -1.581 -2.717 -2.578 ... #> ..$ D2_03 : num [1:150] -2.4 -1.67 -2.31 -3.02 -3.66 ... #> ..$ D3_04 : num [1:150] -0.5734 0.0336 -0.351 -1.3052 -1.737 ... #> ..$ D4_05 : num [1:150] 0.662 0.854 1.002 0.12 -0.371 ... #> ..$ D5_06 : num [1:150] -1.09 -1.28 -1.04 -1.82 -3.15 ... #> ..$ D6_07 : num [1:150] -0.212 0.718 0.189 -0.627 -1.081 ... #> ..$ D7_08 : num [1:150] 0.969 2.117 1.462 0.339 -0.038 ... #> ..$ D8_09 : num [1:150] -0.712 0.43 -0.281 -1.297 -1.822 ... #> ..$ D9_10 : num [1:150] -0.649 0.45 -0.124 -1.77 -1.962 ... #> ..$ D10_11: num [1:150] -1.05 -0.85 -1.58 -1.91 -4.05 ... #> $ :'data.frame': 369 obs. of 12 variables: #> ..$ ID : chr [1:369] \"grp3_1\" \"grp3_2\" \"grp3_3\" \"grp3_4\" ... #> ..$ D0_01 : num [1:369] -0.918 -2.112 -1.372 -0.4 -1.75 ... #> ..$ D1_02 : num [1:369] -1.219 -3.502 -1.504 -0.615 -2.11 ... #> ..$ D2_03 : num [1:369] -0.542 -2.324 -0.592 0.161 -1.342 ... #> ..$ D3_04 : num [1:369] -0.101 -1.495 0.1 0.603 -1.058 ... #> ..$ D4_05 : num [1:369] 1.715 1.557 1.89 2.296 0.653 ... #> ..$ D5_06 : num [1:369] -1.452 -2.37 -1.182 -0.846 -2.531 ... #> ..$ D6_07 : num [1:369] 0.0879 -0.9982 0.4382 0.6215 -0.9464 ... #> ..$ D7_08 : num [1:369] 0.0502 0.4668 0.5378 0.8353 -0.9213 ... #> ..$ D8_09 : num [1:369] -0.0268 -0.4226 0.4528 0.6328 -1.0998 ... #> ..$ D9_10 : num [1:369] -0.5807 -1.7755 -0.1956 0.0866 -1.5876 ... #> ..$ D10_11: num [1:369] 0.118 -0.16 0.687 0.572 -1.037 ... #> ID D0_01 D1_02 D2_03 D3_04 D4_05 D5_06 #> 1 grp1_1 -0.3297741 0.6284682 -1.2232968 -2.411339 -1.413062 -2.2445487 #> 2 grp1_2 -0.3297741 0.6284682 -1.2232968 -1.807533 -1.183498 -2.3088022 #> 3 grp1_6 1.5908054 3.0871284 0.8460835 1.239411 2.054109 0.8300876 #> 4 grp1_7 1.1061795 3.6176250 1.1781012 1.753078 1.643661 1.6400102 #> D6_07 D7_08 D8_09 D9_10 D10_11 #> 1 -0.7839412 -2.6119663 -0.9511467 -1.914273 -2.8711810 #> 2 -1.1251434 -2.6872417 -1.2996614 -2.610647 -3.4165861 #> 3 2.7576288 0.4223618 2.4165602 1.333387 1.1012825 #> 4 2.8877343 0.9284640 2.4538561 1.799019 0.6187648 #> ID D0_01 D1_02 D2_03 D3_04 D4_05 D5_06 #> 1 grp2_1 0.9467431 -1.6162900 -2.400394 -0.57337845 0.6616711 -1.090914 #> 2 grp2_2 1.0120048 -0.9165994 -1.674330 0.03361378 0.8540950 -1.283964 #> 3 grp2_3 0.1561394 -1.5805490 -2.307281 -0.35095093 1.0018741 -1.035030 #> 4 grp2_4 0.3104578 -2.7173471 -3.022886 -1.30521496 0.1198075 -1.816677 #> D6_07 D7_08 D8_09 D9_10 D10_11 #> 1 -0.2116778 0.9688582 -0.7123773 -0.6489204 -1.0504845 #> 2 0.7181970 2.1174460 0.4302981 0.4504066 -0.8503481 #> 3 0.1885422 1.4619677 -0.2805926 -0.1235090 -1.5757556 #> 4 -0.6271954 0.3393109 -1.2967938 -1.7697248 -1.9073101 #> ID D0_01 D1_02 D2_03 D3_04 D4_05 D5_06 #> 1 grp3_1 -0.9178218 -1.2193128 -0.5415780 -0.1007493 1.715495 -1.4515238 #> 2 grp3_2 -2.1120636 -3.5015326 -2.3236475 -1.4949366 1.557463 -2.3698578 #> 3 grp3_3 -1.3724446 -1.5043101 -0.5922380 0.1004978 1.889573 -1.1815469 #> 4 grp3_4 -0.4002445 -0.6145252 0.1613003 0.6031744 2.295751 -0.8464012 #> D6_07 D7_08 D8_09 D9_10 D10_11 #> 1 0.08792489 0.05020749 -0.02681783 -0.58065562 0.1177374 #> 2 -0.99822332 0.46681389 -0.42255125 -1.77554842 -0.1603138 #> 3 0.43822354 0.53780990 0.45276561 -0.19558682 0.6870272 #> 4 0.62145080 0.83528878 0.63275758 0.08663959 0.5720591 data <- pre_process(fungal_dlist, typenameList = c('DataSource_A','DataSource_B','DataSource_C'), replaceNA = TRUE, scale = TRUE, autoColName = \"Section_\") #> [1] \"Reformat a list of datasets:\" head(data,10) #> ID D0_01 D1_02 D2_03 D3_04 D4_05 D5_06 #> 1 grp1_1 1.06146675 1.956656 0.2267383 -0.88312967 0.04945980 -0.7273146 #> 2 grp1_2 1.08465667 1.916983 0.3085454 -0.19892034 0.34311497 -0.6343214 #> 3 grp1_6 -0.01899274 1.717871 -0.8834318 -0.42687491 0.51878884 -0.9019992 #> 4 grp1_7 -0.75869657 2.051470 -0.6782202 -0.03485388 -0.15728499 -0.1613700 #> 5 grp1_8 -0.68132827 1.648062 0.1465161 -1.10875822 -0.09101615 -0.9629427 #> 6 grp1_11 0.27709905 1.981042 -0.5786717 0.02888008 0.54647685 -0.7013406 #> 7 grp1_12 0.90487968 2.180079 -0.6439891 -0.14841544 -0.69040101 -0.3834938 #> 8 grp1_13 0.46335031 1.695812 -1.0938459 -0.76868293 0.74415950 -0.6861036 #> 9 grp1_14 0.72014702 1.708604 -0.9344900 -0.35796963 0.63730277 -0.9872778 #> 10 grp1_15 0.54526798 1.784716 -0.8846753 -0.49538649 0.62916563 -0.9742293 #> D6_07 D7_08 D8_09 D9_10 D10_11 type #> 1 0.6371841 -1.0705560 0.4809808 -0.41877137 -1.31271417 DataSource_A #> 2 0.3938012 -0.9630327 0.2422154 -0.89650312 -1.59653914 DataSource_A #> 3 1.3354026 -1.3752687 0.9395058 -0.31779210 -0.58720856 DataSource_A #> 4 1.2347631 -0.9575502 0.7492778 0.01655139 -1.30408627 DataSource_A #> 5 1.2995756 -1.3741657 0.1285139 1.01932257 -0.02377948 DataSource_A #> 6 1.1406352 -1.6339536 0.2041455 -0.45793129 -0.80638109 DataSource_A #> 7 0.4794959 -1.6340692 0.5360766 -0.31430867 -0.28585374 DataSource_A #> 8 1.2235141 -1.2241931 0.6790383 -0.28487806 -0.74817082 DataSource_A #> 9 1.1466328 -1.4265286 0.5357111 -0.51799361 -0.52413804 DataSource_A #> 10 1.1813273 -1.2944121 0.6417621 -0.46906150 -0.66447461 DataSource_A"},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"if-your-input-is-a-single-dataset","dir":"Articles","previous_headings":"Data Format > Data Format and Pre-process","what":"If your input is a single dataset:","title":"Get started","text":"dataset already includes labeled ‘type’ column: pre_process():","code":"#> ID Day_1 Day_2 Day_3 Day_4 Day_5 Day_6 #> 1 1 NA NA 0.104083550 -1.07999900 1.3416214 -1.733564471 #> 2 2 NA NA 0.882578509 0.08440181 0.7165712 0.004811372 #> 3 3 0.40203917 NA 0.675076590 0.97017740 -0.8210872 0.659073443 #> 4 4 1.02859068 0.30178418 0.412617771 0.39306149 -0.3427640 0.837009714 #> 5 5 -2.14036218 -0.06315709 0.656982200 -0.18301850 -1.3967562 -0.123327229 #> 6 6 -2.04354192 1.30960484 1.371349945 -1.67403067 -1.2946150 0.760315504 #> 7 7 0.31164367 2.26615374 0.972843085 -0.03956293 1.0190965 1.180680430 #> 8 8 -0.79692858 -0.86038791 -3.417872877 0.62118867 -2.1983197 -0.104229763 #> 9 9 -0.12033042 1.17585682 -0.001075426 0.01122359 0.3430992 -0.189234532 #> 10 10 0.02592915 0.48551867 0.841254390 -0.46430744 0.6891800 0.751766756 #> Day_7 Day_8 type #> 1 0.4638261 2.1318716 peptide #> 2 -0.2949926 -0.9310538 peptide #> 3 -0.2520195 0.7809977 peptide #> 4 -1.0120371 -0.1162361 peptide #> 5 -2.0085970 -0.8959521 peptide #> 6 0.7874222 -0.5106016 peptide #> 7 -0.7719437 -1.4686258 peptide #> 8 0.3541200 0.8224521 lipid #> 9 0.5674059 -0.9313882 lipid #> 10 -0.4097464 NA metabolite x1 <- pre_process(x, replaceNA = TRUE,scale = TRUE) head(x1,10) #> ID Day_1 Day_2 Day_3 Day_4 Day_5 Day_6 #> 1 1 -0.12499488 -0.1249949 -0.04022866 -1.00455207 0.9676290 -1.53681943 #> 2 2 -0.10246242 -0.1024624 1.46237249 0.04718422 1.1680372 -0.09393173 #> 3 3 0.16412393 -0.4940273 0.61109508 1.09418472 -1.8381738 0.58489741 #> 4 4 1.27432720 0.1728189 0.34079228 0.31115385 -0.8040231 0.98397755 #> 5 5 -1.35954882 0.7001734 1.41425156 0.58132077 -0.6222015 0.64050965 #> 6 6 -1.35505505 1.0595199 1.10398207 -1.08897287 -0.8157586 0.66398092 #> 7 7 -0.10291149 1.5438734 0.45418630 -0.39882285 0.4931574 0.62930098 #> 8 8 -0.06748422 -0.1105542 -1.84632390 0.89499455 -1.0186109 0.40265173 #> 9 9 -0.36947382 1.7376966 -0.17560476 -0.15561064 0.3839091 -0.48148903 #> 10 10 -0.41003145 0.4704748 1.15201231 -1.34925296 0.8606601 0.98056711 #> Day_7 Day_8 type #> 1 0.2527477 1.6112132 peptide #> 2 -0.6254920 -1.7532453 peptide #> 3 -0.9065915 0.7844915 peptide #> 4 -1.8183370 -0.4607098 peptide #> 5 -1.2288927 -0.1256124 peptide #> 6 0.6835002 -0.2511966 peptide #> 7 -1.0158949 -1.6028889 peptide #> 8 0.7137346 1.0315924 lipid #> 9 0.7485573 -1.6879846 lipid #> 10 -1.2447220 -0.4597079 metabolite"},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"choose-your-annotation","dir":"Articles","previous_headings":"","what":"Choose Your Annotation","title":"Get started","text":"addition specifying data type mentioned , methods support three levels information: functional annotation, taxonomy annotation, feature annotation. annotations matched ID columns annotation data, serving another crucial input generating dashboard. facilitate automatic feature link generation using KeggID GOID, users set corresponding column names beforehand. example annotation formats align data inputs. data two layers labels (like cheese dataset), can specify two layers want focus network using gAnnotation() function.","code":"#> ID taxonomic.scope ko_term Pathway #> 1 grp1_24 Bacteria K02706 00195_Photosynthesis #> 2 grp1_25 Bacteria K02706 00195_Photosynthesis #> 3 grp1_26 Bacteria K02706 00195_Photosynthesis #> 4 grp1_1075 Bacteria K02112 00195_Photosynthesis #> 5 grp1_1076 Bacteria K02112 00195_Photosynthesis #> 6 grp1_1358 Bacteria K02112 00195_Photosynthesis Annotation_path_taxon <- gAnnotation(annotations,\"phylum\",\"class\")"},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"network-generation","dir":"Articles","previous_headings":"","what":"Network Generation","title":"Get started","text":"generate network, first scale cluster trajectories across input molecular features. Trajectories clustered using K-means, built-elbow method used select optimal K. build network clustered trajectories adapting GENIE3 algorithm. Specifically, network learned series regressions, cluster centroid predicted expression patterns cluster centroids using random forests. choose random forests potential model interacting features non-linearity without strong assumptions. determine feature clusters predictive given trajectory, Mean Decrease Accuracy subset top predictors whose expression directly influences expression target cluster taken indication putative link.","code":"cluster_result <- data |> gClusters(ncluster = 5,elbow.max=15) network_result <- cluster_result |> gNetwork(ntop = 3) gNetwork_view(network_result)"},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"dashboard-navigation","dir":"Articles","previous_headings":"","what":"Dashboard Navigation","title":"Get started","text":"workflow inputs related gDashboard function. key function processes data runs dashboard, requiring input previous steps. ’ve launched Shiny dashboard, can zoom make adjustments explore interesting findings within data. effectively navigate dashboard generated MolPad, ’ll follow three main steps:","code":"gDashboard(data, cluster_result, Annotation_path_taxon, network_result, id_colname = \"ko_term\" , id_type = \"KEGG\")"},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"choose-a-primary-functional-annotation-and-adjust-edge-density","dir":"Articles","previous_headings":"Dashboard Navigation","what":"Choose a Primary Functional Annotation and Adjust Edge Density","title":"Get started","text":"Start selecting primary functional annotation available options. , fine-tune edge density adjusting threshold value importance score. Nodes turn bright green indicate clusters containing features related chosen functional annotation.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"explore-the-network","dir":"Articles","previous_headings":"Dashboard Navigation","what":"Explore the Network","title":"Get started","text":"Brushing network unveils patterns taxonomic composition typical trajectories. can also zoom specific taxonomic annotations applying filters.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"investigate-feature-details-and-related-function-annotations","dir":"Articles","previous_headings":"Dashboard Navigation","what":"Investigate Feature Details and Related Function Annotations","title":"Get started","text":"Delve feature table examine specifics features within selected clusters. Explore additional related function annotations using drop-options. Click provided links access online information items interest. interface encourages iterative exploration, enabling conduct multiple steps answer specific questions, comparing pattern distributions two functions identifying functionally important community members metabolizing feature interest.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"related-visualization-outputs","dir":"Articles","previous_headings":"","what":"Related Visualization Outputs","title":"Get started","text":"following visualizations integrated dashboard, ’s need run function plot individually. ’ve linked stacked bar plot ribbon plot network facilitate focus context explorations. setup helps detecting similar longitudinal patterns understanding relationships.","code":""},{"path":[]},{"path":[]},{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/articles/whymolpad.html","id":"why-molpad","dir":"Articles","previous_headings":"","what":"Why MolPad?","title":"Why MolPad?","text":"aim network interpretation, MolPad shows improvements 3 important aspects: Capture groups features share longitudinal patterns functional properties within network. Annotate network complex experimental datasets, way dig specific components notable features. Streamlines exploration modularized functions interactive dashboard. package especially facilitates exploration longitudinal molecular co-expression microbiome experiments using focus-plus-context approach, empowering researchers uncover global patterns insights longitudinal microbiomics data. Unlike existing tools, MolPad stands rich network interactivity support contextualization. also equipped beginner-friendly interface self-contained data processing functions, making accessible users limited programming experience.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/whymolpad.html","id":"key-features","dir":"Articles","previous_headings":"","what":"Key Features","title":"Why MolPad?","text":"Trajectory clustering organizing data clusters, helps identify patterns, relationships, underlying structures within dataset. essence, clustering detects groups features observations behave similarly, allowing deeper insights effective decision-making. Network extracting build network clustered trajectories adapting GENIE3 algorithm. Specifically, network learned series regressions, cluster centroid predicted expression patterns cluster centroids using random forests. choose random forests potential model interacting features non-linearity without strong assumptions. Interactive Dashboard package provides interactive dashboard designed help users explore data uncover relationships within complex networks. utilizing focus-plus-context visualization, dashboard allows users zoom specific details keeping broader dataset view. approach enables deeper understanding data, making easier identify patterns make informed decisions.","code":""},{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/articles/whymolpad.html","id":"molpad-dashboard-overview","dir":"Articles","previous_headings":"What does it look like?","what":"MolPad Dashboard Overview","title":"Why MolPad?","text":"overview MolPad Dashboard. explore dashboard effectively, can start following sequence -B-C-D. approach guide cluster-level network, taxonomic-level bar plot, type-level line plot, feature-level table. , can modify detailed settings identify investigate special patterns within data.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/whymolpad.html","id":"discovering-related-patterns","dir":"Articles","previous_headings":"What does it look like?","what":"Discovering related patterns","title":"Why MolPad?","text":"short example discovering related patterns using network plot: shade edges represents proximity nodes. brushed area, Groups 1-7-8 (circled solid black lines) Groups 1-2 (circled blue dashed lines) strongly linked. Groups 1, 7, 8, patterns form W-shape noticeable peak time section. Although Group 1 shows higher volatility compared Group 2, groups display highly overlapping increasing trend. detailed instructions, please visit “Get Started” page relevant function reference page.","code":""},{"path":"https://kaiyanm.github.io/MolPad/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Kaiyan Ma. Author, maintainer.","code":""},{"path":"https://kaiyanm.github.io/MolPad/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Ma K (2024). MolPad: MolPad: R-Shiny Package Cluster Co-Expression Analysis Longitudinal Microbiomics. R package version 0.1.0, https://kaiyanm.github.io/MolPad/.","code":"@Manual{, title = {MolPad: MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics}, author = {Kaiyan Ma}, year = {2024}, note = {R package version 0.1.0}, url = {https://kaiyanm.github.io/MolPad/}, }"},{"path":"https://kaiyanm.github.io/MolPad/index.html","id":"molpad-","dir":"","previous_headings":"","what":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","title":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","text":"R-Shiny Package Cluster Co-Expression Analysis Longitudinal Microbiomics","code":""},{"path":"https://kaiyanm.github.io/MolPad/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","text":"MolPad offers visualization dashboard tool designed enhance understanding molecular co-expression works context microbiome data. approach involves using cluster network provide initial overview relationships across multiple omics, added functionality interactively zoom specific areas interest. facilitate analysis, ’ve developed focus-plus-context strategy connects online curated annotations. Additionally, package simplifies entire pipeline creating dashboard. user-friendly design makes accessible even people limited R programming experience. Check cheese-data demo .","code":""},{"path":"https://kaiyanm.github.io/MolPad/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","text":"can either install devtools, {r, eval = FALSE} # Install package R: install.packages(\"devtools\") library(devtools) install_github(\"KaiyanM/MolPad\") clone repository local computer (example, onto ./Github) installing: {r, eval = FALSE} # Download install package R: setwd(\"./GitHub\") install(\"MolPad\") , load package: {r,eval=FALSE} library(MolPad)","code":""},{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/index.html","id":"molpad-could-help-you-with","dir":"","previous_headings":"Usage","what":"MolPad could help you with:","title":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","text":"Clustering data k-means building group network. Find significant trend patterns datasets. Target interaction groups, taxons, pathways. Visualize distribution features specific pathways group network. Search particular features user-defined labels. Check detailed information feature automatically generated hyperlinks. better overall understanding datasets.","code":""},{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/index.html","id":"getting-help","dir":"","previous_headings":"","what":"Getting Help","title":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","text":"need assistance MolPad, two primary ways seek help: Ask us anything related MolPad! add question, create issue repository. Stack Overflow another excellent resource answering common issues R. Remember ’s particularly effective can provide reproducible example shows specific problem ’re .","code":""},{"path":"https://kaiyanm.github.io/MolPad/index.html","id":"contribution","dir":"","previous_headings":"","what":"Contribution","title":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","text":"contribute project, use following workflow: fork repository –> create local copy –> submit pull request.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":null,"dir":"Reference","previous_headings":"","what":"Cheese data — cheese","title":"Cheese data — cheese","text":"context cheese production, regular application brine solution maturation technique promotes uniformity microbial populations cheese's surface facilitates interactions among microorganisms. investigation involved analysis longitudinal dataset encompassing three washed-rind cheese communities sampled ripening process.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Cheese data — cheese","text":"two datasets: cheese annotations","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Cheese data — cheese","text":"Reference: doi:10.1128/msystems.00701-22","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Cheese data — cheese","text":"study uncovered remarkably consistent microbial progression within cheese. bacterial community, Firmicutes dominate outset, Proteobacteria swiftly assuming dominance end ripening period. Additionally, Cheese Cheese C consistently demonstrate establishment Actinobacteria Bacteroidetes, distinct manner. corroborate findings using MolPad dashboard, conducted analysis two cheeses (C) three production batches, spanning weeks 2 13.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":"cheese-data","dir":"Reference","previous_headings":"","what":"cheese data","title":"Cheese data — cheese","text":"data frame 106239 rows 18 variables: ID sample ID A_1~A_5 Time series data measured cheese . C_1~C_5 Time series data measured cheese C. domain category feature phylum category feature class category feature order category feature family category feature genus category feature","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":"annotation-data","dir":"Reference","previous_headings":"","what":"annotation data","title":"Cheese data — cheese","text":"data frame 86156 rows 9 variables: ID sample ID GO_ID GO IDs, represents link gene product type molecular function KEGG_ID KEGG IDs, linking genomic information higher order functional information. domain category feature phylum category feature class category feature order category feature family category feature genus category feature","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cheese data — cheese","text":"","code":"data(cheese) head(annotations) #> # A tibble: 6 × 9 #> ID GO_ID KEGG_ID domain phylum class order family genus #> #> 1 9 - - Bacteria Pseudomonadota Alphaproteobac… Caul… Caulo… Caul… #> 2 10 - - Bacteria Pseudomonadota Alphaproteobac… Caul… Caulo… Caul… #> 3 11 - - Bacteria Pseudomonadota Alphaproteobac… Hyph… Barto… Bart… #> 4 12 - - Bacteria Pseudomonadota Alphaproteobac… Hyph… Bruce… Bruc… #> 5 13 - - Bacteria Pseudomonadota Alphaproteobac… Hyph… Nitro… Brad… #> 6 14 - - Bacteria Pseudomonadota Alphaproteobac… Hyph… Phyll… Meso… head(cheese) #> # A tibble: 6 × 18 #> ID A_1 A_2 A_3 A_4 A_5 C_1 C_3 C_4 C_5 GO_ID KEGG_ID #> #> 1 1 38 23 27 5 11 13 1 3 0 NA NA #> 2 2 23 6 4 0 9 21 1 0 2 NA NA #> 3 3 24 5 37 10 19 3 0 1 4 NA NA #> 4 4 3 2 4 2 16 56 1 7 17 NA NA #> 5 5 58 14 45 13 32 82 3 8 3 NA NA #> 6 6 12 9 14 4 13 2 0 1 4 NA NA #> # ℹ 6 more variables: domain , phylum , class , order , #> # family , genus "},{"path":"https://kaiyanm.github.io/MolPad/reference/color_palettes__.html","id":null,"dir":"Reference","previous_headings":"","what":"Color palettes — color_palettes__","title":"Color palettes — color_palettes__","text":"internal function built-color palettes.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/color_palettes__.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Color palettes — color_palettes__","text":"","code":"color_palettes__(name)"},{"path":"https://kaiyanm.github.io/MolPad/reference/color_palettes__.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Color palettes — color_palettes__","text":"name string two options: \"graytone\" \"darkwarm\".","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/color_palettes__.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Color palettes — color_palettes__","text":"","code":"color_palettes__(\"graytone\") #> [1] \"#c3c5c7\" \"#30beba\" \"#998a73\" \"#807566\" \"#a5b1c9\" \"#5d5232\" \"#9a2b41\" #> [8] \"#d6b7a2\" \"#882db4\" \"#b47a53\" color_palettes__(\"darkwarm\") #> [1] \"#251305\" \"#C70A80\" \"#FBCB0A\" \"#ff1122\" \"#7D7463\" \"#CECE5A\" \"#FF9B9B\" #> [8] \"#A459D1\" \"#00235B\" \"#7E1717\""},{"path":"https://kaiyanm.github.io/MolPad/reference/convert_range.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert range — convert_range","title":"Convert range — convert_range","text":"internal function range conversion. element x, returns distance minimal values divided range.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/convert_range.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert range — convert_range","text":"","code":"convert_range(x)"},{"path":"https://kaiyanm.github.io/MolPad/reference/convert_range.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert range — convert_range","text":"x vector numbers.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/convert_range.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert range — convert_range","text":"vector calculated (x - min(x)) / (max(x) - min(x))","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/convert_range.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert range — convert_range","text":"","code":"convert_range(5:10) #> [1] 0.0 0.2 0.4 0.6 0.8 1.0"},{"path":"https://kaiyanm.github.io/MolPad/reference/extend.color__.html","id":null,"dir":"Reference","previous_headings":"","what":"Extend color palettes — extend.color__","title":"Extend color palettes — extend.color__","text":"internal function designed pair vectors color palettes automatically generate colors longer vectors find match.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/extend.color__.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extend color palettes — extend.color__","text":"","code":"extend.color__(n, colors, extendby = 1, alpha = 1, ...)"},{"path":"https://kaiyanm.github.io/MolPad/reference/extend.color__.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extend color palettes — extend.color__","text":"n number. length feature vector want match colors. colors vector colors (finite). extendby number select 1, 2, 3, 4 5, representing distinct auto-fill schemes. alpha number select range 0,1.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gAnnotation.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate processed annotation — gAnnotation","title":"Generate processed annotation — gAnnotation","text":"gAnnotation() provides standard input format dashboard, allowing users select two columns primary factors wish visualize describe data.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gAnnotation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate processed annotation — gAnnotation","text":"","code":"gAnnotation(data, first_order, second_order)"},{"path":"https://kaiyanm.github.io/MolPad/reference/gAnnotation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate processed annotation — gAnnotation","text":"data data.frame containing annotations used describing features measured time point. also required include ID least two categorical variables. first_order string. name one column categorical variable data. second_order string. name another column categorical variable data.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gAnnotation.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate processed annotation — gAnnotation","text":"Guidelines Selecting Annotations: first-order annotation recommended functional, pathway functional system. parameter primarily serve purpose filtering one network time displaying dashboard. second-order annotation utilized illustrate composition first-order annotation using bar plot. Therefore, better set taxon, class label, etc.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gAnnotation.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate processed annotation — gAnnotation","text":"","code":"data(test_data) test_annotations_processed <- gAnnotation(test_annotations,system,class) head(test_annotations_processed) #> ID #> 1 1 #> 2 2 #> 3 3 #> 4 4 #> 5 5 #> 6 6 #> GO_ID #> 1 #> 2 #> 3 GO:0003674,GO:0003824,GO:0005575,GO:0006629,GO:0006644,GO:0006650,GO:0006655,GO:0006793,GO:0006796,GO:0008150,GO:0008152,GO:0008610,GO:0008654,GO:0008808,GO:0009058,GO:0009987,GO:0016020,GO:0016740,GO:0016772,GO:0016780,GO:0019637,GO:0030572,GO:0032048,GO:0032049,GO:0044237,GO:0044238,GO:0044249,GO:0044255,GO:0045017,GO:0046471,GO:0046474,GO:0046486,GO:0071704,GO:0090407,GO:1901576 #> 4 GO:0003674,GO:0003676,GO:0003723,GO:0003729,GO:0003824,GO:0004654,GO:0005488,GO:0005575,GO:0005622,GO:0005623,GO:0005737,GO:0006139,GO:0006401,GO:0006402,GO:0006725,GO:0006807,GO:0008150,GO:0008152,GO:0009056,GO:0009057,GO:0009892,GO:0009987,GO:0010468,GO:0010605,GO:0010629,GO:0016070,GO:0016071,GO:0016740,GO:0016772,GO:0016779,GO:0019222,GO:0019439,GO:0034641,GO:0034655,GO:0043170,GO:0044237,GO:0044238,GO:0044248,GO:0044260,GO:0044265,GO:0044270,GO:0044424,GO:0044464,GO:0046483,GO:0046700,GO:0048519,GO:0050789,GO:0060255,GO:0065007,GO:0071704,GO:0090304,GO:0097159,GO:1901360,GO:1901361,GO:1901363,GO:1901575 #> 5 #> 6 #> KEGG_ID Pathway taxonomic.scope #> 1 K07124 Integumentary System hormonal proteins #> 2 Skeletal System structural proteins #> 3 K06131 Muscular System enzymes #> 4 K00962 Nervous System contractile proteins #> 5 K02083 Endocrine System contractile proteins #> 6 Cardiovascular System structural proteins"},{"path":"https://kaiyanm.github.io/MolPad/reference/gClusters.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate clusters — gClusters","title":"Generate clusters — gClusters","text":"gClusters() returns clusters generated k-means yield elbow plot way finding optimal parameter.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gClusters.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate clusters — gClusters","text":"","code":"gClusters(data, ncluster = 20, elbow.max = 50, ...)"},{"path":"https://kaiyanm.github.io/MolPad/reference/gClusters.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate clusters — gClusters","text":"data scaled data.frame contain variables ID, value time_1, ..., value time_k, type extracting patterns across time. See also pre_process(). ncluster number clusters. related complexity information network: choosing ncluster, suggest thinking many nodes show visualization representative want clustered pattern. elbow.max number maximum value x-axis elbow method plot. larger expected ncluster smaller sample size. iter.max number maximum iterations allowed k-means. See also stats::kmeans. nstart number random attempts generating initial configurations. k-means algorithm choose best one among attempts. larger data, 'nstart' can set lower just set 1. See also stats::kmeans.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gClusters.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate clusters — gClusters","text":"function return list 2 elements: k-means cluster result elbow method plot.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gClusters.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate clusters — gClusters","text":"determine optimal number clusters (ncluster), advised closely examine elbow plot identify point graph substantial change 'elbow' occurs. often indicative suitable cluster count. cases dataset extensive intricate, might consider increasing value elbow.max ensure comprehensive exploration potential cluster counts. can help achieving accurate meaningful results, especially working larger complex datasets. function can executed data parameter outset. However, achieve best clustering results, adjustments recommended. initial run, users expected adjust function's parameters based clustering outcomes elbow plot analysis.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gClusters.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate clusters — gClusters","text":"","code":"data(test_data) reslist <- gClusters(test_data_processed) # k-means result reslist[[1]] #> K-means clustering with 20 clusters of sizes 7, 6, 7, 10, 8, 2, 3, 5, 4, 6, 10, 3, 3, 4, 4, 6, 4, 3, 2, 3 #> #> Cluster means: #> T1 T2 T3 T4 T5 T6 #> 1 2.22789830 0.02950883 -0.75836275 -0.2619747 -0.5953255 -0.70974025 #> 2 2.77349939 -0.21724464 -0.35972402 -0.4172516 -0.5000122 -0.11678355 #> 3 0.93534606 -0.39771771 0.31238452 1.1531782 1.1995301 -1.05203742 #> 4 -0.27715339 -0.39760069 -0.06466866 1.3995407 2.1316423 -0.28068841 #> 5 0.11559709 -0.19000668 -0.25629794 -0.1487697 -0.4383348 2.58301481 #> 6 -0.27847690 -0.82258408 -0.82258408 -0.8225841 1.2338761 -0.82258408 #> 7 0.26998015 -0.08094677 -0.28926312 -0.7813127 -0.6065051 -0.14945230 #> 8 -0.81355388 -0.84696982 -0.32573017 1.1218534 2.0955812 -0.56207309 #> 9 1.45173820 0.26236463 1.31600581 -0.2531012 0.2004547 -1.12389420 #> 10 0.34700978 0.13673201 0.82464908 0.2571569 1.2656031 0.63541302 #> 11 -0.19020083 -0.69654961 -0.66836949 -0.4657663 -0.3851487 -0.55904213 #> 12 1.62582942 1.51619556 -0.35316244 0.2978109 -0.1777508 0.02299732 #> 13 -0.09187847 -0.87257293 -0.87257293 -0.8725729 -0.5696817 2.05668333 #> 14 0.85679806 -0.37993358 -0.46099690 0.3934155 0.5857582 1.85027957 #> 15 1.12205113 0.09185659 -0.89815993 -0.7233153 -0.5717924 -0.86037548 #> 16 2.09193480 -0.33320285 -0.50786563 -0.5579659 -0.5579659 1.54559773 #> 17 1.56599593 -0.49119311 -0.83000857 -0.6160199 -0.2092753 1.19151568 #> 18 0.89463785 0.01698143 -0.57023879 -1.1815834 -1.1815834 1.18824796 #> 19 -0.13543375 -0.90710305 -0.66248003 -0.4178570 -0.6624800 1.37010464 #> 20 1.31631350 -0.76059916 -0.76059916 1.5193444 0.6006058 0.84385791 #> T7 T8 T9 T10 #> 1 -0.567081109 0.132060988 0.03929621 0.46372005 #> 2 -0.357532815 -0.298895212 -0.29119673 -0.21485863 #> 3 -1.222209771 -0.334925678 -0.36853535 -0.22501291 #> 4 -0.548494719 -0.646992821 -0.66392148 -0.65166279 #> 5 -0.006327004 -0.327677972 -0.66559889 -0.66559889 #> 6 -0.822584085 1.233876127 1.23387613 0.68976894 #> 7 -0.763902351 -0.412975440 0.39220455 2.42217307 #> 8 -0.651292750 -0.498839228 0.42446013 0.05656427 #> 9 -0.987867401 -0.340013670 -0.33085684 -0.19483004 #> 10 -1.004339966 -0.620506186 -0.71949086 -1.12222690 #> 11 -0.566522777 0.002653353 1.73917982 1.78976662 #> 12 -0.770498738 -0.620423681 -0.77049874 -0.77049874 #> 13 0.882337606 0.086536293 0.12686085 0.12686085 #> 14 -0.526130916 -0.896529515 -0.89652951 -0.52613092 #> 15 -0.822591022 0.091471523 1.00908283 1.56177203 #> 16 -0.425513174 -0.557965933 -0.30571480 -0.39133827 #> 17 0.774154665 0.196302112 -0.62663627 -0.95483532 #> 18 0.894637851 -0.836550610 0.41343688 0.36201421 #> 19 -0.907103054 -0.417856999 1.37010464 1.37010464 #> 20 -0.666108041 -0.666108041 -0.66610804 -0.76059916 #> #> Clustering vector: #> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 #> 11 16 6 10 10 11 13 12 4 8 3 7 15 2 3 8 5 11 9 1 #> 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 #> 17 11 5 16 2 8 20 1 5 4 17 4 10 6 4 10 3 15 11 12 #> 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 #> 5 16 11 11 14 11 5 14 16 5 1 17 18 15 5 17 13 4 1 4 #> 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 #> 3 14 20 15 10 16 2 14 9 9 7 8 5 12 7 4 3 4 3 20 #> 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 #> 3 19 1 13 19 16 9 2 18 2 4 18 4 1 11 10 8 2 11 1 #> #> Within cluster sum of squares by cluster: #> [1] 13.8467580 2.0713631 15.4102772 5.9461649 8.0412334 0.9921913 #> [7] 3.1842269 3.4617088 9.9418120 17.6305488 8.0515257 4.9390800 #> [13] 4.0041862 7.3057948 4.7723673 3.0260293 7.3290085 5.6033175 #> [19] 0.9549143 2.4583646 #> (between_SS / total_SS = 83.4 %) #> #> Available components: #> #> [1] \"cluster\" \"centers\" \"totss\" \"withinss\" \"tot.withinss\" #> [6] \"betweenss\" \"size\" \"iter\" \"ifault\" # elbow plot reslist[[2]]"},{"path":"https://kaiyanm.github.io/MolPad/reference/gDashboard.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate shiny dashboard — gDashboard","title":"Generate shiny dashboard — gDashboard","text":"outputs g-functions, ready create custom Molpad dashboard. sure specify web ID columns corresponding column names.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gDashboard.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate shiny dashboard — gDashboard","text":"","code":"gDashboard( data, cluster, annotation, networkres, dashboardtitle = \"MolPad Dashboard\", id_colname = NULL, id_type = NULL )"},{"path":"https://kaiyanm.github.io/MolPad/reference/gDashboard.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate shiny dashboard — gDashboard","text":"data output pre_process():scaled data.frame contain variables ID, value time_1, ..., value time_k, type. cluster output gClusters(): list contains result k-means Cluster means, vectors sum squares. annotation output gAnnotation(): data.frame containing annotations describing features. Variables must include ID, Pathway, taxonomic.scope. Note NA permitted 3 variables. networkres output gNetwork(): data.frame 4 variables weight,IncNodePurity,var_names,. dashboardtitle string customized dashboard name. id_colname single string string vector. column names annotation dataset vector contains external database IDs. id_type single string string vector. corresponding database names columns, must chosen \"KEGG\" \"GO\".","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gDashboard.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate shiny dashboard — gDashboard","text":"Please ensure columns containing external database IDs adhere following standards: KEGG ID: Begin 'K' followed 5 digits, example, K05685 K06671. GO ID: Begin 'GO:' followed 7 digits, GO:0003674.\"","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gDashboard.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate shiny dashboard — gDashboard","text":"","code":"if (FALSE) { data(test_data) gDashboard(test_data_processed,test_cluster,test_annotations_processed,test_network,id_colname = c(\"GO_ID\",\"KEGG_ID\"),id_type = c(\"GO\",\"KEGG\")) }"},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate prediction network — gNetwork","title":"Generate prediction network — gNetwork","text":"gNetwork() generates prediction network functional annotation. every feature, features considered independent variables, top predictors selected based %IncMSE.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate prediction network — gNetwork","text":"","code":"gNetwork(clusters, ntop = 10, method = \"randomforest\")"},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate prediction network — gNetwork","text":"clusters list two outputs gClusters(). first element k-means result (See also stats::kmeans), element plot automatically omitted ease directly passing results function. ntop number pick set top n predictors(default: top 10) feature important ones. used construct network among clustered patterns. seed number random seed.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate prediction network — gNetwork","text":"assess relationships clusters using Mean Squared Error (MSE) changes resulting random shuffling. gNetwork()'s output includes edge weights node pairs, essential inputs dashboard.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate prediction network — gNetwork","text":"","code":"data(test_data) networkres <- gNetwork(test_cluster, ntop = 3) head(networkres) #> weight IncNodePurity var_names from #> 1 1.6429045 0.7371260 Group_3 Group_1 #> 2 0.4588961 0.9310697 Group_5 Group_1 #> 3 -0.7833169 0.6761286 Group_2 Group_1 #> 4 5.0076415 0.8821582 Group_5 Group_2 #> 5 4.1618215 1.1976058 Group_4 Group_2 #> 6 2.5714583 1.2820391 Group_1 Group_2 gNetwork_view(networkres)"},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork_view.html","id":null,"dir":"Reference","previous_headings":"","what":"View the feature importance plot — gNetwork_view","title":"View the feature importance plot — gNetwork_view","text":"gNetwork_view() Generate feature importance plot random forest result. plot visually highlights importance individual cluster within dataset, helping identify key factors predictive modeling.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork_view.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"View the feature importance plot — gNetwork_view","text":"","code":"gNetwork_view(data)"},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork_view.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"View the feature importance plot — gNetwork_view","text":"data dataframe; output gNetwork(), including 4 variables: weight, IncNodePurity, var_names . weight edge value, IncNodePurity(%IncMSE) measure predictions result var_names permuted. dependent variable round random forest.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork_view.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"View the feature importance plot — gNetwork_view","text":"","code":"data(test_data) gNetwork_view(test_network) #> Warning: Removed 1 rows containing missing values (`geom_segment()`). #> Warning: Removed 1 rows containing missing values (`geom_point()`)."},{"path":"https://kaiyanm.github.io/MolPad/reference/make_line_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Make line plot — make_line_plot","title":"Make line plot — make_line_plot","text":"Generate line ribbon plot dashboard.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_line_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make line plot — make_line_plot","text":"","code":"make_line_plot(dfgroup_long, selected_groups, selected_taxa)"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_line_plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Make line plot — make_line_plot","text":"function makes ribbon plot every brush action Shiny show min, max, mean value clustered pattern group across time. ribbon plot grouped colored type variable input datasets. See also ggplot2::geom_ribbon.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_line_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Make line plot — make_line_plot","text":"","code":"data(test_data) make_line_plot(test_maindata, \"Group_5\", c(\"hormonal proteins\",\"structural proteins\",\"enzymes\",\"storage proteins\",\"antibodies\",\"transport proteins\"))"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_stackbar_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Make stackbar plot — make_stackbar_plot","title":"Make stackbar plot — make_stackbar_plot","text":"Generate stackbar plot dashboard.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_stackbar_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make stackbar plot — make_stackbar_plot","text":"","code":"make_stackbar_plot(dfgroup_long, selected_groups, selected_taxa)"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_stackbar_plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Make stackbar plot — make_stackbar_plot","text":"function makes bar plot every brush action Shiny show components clustered pattern group. bar plot colored taxonomic.scope variable processed annotation dataset generated gAnnotation(). See also ggplot2::geom_bar.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_stackbar_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Make stackbar plot — make_stackbar_plot","text":"","code":"data(test_data) make_stackbar_plot(test_maindata, \"Group_5\", c(\"hormonal proteins\",\"structural proteins\",\"enzymes\",\"storage proteins\",\"antibodies\",\"transport proteins\"))"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_graph.html","id":null,"dir":"Reference","previous_headings":"","what":"Make graph plot — make_the_graph","title":"Make graph plot — make_the_graph","text":"Generate graph dashboard.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_graph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make graph plot — make_the_graph","text":"","code":"make_the_graph(ptw, network_output, min_weight, s_ptw, graph_layout)"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_graph.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Make graph plot — make_the_graph","text":"function makes network plot every Pathway selection Shiny show relationship among clustered pattern groups. network plot built dataset generated gNetwork() can adjusted layout minimum weights. See also ggraph::ggraph.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_graph.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Make graph plot — make_the_graph","text":"","code":"data(test_data) make_the_graph(test_graphptw, test_network, 0.03, \"Muscular System\",\"kk\")"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_table.html","id":null,"dir":"Reference","previous_headings":"","what":"Make table from brushed region — make_the_table","title":"Make table from brushed region — make_the_table","text":"Generate table selected groups dashboard.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make table from brushed region — make_the_table","text":"","code":"make_the_table(p)"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Make table from brushed region — make_the_table","text":"p ggplot output, see also ggplot2::ggplot_build.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_table.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Make table from brushed region — make_the_table","text":"function simply aims collect position information brushed area network plot returns annotation table corresponding features. takes plot object produce object information table.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/mass_produce_lm__.html","id":null,"dir":"Reference","previous_headings":"","what":"Mass produce linear model — mass_produce_lm__","title":"Mass produce linear model — mass_produce_lm__","text":"Take column dependent vairable time, produce n linear models n columns dataset.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/mass_produce_lm__.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mass produce linear model — mass_produce_lm__","text":"","code":"mass_produce_lm__(data)"},{"path":"https://kaiyanm.github.io/MolPad/reference/mass_produce_lm__.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mass produce linear model — mass_produce_lm__","text":"data dataframe.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/mass_produce_lm__.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Mass produce linear model — mass_produce_lm__","text":"internal function designed automatically generate list functions regression. column considered response variable columns. resulting functions can utilized inputs random forest regression gNetwork().","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/match.color__.html","id":null,"dir":"Reference","previous_headings":"","what":"Match color — match.color__","title":"Match color — match.color__","text":"Match vector finite list colors.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/match.color__.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Match color — match.color__","text":"","code":"match.color__(valist, mycolors, extendby = 5)"},{"path":"https://kaiyanm.github.io/MolPad/reference/match.color__.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Match color — match.color__","text":"valist vector want specify colors element. mycolors vector colors. extendby number select 1, 2, 3, 4, 5, representing distinct auto-fill schemes.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/match.color__.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Match color — match.color__","text":"","code":"my_vector <- paste0(\"N_\",1:10) match.color__(my_vector,c(\"red\",\"yellow\",\"blue\")) #> N_1 N_2 N_3 N_4 N_5 #> \"red\" \"yellow\" \"blue\" \"white\" \"antiquewhite3\" #> N_6 N_7 N_8 N_9 N_10 #> \"aquamarine3\" \"azure3\" \"bisque2\" \"blue\" \"blueviolet\""},{"path":"https://kaiyanm.github.io/MolPad/reference/match_database.html","id":null,"dir":"Reference","previous_headings":"","what":"Match database — match_database","title":"Match database — match_database","text":"matches selected columns online database names.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/match_database.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Match database — match_database","text":"","code":"match_database(data, id_colname, id_type)"},{"path":"https://kaiyanm.github.io/MolPad/reference/match_database.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Match database — match_database","text":"match_database() internal function matches selected columns dataset corresponding databases. Currently MolPad support two types annotation database: GO KEGG. want custom URL platform, download modify paste_URL() function.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/match_database.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Match database — match_database","text":"","code":"data(test_data) head(test_annotations_processed) #> ID #> 1 1 #> 2 2 #> 3 3 #> 4 4 #> 5 5 #> 6 6 #> GO_ID #> 1 #> 2 #> 3 GO:0003674,GO:0003824,GO:0005575,GO:0006629,GO:0006644,GO:0006650,GO:0006655,GO:0006793,GO:0006796,GO:0008150,GO:0008152,GO:0008610,GO:0008654,GO:0008808,GO:0009058,GO:0009987,GO:0016020,GO:0016740,GO:0016772,GO:0016780,GO:0019637,GO:0030572,GO:0032048,GO:0032049,GO:0044237,GO:0044238,GO:0044249,GO:0044255,GO:0045017,GO:0046471,GO:0046474,GO:0046486,GO:0071704,GO:0090407,GO:1901576 #> 4 GO:0003674,GO:0003676,GO:0003723,GO:0003729,GO:0003824,GO:0004654,GO:0005488,GO:0005575,GO:0005622,GO:0005623,GO:0005737,GO:0006139,GO:0006401,GO:0006402,GO:0006725,GO:0006807,GO:0008150,GO:0008152,GO:0009056,GO:0009057,GO:0009892,GO:0009987,GO:0010468,GO:0010605,GO:0010629,GO:0016070,GO:0016071,GO:0016740,GO:0016772,GO:0016779,GO:0019222,GO:0019439,GO:0034641,GO:0034655,GO:0043170,GO:0044237,GO:0044238,GO:0044248,GO:0044260,GO:0044265,GO:0044270,GO:0044424,GO:0044464,GO:0046483,GO:0046700,GO:0048519,GO:0050789,GO:0060255,GO:0065007,GO:0071704,GO:0090304,GO:0097159,GO:1901360,GO:1901361,GO:1901363,GO:1901575 #> 5 #> 6 #> KEGG_ID Pathway taxonomic.scope #> 1 K07124 Integumentary System hormonal proteins #> 2 Skeletal System structural proteins #> 3 K06131 Muscular System enzymes #> 4 K00962 Nervous System contractile proteins #> 5 K02083 Endocrine System contractile proteins #> 6 Cardiovascular System structural proteins head(match_database(test_annotations_processed,id_colname = c(\"GO_ID\",\"KEGG_ID\"),id_type = c(\"GO\",\"KEGG\"))) #> ID #> 1 1 #> 2 2 #> 3 3 #> 4 4 #> 5 5 #> 6 6 #> GO_ID #> 1 #> 2 #> 3 GO:0003674<\/a>
GO:0003824<\/a>
GO:0005575<\/a>
GO:0006629<\/a>
GO:0006644<\/a>
GO:0006650<\/a>
GO:0006655<\/a>
GO:0006793<\/a>
GO:0006796<\/a>
GO:0008150<\/a>
GO:0008152<\/a>
GO:0008610<\/a>
GO:0008654<\/a>
GO:0008808<\/a>
GO:0009058<\/a>
GO:0009987<\/a>
GO:0016020<\/a>
GO:0016740<\/a>
GO:0016772<\/a>
GO:0016780<\/a>
GO:0019637<\/a>
GO:0030572<\/a>
GO:0032048<\/a>
GO:0032049<\/a>
GO:0044237<\/a>
GO:0044238<\/a>
GO:0044249<\/a>
GO:0044255<\/a>
GO:0045017<\/a>
GO:0046471<\/a>
GO:0046474<\/a>
GO:0046486<\/a>
GO:0071704<\/a>
GO:0090407<\/a>
GO:1901576<\/a> #> 4 GO:0003674<\/a>
GO:0003676<\/a>
GO:0003723<\/a>
GO:0003729<\/a>
GO:0003824<\/a>
GO:0004654<\/a>
GO:0005488<\/a>
GO:0005575<\/a>
GO:0005622<\/a>
GO:0005623<\/a>
GO:0005737<\/a>
GO:0006139<\/a>
GO:0006401<\/a>
GO:0006402<\/a>
GO:0006725<\/a>
GO:0006807<\/a>
GO:0008150<\/a>
GO:0008152<\/a>
GO:0009056<\/a>
GO:0009057<\/a>
GO:0009892<\/a>
GO:0009987<\/a>
GO:0010468<\/a>
GO:0010605<\/a>
GO:0010629<\/a>
GO:0016070<\/a>
GO:0016071<\/a>
GO:0016740<\/a>
GO:0016772<\/a>
GO:0016779<\/a>
GO:0019222<\/a>
GO:0019439<\/a>
GO:0034641<\/a>
GO:0034655<\/a>
GO:0043170<\/a>
GO:0044237<\/a>
GO:0044238<\/a>
GO:0044248<\/a>
GO:0044260<\/a>
GO:0044265<\/a>
GO:0044270<\/a>
GO:0044424<\/a>
GO:0044464<\/a>
GO:0046483<\/a>
GO:0046700<\/a>
GO:0048519<\/a>
GO:0050789<\/a>
GO:0060255<\/a>
GO:0065007<\/a>
GO:0071704<\/a>
GO:0090304<\/a>
GO:0097159<\/a>
GO:1901360<\/a>
GO:1901361<\/a>
GO:1901363<\/a>
GO:1901575<\/a> #> 5 #> 6 #> KEGG_ID #> 1 K07124<\/a> #> 2 #> 3 K06131<\/a> #> 4 K00962<\/a> #> 5 K02083<\/a> #> 6 #> Pathway taxonomic.scope #> 1 Integumentary System hormonal proteins #> 2 Skeletal System structural proteins #> 3 Muscular System enzymes #> 4 Nervous System contractile proteins #> 5 Endocrine System contractile proteins #> 6 Cardiovascular System structural proteins"},{"path":"https://kaiyanm.github.io/MolPad/reference/paste_URL.html","id":null,"dir":"Reference","previous_headings":"","what":"Paste URL — paste_URL","title":"Paste URL — paste_URL","text":"Retrieve database IDs associate respective URLs.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/paste_URL.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Paste URL — paste_URL","text":"","code":"paste_URL(x, id_type)"},{"path":"https://kaiyanm.github.io/MolPad/reference/paste_URL.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Paste URL — paste_URL","text":"","code":"data(test_data) paste_URL(test_annotations$GO_ID[1:4], id_type = \"GO\") #> [1] NA #> [2] NA #> [3] \"GO:0003674<\/a>
GO:0003824<\/a>
GO:0005575<\/a>
GO:0006629<\/a>
GO:0006644<\/a>
GO:0006650<\/a>
GO:0006655<\/a>
GO:0006793<\/a>
GO:0006796<\/a>
GO:0008150<\/a>
GO:0008152<\/a>
GO:0008610<\/a>
GO:0008654<\/a>
GO:0008808<\/a>
GO:0009058<\/a>
GO:0009987<\/a>
GO:0016020<\/a>
GO:0016740<\/a>
GO:0016772<\/a>
GO:0016780<\/a>
GO:0019637<\/a>
GO:0030572<\/a>
GO:0032048<\/a>
GO:0032049<\/a>
GO:0044237<\/a>
GO:0044238<\/a>
GO:0044249<\/a>
GO:0044255<\/a>
GO:0045017<\/a>
GO:0046471<\/a>
GO:0046474<\/a>
GO:0046486<\/a>
GO:0071704<\/a>
GO:0090407<\/a>
GO:1901576<\/a>\" #> [4] \"GO:0003674<\/a>
GO:0003676<\/a>
GO:0003723<\/a>
GO:0003729<\/a>
GO:0003824<\/a>
GO:0004654<\/a>
GO:0005488<\/a>
GO:0005575<\/a>
GO:0005622<\/a>
GO:0005623<\/a>
GO:0005737<\/a>
GO:0006139<\/a>
GO:0006401<\/a>
GO:0006402<\/a>
GO:0006725<\/a>
GO:0006807<\/a>
GO:0008150<\/a>
GO:0008152<\/a>
GO:0009056<\/a>
GO:0009057<\/a>
GO:0009892<\/a>
GO:0009987<\/a>
GO:0010468<\/a>
GO:0010605<\/a>
GO:0010629<\/a>
GO:0016070<\/a>
GO:0016071<\/a>
GO:0016740<\/a>
GO:0016772<\/a>
GO:0016779<\/a>
GO:0019222<\/a>
GO:0019439<\/a>
GO:0034641<\/a>
GO:0034655<\/a>
GO:0043170<\/a>
GO:0044237<\/a>
GO:0044238<\/a>
GO:0044248<\/a>
GO:0044260<\/a>
GO:0044265<\/a>
GO:0044270<\/a>
GO:0044424<\/a>
GO:0044464<\/a>
GO:0046483<\/a>
GO:0046700<\/a>
GO:0048519<\/a>
GO:0050789<\/a>
GO:0060255<\/a>
GO:0065007<\/a>
GO:0071704<\/a>
GO:0090304<\/a>
GO:0097159<\/a>
GO:1901360<\/a>
GO:1901361<\/a>
GO:1901363<\/a>
GO:1901575<\/a>\""},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":null,"dir":"Reference","previous_headings":"","what":"Pre-processing datasets — pre_process","title":"Pre-processing datasets — pre_process","text":"pre_process() function aids processing data inputs automatically establishes standardized format future use. allows two types data input: list datasets different sources long dataset containing specified last column type.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pre-processing datasets — pre_process","text":"","code":"pre_process( data, typenameList = NULL, replaceNA = TRUE, scale = TRUE, autoColName = \"Sec_\" )"},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pre-processing datasets — pre_process","text":"data data.frame describe feature one row. data contain variables ID value time_1, ..., value time_k, type extracting patterns across time. Note initial last column must exactly ID type. multiple data.frame format needs analyzed, also put list data.frame argument. case, variable type required generated next argument typenameList. typenameList vector strings. parameter used clarify source names data.frame, applicable input data list data.frame. default, set \"Dataset_1\", \"Dataset_2\", ..., etc. scale Logical; scale TRUE (default), standardize data.frame row base::scale. converts original value z-score. See also scale_by_row__(). autoColName string; autoColName -NULL (default), automatically set uniform column names data.frames. parameter applicable input data list data.frame. replaceNa Logical; replaceNa TRUE (default), replace NA 0.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pre-processing datasets — pre_process","text":"function returns long data.frame columns ID, value time_1, ..., value time_k, type.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Pre-processing datasets — pre_process","text":"consider two distinct scenarios application: one scenario, individuals collect several datasets various aspects instruments objects. example, might separately detecting lipids, metabolites, peptides specific soil sample. scenario, data uniform quality, can categorized larger groups exhibit significant differences. cases, pre_process() function serves valuable versatile tool. Yet, function optional generating dashboard. Users can perform processing long format matches required output. However, mindful number samples (timepoints) must greater 5 avoid potential errors subsequent prediction section.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pre-processing datasets — pre_process","text":"","code":"data(test_data) head(test_data, 10) #> ID T1 T2 T3 T4 T5 T6 T7 T8 T9 T10 type #> 1 1 1 0 0 1 1 0 0 1 6 6 type_A #> 2 2 6 0 0 0 0 3 1 0 2 1 type_A #> 3 3 1 0 0 0 2 0 0 2 2 1 type_A #> 4 4 4 5 3 3 7 2 1 1 0 0 type_A #> 5 5 4 3 NA 2 5 5 0 0 0 0 type_A #> 6 6 4 1 0 1 3 1 3 5 11 14 type_A #> 7 7 1 0 0 0 1 3 3 1 1 1 type_A #> 8 8 4 2 1 1 1 1 0 0 0 0 type_A #> 9 9 1 1 1 19 22 1 2 1 1 2 type_A #> 10 10 1 1 3 5 8 2 2 2 5 2 type_A a <- pre_process(test_data) head(a, 10) #> ID T1 T2 T3 T4 T5 T6 #> 1 1 -0.25354628 -0.6761234 -0.67612340 -0.25354628 -0.25354628 -0.6761234 #> 2 2 2.41458180 -0.6678631 -0.66786305 -0.66786305 -0.66786305 0.8733594 #> 3 3 0.21764288 -0.8705715 -0.87057150 -0.87057150 1.30585725 -0.8705715 #> 4 4 0.61658123 1.0569964 0.17616607 0.17616607 1.93782672 -0.2642491 #> 5 5 0.96186009 0.5038315 -0.87025436 0.04580286 1.41988870 1.4198887 #> 6 6 -0.06459959 -0.7105955 -0.92592741 -0.71059546 -0.27993154 -0.7105955 #> 7 7 -0.09086738 -0.9995412 -0.99954118 -0.99954118 -0.09086738 1.7264802 #> 8 8 2.40535118 0.8017837 0.00000000 0.00000000 0.00000000 0.0000000 #> 9 9 -0.50260633 -0.5026063 -0.50260633 1.70395805 2.07171878 -0.5026063 #> 10 10 -0.94019379 -0.9401938 -0.04477113 0.85065153 2.19378551 -0.4924825 #> T7 T8 T9 T10 type #> 1 -0.6761234 -0.25354628 1.85933936 1.85933936 type_A #> 2 -0.1541222 -0.66786305 0.35961857 -0.15412224 type_A #> 3 -0.8705715 1.30585725 1.30585725 0.21764288 type_A #> 4 -0.7046643 -0.70466426 -1.14507943 -1.14507943 type_A #> 5 -0.8702544 -0.87025436 -0.87025436 -0.87025436 type_A #> 6 -0.2799315 0.15073237 1.44272411 2.08871998 type_A #> 7 1.7264802 -0.09086738 -0.09086738 -0.09086738 type_A #> 8 -0.8017837 -0.80178373 -0.80178373 -0.80178373 type_A #> 9 -0.3800194 -0.50260633 -0.50260633 -0.38001942 type_A #> 10 -0.4924825 -0.49248246 0.85065153 -0.49248246 type_A"},{"path":"https://kaiyanm.github.io/MolPad/reference/reshape_for_make_functions.html","id":null,"dir":"Reference","previous_headings":"","what":"Reshape for 'make' functions — reshape_for_make_functions","title":"Reshape for 'make' functions — reshape_for_make_functions","text":"internal function produces three primary datasets dashboard intended \"make\" functions.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/reshape_for_make_functions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reshape for 'make' functions — reshape_for_make_functions","text":"","code":"reshape_for_make_functions(data, cluster, annotation, id_colname, id_type)"},{"path":"https://kaiyanm.github.io/MolPad/reference/reshape_for_make_functions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reshape for 'make' functions — reshape_for_make_functions","text":"data output pre_process() cluster output gClusters() annotation output gPathway() id_colname columns contain database IDs. id_type corresponding database names columns.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/reshape_for_make_functions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reshape for 'make' functions — reshape_for_make_functions","text":"","code":"data(test_data) l <- reshape_for_make_functions(test_data_processed, test_cluster, test_annotations_processed, id_colname = c(\"GO_ID\",\"KEGG_ID\"),id_type = c(\"GO\",\"KEGG\")) head(l[[1]]) #> ID cluster #> 1 1 Group_2 #> 2 2 Group_5 #> 3 3 Group_2 #> 4 4 Group_4 #> 5 5 Group_4 #> 6 6 Group_2 #> GO_ID #> 1 #> 2 #> 3 GO:0003674,GO:0003824,GO:0005575,GO:0006629,GO:0006644,GO:0006650,GO:0006655,GO:0006793,GO:0006796,GO:0008150,GO:0008152,GO:0008610,GO:0008654,GO:0008808,GO:0009058,GO:0009987,GO:0016020,GO:0016740,GO:0016772,GO:0016780,GO:0019637,GO:0030572,GO:0032048,GO:0032049,GO:0044237,GO:0044238,GO:0044249,GO:0044255,GO:0045017,GO:0046471,GO:0046474,GO:0046486,GO:0071704,GO:0090407,GO:1901576 #> 4 GO:0003674,GO:0003676,GO:0003723,GO:0003729,GO:0003824,GO:0004654,GO:0005488,GO:0005575,GO:0005622,GO:0005623,GO:0005737,GO:0006139,GO:0006401,GO:0006402,GO:0006725,GO:0006807,GO:0008150,GO:0008152,GO:0009056,GO:0009057,GO:0009892,GO:0009987,GO:0010468,GO:0010605,GO:0010629,GO:0016070,GO:0016071,GO:0016740,GO:0016772,GO:0016779,GO:0019222,GO:0019439,GO:0034641,GO:0034655,GO:0043170,GO:0044237,GO:0044238,GO:0044248,GO:0044260,GO:0044265,GO:0044270,GO:0044424,GO:0044464,GO:0046483,GO:0046700,GO:0048519,GO:0050789,GO:0060255,GO:0065007,GO:0071704,GO:0090304,GO:0097159,GO:1901360,GO:1901361,GO:1901363,GO:1901575 #> 5 #> 6 #> KEGG_ID Pathway taxonomic.scope #> 1 K07124 Integumentary System hormonal proteins #> 2 Skeletal System structural proteins #> 3 K06131 Muscular System enzymes #> 4 K00962 Nervous System contractile proteins #> 5 K02083 Endocrine System contractile proteins #> 6 Cardiovascular System structural proteins head(l[[2]]) #> # A tibble: 6 × 6 #> ID type cluster day value taxonomic.scope #> #> 1 1 type_A Group_2 T1 -0.254 hormonal proteins #> 2 1 type_A Group_2 T2 -0.676 hormonal proteins #> 3 1 type_A Group_2 T3 -0.676 hormonal proteins #> 4 1 type_A Group_2 T4 -0.254 hormonal proteins #> 5 1 type_A Group_2 T5 -0.254 hormonal proteins #> 6 1 type_A Group_2 T6 -0.676 hormonal proteins head(l[[3]]) #> ID cluster #> 1 1 Group_2 #> 2 2 Group_5 #> 3 3 Group_2 #> 4 4 Group_4 #> 5 5 Group_4 #> 6 6 Group_2 #> GO_ID #> 1 #> 2 #> 3 GO:0003674<\/a>
GO:0003824<\/a>
GO:0005575<\/a>
GO:0006629<\/a>
GO:0006644<\/a>
GO:0006650<\/a>
GO:0006655<\/a>
GO:0006793<\/a>
GO:0006796<\/a>
GO:0008150<\/a>
GO:0008152<\/a>
GO:0008610<\/a>
GO:0008654<\/a>
GO:0008808<\/a>
GO:0009058<\/a>
GO:0009987<\/a>
GO:0016020<\/a>
GO:0016740<\/a>
GO:0016772<\/a>
GO:0016780<\/a>
GO:0019637<\/a>
GO:0030572<\/a>
GO:0032048<\/a>
GO:0032049<\/a>
GO:0044237<\/a>
GO:0044238<\/a>
GO:0044249<\/a>
GO:0044255<\/a>
GO:0045017<\/a>
GO:0046471<\/a>
GO:0046474<\/a>
GO:0046486<\/a>
GO:0071704<\/a>
GO:0090407<\/a>
GO:1901576<\/a> #> 4 GO:0003674<\/a>
GO:0003676<\/a>
GO:0003723<\/a>
GO:0003729<\/a>
GO:0003824<\/a>
GO:0004654<\/a>
GO:0005488<\/a>
GO:0005575<\/a>
GO:0005622<\/a>
GO:0005623<\/a>
GO:0005737<\/a>
GO:0006139<\/a>
GO:0006401<\/a>
GO:0006402<\/a>
GO:0006725<\/a>
GO:0006807<\/a>
GO:0008150<\/a>
GO:0008152<\/a>
GO:0009056<\/a>
GO:0009057<\/a>
GO:0009892<\/a>
GO:0009987<\/a>
GO:0010468<\/a>
GO:0010605<\/a>
GO:0010629<\/a>
GO:0016070<\/a>
GO:0016071<\/a>
GO:0016740<\/a>
GO:0016772<\/a>
GO:0016779<\/a>
GO:0019222<\/a>
GO:0019439<\/a>
GO:0034641<\/a>
GO:0034655<\/a>
GO:0043170<\/a>
GO:0044237<\/a>
GO:0044238<\/a>
GO:0044248<\/a>
GO:0044260<\/a>
GO:0044265<\/a>
GO:0044270<\/a>
GO:0044424<\/a>
GO:0044464<\/a>
GO:0046483<\/a>
GO:0046700<\/a>
GO:0048519<\/a>
GO:0050789<\/a>
GO:0060255<\/a>
GO:0065007<\/a>
GO:0071704<\/a>
GO:0090304<\/a>
GO:0097159<\/a>
GO:1901360<\/a>
GO:1901361<\/a>
GO:1901363<\/a>
GO:1901575<\/a> #> 5 #> 6 #> KEGG_ID #> 1 K07124<\/a> #> 2 #> 3 K06131<\/a> #> 4 K00962<\/a> #> 5 K02083<\/a> #> 6 #> Pathway taxonomic.scope #> 1 Integumentary System hormonal proteins #> 2 Skeletal System structural proteins #> 3 Muscular System enzymes #> 4 Nervous System contractile proteins #> 5 Endocrine System contractile proteins #> 6 Cardiovascular System structural proteins"},{"path":"https://kaiyanm.github.io/MolPad/reference/scale_by_row__.html","id":null,"dir":"Reference","previous_headings":"","what":"Scale by row — scale_by_row__","title":"Scale by row — scale_by_row__","text":"Scales values sample, row independently processed.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/scale_by_row__.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Scale by row — scale_by_row__","text":"","code":"scale_by_row__(data)"},{"path":"https://kaiyanm.github.io/MolPad/reference/scale_by_row__.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Scale by row — scale_by_row__","text":"input expected data frame first column ID following columns containing observations different time points. ID column remains unaltered, columns double (dbl) format scaled.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/scale_by_row__.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Scale by row — scale_by_row__","text":"","code":"data(test_data) scale_by_row__(test_data[1:5,1:10]) #> ID T1 T2 T3 T4 T5 T6 #> 1 1 -0.05847053 -0.5847053 -0.58470535 -0.05847053 -0.05847053 -0.5847053 #> 2 2 2.26366583 -0.6467617 -0.64676167 -0.64676167 -0.64676167 0.8084521 #> 3 3 0.22866478 -0.8003267 -0.80032673 -0.80032673 1.25765629 -0.8003267 #> 4 4 0.50395263 0.9575100 0.05039526 0.05039526 1.86462473 -0.4031621 #> 5 5 0.73869087 0.2841119 NA -0.17046712 1.19326987 1.1932699 #> T7 T8 T9 #> 1 -0.5847053 -0.05847053 2.5727035 #> 2 -0.1616904 -0.64676167 0.3233808 #> 3 -0.8003267 1.25765629 1.2576563 #> 4 -0.8567195 -0.85671947 -1.3102768 #> 5 -1.0796251 -1.07962512 -1.0796251"},{"path":"https://kaiyanm.github.io/MolPad/reference/test_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Test data — test_data","title":"Test data — test_data","text":"synthetically generated dataset created basic testing purposes.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/test_data.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Test data — test_data","text":"two datasets: data annotations","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/test_data.html","id":"data","dir":"Reference","previous_headings":"","what":"data","title":"Test data — test_data","text":"data frame 100 rows 12 variables: ID row ID T1~T10 count value 10 timepoints type type ~D","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/test_data.html","id":"annotations","dir":"Reference","previous_headings":"","what":"annotations","title":"Test data — test_data","text":"data frame 100 rows 5 variables: ID row ID GO_ID go ID KEGG_ID kegg ID system primary lable: 'Integumentary System', 'Skeletal System', 'Muscular System', 'Nervous System', 'Endocrine System', 'Cardiovascular System', 'Lymphatic System', 'Respiratory System', 'Digestive System', 'Urinary System' class secondary label: 'antibodies', 'contractile proteins', 'enzymes', 'hormonal proteins', 'structural proteins', 'storage proteins', 'transport proteins'","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/test_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Test data — test_data","text":"","code":"data(test_data)"},{"path":"https://kaiyanm.github.io/MolPad/reference/transpose_dataframe__.html","id":null,"dir":"Reference","previous_headings":"","what":"Transpose dataframe — transpose_dataframe__","title":"Transpose dataframe — transpose_dataframe__","text":"function transposes provided data frame, using values first column new column names.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/transpose_dataframe__.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transpose dataframe — transpose_dataframe__","text":"","code":"transpose_dataframe__(data)"},{"path":"https://kaiyanm.github.io/MolPad/reference/transpose_dataframe__.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transpose dataframe — transpose_dataframe__","text":"expected input data frame first column serves Time, subsequent columns contain observations various features.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/transpose_dataframe__.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transpose dataframe — transpose_dataframe__","text":"","code":"a <- data.frame(\"Day\"=c(\"Day1\",\"Day2\",\"Day3\"),\"feature_1\" =c(1,2,3),\"feature_2\" =c(0,4,1),\"feature_3\" =c(1,1,0)) a #> Day feature_1 feature_2 feature_3 #> 1 Day1 1 0 1 #> 2 Day2 2 4 1 #> 3 Day3 3 1 0 transpose_dataframe__(a) #> Day1 Day2 Day3 #> 1 1 2 3 #> 2 0 4 1 #> 3 1 1 0"}]
+[{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"CC0 1.0 Universal","title":"CC0 1.0 Universal","text":"CREATIVE COMMONS CORPORATION LAW FIRM PROVIDE LEGAL SERVICES. DISTRIBUTION DOCUMENT CREATE ATTORNEY-CLIENT RELATIONSHIP. CREATIVE COMMONS PROVIDES INFORMATION “-” BASIS. CREATIVE COMMONS MAKES WARRANTIES REGARDING USE DOCUMENT INFORMATION WORKS PROVIDED HEREUNDER, DISCLAIMS LIABILITY DAMAGES RESULTING USE DOCUMENT INFORMATION WORKS PROVIDED HEREUNDER.","code":""},{"path":"https://kaiyanm.github.io/MolPad/LICENSE.html","id":"statement-of-purpose","dir":"","previous_headings":"","what":"Statement of Purpose","title":"CC0 1.0 Universal","text":"laws jurisdictions throughout world automatically confer exclusive Copyright Related Rights (defined ) upon creator subsequent owner(s) (, “owner”) original work authorship /database (, “Work”). 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Affirmer understands acknowledges Creative Commons party document duty obligation respect CC0 use Work.","code":""},{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/articles/FAQ.html","id":"how-can-i-tell-if-my-dataset-is-suitable-for-using-this-dashboard","dir":"Articles","previous_headings":"Common Questions:","what":"How can I tell if my dataset is suitable for using this dashboard?","title":"FAQs","text":"lot longitudinal data least two descriptive columns (like types), match minimal needs starting dashboard. addition, data specific web-ID columns, MolPad make convenient createing links databases automatically.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/FAQ.html","id":"how-can-i-customize-my-dashboard","dir":"Articles","previous_headings":"Common Questions:","what":"How can I customize my dashboard?","title":"FAQs","text":"can edit title within gDashboard() function. additional personalization, adjusting colors sizes figures, can fork repository, create copy, make modifications dashboard.css file.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/FAQ.html","id":"what-should-i-do-if-i-only-have-a-few-time-points5","dir":"Articles","previous_headings":"Common Questions:","what":"What should I do if I only have a few time points(<5)?","title":"FAQs","text":"Firstly, strongly recommend extending length longitudinal samples. can achieve increasing number measurements two days incorporating simulated data. options don’t suit needs, can also consider using “linear” method gNetwork() function.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Case Study: Cheese Communities","text":"vignette provides comprehensive guide using MolPad case study, including data pre-processing, network generation, result analysis.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"washed-rind-cheese-microbial-communities","dir":"Articles","previous_headings":"","what":"Washed-Rind Cheese Microbial Communities","title":"Case Study: Cheese Communities","text":"Cheese making ancient craft involves coagulation milk proteins form curds, separated liquid whey. curds processed, shaped, aged develop desired texture flavor. Various factors, type milk, specific cultures bacteria, aging conditions, contribute unique characteristics cheese. One crucial step process aging practice, regular washing brine solution plays significant role. process producing cheese, regular washing brine solution aging practice can homogenize microbial communities cheese’ surface facilitate intermicrobial interactions. following parts, analyze longitudinal data set three washed-rind cheese communities collected cheese ripening.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"data","dir":"Articles","previous_headings":"","what":"Data","title":"Case Study: Cheese Communities","text":"analysis, use data set contained study Smith et al. (2022) microbial communities cheese. original research investigated successional dynamics occur within cheese rind microbial communities using combination 16S rRNA amplicon, Illumina, PacBio sequencing. functionally taxonomically annotate (using eggNOG (21) MMseqs2 (22)) contigs generated Illumina reads, demonstrate utility MolPad using single-omic. Specifically, focus Cheese Sample Cheese Sample C. detailed information attached data, please check documentation.","code":"data(\"cheese\") str(cheese) #> tibble [106,239 × 18] (S3: tbl_df/tbl/data.frame) #> $ ID : chr [1:106239] \"1\" \"2\" \"3\" \"4\" ... #> $ A_1 : int [1:106239] 38 23 24 3 58 12 1 14 1 3 ... #> $ A_2 : int [1:106239] 23 6 5 2 14 9 1 7 1 1 ... #> $ A_3 : int [1:106239] 27 4 37 4 45 14 0 14 3 5 ... #> $ A_4 : int [1:106239] 5 0 10 2 13 4 1 4 4 0 ... #> $ A_5 : int [1:106239] 11 9 19 16 32 13 0 4 1 1 ... #> $ C_1 : int [1:106239] 13 21 3 56 82 2 4 17 7 2 ... #> $ C_3 : int [1:106239] 1 1 0 1 3 0 0 0 0 1 ... #> $ C_4 : int [1:106239] 3 0 1 7 8 1 0 1 2 2 ... #> $ C_5 : int [1:106239] 0 2 4 17 3 4 0 1 8 6 ... #> $ GO_ID : chr [1:106239] NA NA NA NA ... #> $ KEGG_ID: chr [1:106239] NA NA NA NA ... #> $ domain : chr [1:106239] NA NA NA NA ... #> $ phylum : chr [1:106239] NA NA NA NA ... #> $ class : chr [1:106239] NA NA NA NA ... #> $ order : chr [1:106239] NA NA NA NA ... #> $ family : chr [1:106239] NA NA NA NA ... #> $ genus : chr [1:106239] NA NA NA NA ... str(annotations) #> tibble [86,156 × 9] (S3: tbl_df/tbl/data.frame) #> $ ID : chr [1:86156] \"9\" \"10\" \"11\" \"12\" ... #> $ GO_ID : chr [1:86156] \"-\" \"-\" \"-\" \"-\" ... #> $ KEGG_ID: chr [1:86156] \"-\" \"-\" \"-\" \"-\" ... #> $ domain : chr [1:86156] \"Bacteria\" \"Bacteria\" \"Bacteria\" \"Bacteria\" ... #> $ phylum : chr [1:86156] \"Pseudomonadota\" \"Pseudomonadota\" \"Pseudomonadota\" \"Pseudomonadota\" ... #> $ class : chr [1:86156] \"Alphaproteobacteria\" \"Alphaproteobacteria\" \"Alphaproteobacteria\" \"Alphaproteobacteria\" ... #> $ order : chr [1:86156] \"Caulobacterales\" \"Caulobacterales\" \"Hyphomicrobiales\" \"Hyphomicrobiales\" ... #> $ family : chr [1:86156] \"Caulobacteraceae\" \"Caulobacteraceae\" \"Bartonellaceae\" \"Brucellaceae;-_Brucella/Ochrobactrum group\" ... #> $ genus : chr [1:86156] \"Caulobacter\" \"Caulobacter;-_unclassified Caulobacter\" \"Bartonella\" \"Brucella\" ..."},{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"data-and-annotations","dir":"Articles","previous_headings":"Data > Pre-process","what":"Data and Annotations","title":"Case Study: Cheese Communities","text":"select ‘type’ column phylum describe characteristic cheese data. Also, columns phylum class taken tags elemental composition. section, introduce data preparing steps analysis. annotations dataset contains various columns describe characteristics properties samples. First, select ‘type’ column phylum provide broad categorization microbial communities present cheese surface. categorization helps understanding overall composition diversity microbes high taxonomic level. run pre_process() function clean standardize data. annotate dataset, also use columns phylum class tags elemental composition microbial communities. phylum column represents major taxonomic rank, giving us broad overview microbial distribution. class column provides detailed information, allowing us delve deeper specific types microbes present. pre-processing, two datasets put dashboard look like:","code":"cheesedata <- cheese |> select(ID, A_1:C_5, phylum) |> rename(type=phylum) |> pre_process() pathchee <- gAnnotation(annotations,\"phylum\",\"class\") # data cheesedata[112:115,] #> # A tibble: 4 × 11 #> ID A_1 A_2 A_3 A_4 A_5 C_1 C_3 C_4 C_5 type #> #> 1 112 0.943 -0.471 -0.471 -0.471 -0.471 2.36 -0.471 -0.471 -0.471 Other #> 2 113 0.786 -0.124 0.126 -0.623 -0.637 2.33 -0.667 -0.593 -0.593 Other #> 3 114 1.22 -1.09 -0.430 -0.829 -0.170 1.43 -1.30 0.455 0.715 Other #> 4 115 2.67 -0.333 -0.333 -0.333 -0.333 -0.333 -0.333 -0.333 -0.333 Other # annotation pathchee[112:115,] #> # A tibble: 4 × 9 #> ID GO_ID KEGG_ID domain Pathway taxonomic.scope order family genus #> #> 1 145 - ko:K00004 Bacte… Pseudo… Unknown NA NA NA #> 2 147 - ko:K00004 Bacte… Actino… Actinomycetes NA NA NA #> 3 148 - ko:K00004 Bacte… Bacill… Bacilli Baci… Bacil… Lysi… #> 4 149 - ko:K00004,ko:K0… Bacte… Actino… Actinomycetes NA NA NA"},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"cluster-input","dir":"Articles","previous_headings":"Data","what":"Cluster Input","title":"Case Study: Cheese Communities","text":"section, generate clusters first dataset using gClusters function. function takes cheese dataset (cheesedata) input generates clusters based specified parameters. , set number clusters 10 (ncluster = 10) specify maximum number clusters consider determining optimal number clusters (elbow.max=15).","code":"cluschee <- gClusters(cheesedata,ncluster = 10,elbow.max=15)"},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"network-input","dir":"Articles","previous_headings":"Data","what":"network input","title":"Case Study: Cheese Communities","text":"generating clusters major patterns, proceed obtain network results clusters. Taking cluster centroids nodes, prediction process edges divided individual regression tasks, cluster centroid independentally predicted expression cluster centroids, using random forests. pick top 3 related predictors cluster centroid save network output future use. achieved using gNetwork() function. gain insight network results, can visualize details using gNetwork_view() function, shown .","code":"networkchee <- gNetwork(cluschee,ntop = 3) gNetwork_view(networkchee)"},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"run-dashboard","dir":"Articles","previous_headings":"","what":"Run Dashboard","title":"Case Study: Cheese Communities","text":"clusters network results obtained, can proceed run dashboard. involves declaring annotations executing dashboard using gDashboard() function. , pass cheese dataset (cheesedata), cluster results (cluschee), network results (networkchee), specify column names types annotation identifiers.","code":"gDashboard(cheesedata, cluschee, pathchee, networkchee, id_colname = c(\"GO_ID\",\"KEGG_ID\"), id_type = c(\"GO\",\"KEGG\"))"},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"results","dir":"Articles","previous_headings":"","what":"Results","title":"Case Study: Cheese Communities","text":"example, filter Actinomycetota (Actinobacteria) functional group, see edges connecting group 10 group 3- clusters features associated Actinomycetoa Cheese sample (Fig 1.3.). Fig 1: Dashboard showing Actinomycetota filtered network () enrichment pattern Cheese Sample-(B) Cheese Sample-C (C); Cluster pattern Group 9, also enriched Type IV secretion genes (D). Looking pattern traces groups, (Fig 1.3.B), peak samples A4 (week 9) A5 (week 13), mirrors 16S rRNA results Saak et al. Since two clusters edges connecting groups, suggests Actinomycetoa features found groups follow distinct longitudinal succession patterns independent. looking Actinomycetoa within Cheese Sample C see different pattern. Groups 2 5, features associated Actinomycetoa, highly connected groups (Fig 1.3.). results, can hypothesize though Actinomycetoa features abundant later time points cheese samples, dynamics differentially influenced. authors found Type VI secretion enriched Pseudomonadota bacteria (specifically, Psychrobacter), hypothesized enrichment due importance physical species interactions occur habitat. Using MolPad, searched secretion systems associated genes, understand dynamics within community. Focusing KEGG annotated Type IV secretion genes, found Group 9 contained 12/13 genes. Within group, features cluster ones peak Cheese sample C5 (week 13, Fig 1.3.D). also taxonomically diverse sample. , can hypothesize increased taxonomic diversity also associated increases genes related competitive species interactions.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"installation","dir":"Articles","previous_headings":"","what":"Installation","title":"Get started","text":"can either install MolPad devtools, clone repository local computer (example, onto ./Github) installing: , load package:","code":"# Install the package in R: install.packages(\"devtools\") library(devtools) install_github(\"KaiyanM/MolPad\") # Download through Github and install the package in R: setwd(\"./GitHub\") install(\"MolPad\") library(MolPad)"},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"before-input","dir":"Articles","previous_headings":"","what":"Before Input","title":"Get started","text":"recommend carefully reviewing data considering applying normalization (standardization, quantile normalization, log transformation, …) imputation (KNN, PMM, Mean/Median/Mode, …) address issues related library size missing data beforehead, otherwise pre_processing() function default converting NAs 0s.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"tips-for-normalization-and-imputation","dir":"Articles","previous_headings":"Before Input","what":"Tips for normalization and imputation","title":"Get started","text":"choose normalization method, first consider data distribution. Use Z-score normalization data normally distributed, log transformation robust scaling ’s skewed. analysis requires data within specific range, like [0, 1], opt Min-Max normalization. datasets outliers, robust scaling rank normalization better choices. selecting imputation method, assess amount pattern missing data. small amount missing data, simple methods like mean, median, mode imputation effective. missing data related variables, consider sophisticated approaches like Predictive Mean Matching k-NN imputation. computational resources concern, simpler imputation methods might practical, resources allow, advanced methods can improve data quality. ’s hard decide, can always experiment different methods use cross-validation compare effectiveness. Let choice guided domain knowledge specific requirements analysis increase quality following steps.","code":""},{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"multi-omics-yes-or-no","dir":"Articles","previous_headings":"Data Format","what":"Multi-omics: Yes or No","title":"Get started","text":"visualization pipeline starts pre-processing module includes built-functions reshape data inputs standard format streamline data preparation. Depending nature datasets, two primary conditions consider: research object includes multi-omics measurements, assume provide list dataframes, collected different omics type. example, might datasets peptides, metabolites, lipids. combine together standard long dataset row represents feature, simply run pre_process() function, conduct data preparation yield standard input format demonstrated . Datasets multi-omics, can still utilize dashboard long ensuring data inputs reformatted standard longitudinal format. situation, can manually assign type column category label describe major groups data, input data pre_processing() module data preparation . case study, utilized “Kingdom” type label column cheese data. datasets multi-omics, can simply apply dashboard reformatting data standard longitudinal format. case, manually assign category label type column represent major groups. can still input data pre_processing() module cleaning preparation. case study, used “Kingdom” type label cheese datasets.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"data-format-and-pre-process","dir":"Articles","previous_headings":"Data Format","what":"Data Format and Pre-process","title":"Get started","text":"figure, , B, C can represent datasets different sources different aspects measurements. two examples input data might look like transform required format pre_process() modules.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"if-your-input-is-a-list-of-datasets","dir":"Articles","previous_headings":"Data Format > Data Format and Pre-process","what":"If your input is a list of datasets:","title":"Get started","text":"list datasets, following format: ID column identical column names. pre_process():","code":"str(fungal_dlist) #> List of 3 #> $ :'data.frame': 149786 obs. of 12 variables: #> ..$ ID : chr [1:149786] \"grp1_1\" \"grp1_2\" \"grp1_6\" \"grp1_7\" ... #> ..$ D0_01 : num [1:149786] -0.33 -0.33 1.591 1.106 0.659 ... #> ..$ D1_02 : num [1:149786] 0.628 0.628 3.087 3.618 2.843 ... #> ..$ D2_03 : num [1:149786] -1.223 -1.223 0.846 1.178 1.435 ... #> ..$ D3_04 : num [1:149786] -2.411 -1.808 1.239 1.753 0.258 ... #> ..$ D4_05 : num [1:149786] -1.41 -1.18 2.05 1.64 1.21 ... #> ..$ D5_06 : num [1:149786] -2.245 -2.309 0.83 1.64 0.395 ... #> ..$ D6_07 : num [1:149786] -0.784 -1.125 2.758 2.888 2.516 ... #> ..$ D7_08 : num [1:149786] -2.61197 -2.68724 0.42236 0.92846 0.00892 ... #> ..$ D8_09 : num [1:149786] -0.951 -1.3 2.417 2.454 1.418 ... #> ..$ D9_10 : num [1:149786] -1.91 -2.61 1.33 1.8 2.25 ... #> ..$ D10_11: num [1:149786] -2.871 -3.417 1.101 0.619 1.275 ... #> $ :'data.frame': 150 obs. of 12 variables: #> ..$ ID : chr [1:150] \"grp2_1\" \"grp2_2\" \"grp2_3\" \"grp2_4\" ... #> ..$ D0_01 : num [1:150] 0.947 1.012 0.156 0.31 0.138 ... #> ..$ D1_02 : num [1:150] -1.616 -0.917 -1.581 -2.717 -2.578 ... #> ..$ D2_03 : num [1:150] -2.4 -1.67 -2.31 -3.02 -3.66 ... #> ..$ D3_04 : num [1:150] -0.5734 0.0336 -0.351 -1.3052 -1.737 ... #> ..$ D4_05 : num [1:150] 0.662 0.854 1.002 0.12 -0.371 ... #> ..$ D5_06 : num [1:150] -1.09 -1.28 -1.04 -1.82 -3.15 ... #> ..$ D6_07 : num [1:150] -0.212 0.718 0.189 -0.627 -1.081 ... #> ..$ D7_08 : num [1:150] 0.969 2.117 1.462 0.339 -0.038 ... #> ..$ D8_09 : num [1:150] -0.712 0.43 -0.281 -1.297 -1.822 ... #> ..$ D9_10 : num [1:150] -0.649 0.45 -0.124 -1.77 -1.962 ... #> ..$ D10_11: num [1:150] -1.05 -0.85 -1.58 -1.91 -4.05 ... #> $ :'data.frame': 369 obs. of 12 variables: #> ..$ ID : chr [1:369] \"grp3_1\" \"grp3_2\" \"grp3_3\" \"grp3_4\" ... #> ..$ D0_01 : num [1:369] -0.918 -2.112 -1.372 -0.4 -1.75 ... #> ..$ D1_02 : num [1:369] -1.219 -3.502 -1.504 -0.615 -2.11 ... #> ..$ D2_03 : num [1:369] -0.542 -2.324 -0.592 0.161 -1.342 ... #> ..$ D3_04 : num [1:369] -0.101 -1.495 0.1 0.603 -1.058 ... #> ..$ D4_05 : num [1:369] 1.715 1.557 1.89 2.296 0.653 ... #> ..$ D5_06 : num [1:369] -1.452 -2.37 -1.182 -0.846 -2.531 ... #> ..$ D6_07 : num [1:369] 0.0879 -0.9982 0.4382 0.6215 -0.9464 ... #> ..$ D7_08 : num [1:369] 0.0502 0.4668 0.5378 0.8353 -0.9213 ... #> ..$ D8_09 : num [1:369] -0.0268 -0.4226 0.4528 0.6328 -1.0998 ... #> ..$ D9_10 : num [1:369] -0.5807 -1.7755 -0.1956 0.0866 -1.5876 ... #> ..$ D10_11: num [1:369] 0.118 -0.16 0.687 0.572 -1.037 ... #> ID D0_01 D1_02 D2_03 D3_04 D4_05 D5_06 #> 1 grp1_1 -0.3297741 0.6284682 -1.2232968 -2.411339 -1.413062 -2.2445487 #> 2 grp1_2 -0.3297741 0.6284682 -1.2232968 -1.807533 -1.183498 -2.3088022 #> 3 grp1_6 1.5908054 3.0871284 0.8460835 1.239411 2.054109 0.8300876 #> 4 grp1_7 1.1061795 3.6176250 1.1781012 1.753078 1.643661 1.6400102 #> D6_07 D7_08 D8_09 D9_10 D10_11 #> 1 -0.7839412 -2.6119663 -0.9511467 -1.914273 -2.8711810 #> 2 -1.1251434 -2.6872417 -1.2996614 -2.610647 -3.4165861 #> 3 2.7576288 0.4223618 2.4165602 1.333387 1.1012825 #> 4 2.8877343 0.9284640 2.4538561 1.799019 0.6187648 #> ID D0_01 D1_02 D2_03 D3_04 D4_05 D5_06 #> 1 grp2_1 0.9467431 -1.6162900 -2.400394 -0.57337845 0.6616711 -1.090914 #> 2 grp2_2 1.0120048 -0.9165994 -1.674330 0.03361378 0.8540950 -1.283964 #> 3 grp2_3 0.1561394 -1.5805490 -2.307281 -0.35095093 1.0018741 -1.035030 #> 4 grp2_4 0.3104578 -2.7173471 -3.022886 -1.30521496 0.1198075 -1.816677 #> D6_07 D7_08 D8_09 D9_10 D10_11 #> 1 -0.2116778 0.9688582 -0.7123773 -0.6489204 -1.0504845 #> 2 0.7181970 2.1174460 0.4302981 0.4504066 -0.8503481 #> 3 0.1885422 1.4619677 -0.2805926 -0.1235090 -1.5757556 #> 4 -0.6271954 0.3393109 -1.2967938 -1.7697248 -1.9073101 #> ID D0_01 D1_02 D2_03 D3_04 D4_05 D5_06 #> 1 grp3_1 -0.9178218 -1.2193128 -0.5415780 -0.1007493 1.715495 -1.4515238 #> 2 grp3_2 -2.1120636 -3.5015326 -2.3236475 -1.4949366 1.557463 -2.3698578 #> 3 grp3_3 -1.3724446 -1.5043101 -0.5922380 0.1004978 1.889573 -1.1815469 #> 4 grp3_4 -0.4002445 -0.6145252 0.1613003 0.6031744 2.295751 -0.8464012 #> D6_07 D7_08 D8_09 D9_10 D10_11 #> 1 0.08792489 0.05020749 -0.02681783 -0.58065562 0.1177374 #> 2 -0.99822332 0.46681389 -0.42255125 -1.77554842 -0.1603138 #> 3 0.43822354 0.53780990 0.45276561 -0.19558682 0.6870272 #> 4 0.62145080 0.83528878 0.63275758 0.08663959 0.5720591 data <- pre_process(fungal_dlist, typenameList = c('DataSource_A','DataSource_B','DataSource_C'), replaceNA = TRUE, scale = TRUE, autoColName = \"Section_\") #> [1] \"Reformat a list of datasets:\" head(data,10) #> ID D0_01 D1_02 D2_03 D3_04 D4_05 D5_06 #> 1 grp1_1 1.06146675 1.956656 0.2267383 -0.88312967 0.04945980 -0.7273146 #> 2 grp1_2 1.08465667 1.916983 0.3085454 -0.19892034 0.34311497 -0.6343214 #> 3 grp1_6 -0.01899274 1.717871 -0.8834318 -0.42687491 0.51878884 -0.9019992 #> 4 grp1_7 -0.75869657 2.051470 -0.6782202 -0.03485388 -0.15728499 -0.1613700 #> 5 grp1_8 -0.68132827 1.648062 0.1465161 -1.10875822 -0.09101615 -0.9629427 #> 6 grp1_11 0.27709905 1.981042 -0.5786717 0.02888008 0.54647685 -0.7013406 #> 7 grp1_12 0.90487968 2.180079 -0.6439891 -0.14841544 -0.69040101 -0.3834938 #> 8 grp1_13 0.46335031 1.695812 -1.0938459 -0.76868293 0.74415950 -0.6861036 #> 9 grp1_14 0.72014702 1.708604 -0.9344900 -0.35796963 0.63730277 -0.9872778 #> 10 grp1_15 0.54526798 1.784716 -0.8846753 -0.49538649 0.62916563 -0.9742293 #> D6_07 D7_08 D8_09 D9_10 D10_11 type #> 1 0.6371841 -1.0705560 0.4809808 -0.41877137 -1.31271417 DataSource_A #> 2 0.3938012 -0.9630327 0.2422154 -0.89650312 -1.59653914 DataSource_A #> 3 1.3354026 -1.3752687 0.9395058 -0.31779210 -0.58720856 DataSource_A #> 4 1.2347631 -0.9575502 0.7492778 0.01655139 -1.30408627 DataSource_A #> 5 1.2995756 -1.3741657 0.1285139 1.01932257 -0.02377948 DataSource_A #> 6 1.1406352 -1.6339536 0.2041455 -0.45793129 -0.80638109 DataSource_A #> 7 0.4794959 -1.6340692 0.5360766 -0.31430867 -0.28585374 DataSource_A #> 8 1.2235141 -1.2241931 0.6790383 -0.28487806 -0.74817082 DataSource_A #> 9 1.1466328 -1.4265286 0.5357111 -0.51799361 -0.52413804 DataSource_A #> 10 1.1813273 -1.2944121 0.6417621 -0.46906150 -0.66447461 DataSource_A"},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"if-your-input-is-a-single-dataset","dir":"Articles","previous_headings":"Data Format > Data Format and Pre-process","what":"If your input is a single dataset:","title":"Get started","text":"dataset already includes labeled ‘type’ column: pre_process():","code":"#> ID Day_1 Day_2 Day_3 Day_4 Day_5 Day_6 #> 1 1 NA NA -0.82919412 0.08237778 1.30438699 0.4875914 #> 2 2 NA NA -0.41606256 1.42523475 -0.44792066 1.1425154 #> 3 3 1.0354708 NA 0.78841026 -1.49585251 -0.41361867 -0.3271995 #> 4 4 -0.3005323 0.6998150 -1.86018783 1.14167433 -1.04204735 -2.4930409 #> 5 5 -0.1307903 0.7839983 0.25834857 0.62421147 -0.07202124 -0.9214203 #> 6 6 -0.9910321 1.8059465 -1.25395397 -0.45260649 -0.57303842 0.9990113 #> 7 7 -0.9160734 1.4475673 -3.10452531 -0.06560360 -1.27374744 -0.5703149 #> 8 8 -0.7381729 -0.4844302 1.10281675 0.12049188 0.20892638 -0.3115689 #> 9 9 -0.2581716 -0.5906591 0.09794986 2.44249606 1.13135657 0.4886958 #> 10 10 1.8831483 -1.2490571 0.55981559 -2.31731640 0.97323395 -0.7994677 #> Day_7 Day_8 type #> 1 -1.4632070 0.05832042 peptide #> 2 -0.4292379 -0.79047610 peptide #> 3 -0.3508094 -0.34995898 peptide #> 4 -0.3011731 0.67869535 peptide #> 5 1.1761504 -0.86303628 peptide #> 6 -0.8993647 -0.44801227 peptide #> 7 1.1593812 -0.90149427 peptide #> 8 0.5463595 -1.51687039 lipid #> 9 -0.5016034 -0.08241911 lipid #> 10 -0.9442708 NA metabolite x1 <- pre_process(x, replaceNA = TRUE,scale = TRUE) head(x1,10) #> ID Day_1 Day_2 Day_3 Day_4 Day_5 Day_6 #> 1 1 0.05451890 0.05451890 -0.9508450 0.1543986 1.6360347 0.64570353 #> 2 2 -0.07554744 -0.07554744 -0.5950354 1.7039742 -0.6348129 1.35097605 #> 3 3 1.49874204 0.17759695 1.1835204 -1.7309438 -0.3501343 -0.23987305 #> 4 4 0.10376561 0.87801613 -1.1033792 1.2200072 -0.4701534 -1.59319589 #> 5 5 -0.31530188 0.89803359 0.2008348 0.6860993 -0.2373532 -1.36395879 #> 6 6 -0.71746086 1.90776216 -0.9642373 -0.2120986 -0.3251351 1.15037885 #> 7 7 -0.27002806 1.37506283 -1.7931878 0.3218978 -0.5189688 -0.02938062 #> 8 8 -0.74469269 -0.43190500 1.5246885 0.3137802 0.4227931 -0.21881953 #> 9 9 -0.58919072 -0.91616395 -0.2389755 2.0666865 0.7772922 0.14528989 #> 10 10 1.56649401 -0.74805373 0.5886154 -1.5374455 0.8941115 -0.41582898 #> Day_7 Day_8 type #> 1 -1.7195596 0.1252300 peptide #> 2 -0.6114859 -1.0625212 peptide #> 3 -0.2699967 -0.2689116 peptide #> 4 0.1032697 0.8616699 peptide #> 5 1.4181669 -1.2865208 peptide #> 6 -0.6314225 -0.2077865 peptide #> 7 1.1744856 -0.2598810 peptide #> 8 0.8387456 -1.7045902 lipid #> 9 -0.8285852 -0.4163531 lipid #> 10 -0.5228315 0.1749388 metabolite"},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"choose-your-annotation","dir":"Articles","previous_headings":"","what":"Choose Your Annotation","title":"Get started","text":"addition specifying data type mentioned , methods support three levels information: functional annotation, taxonomy annotation, feature annotation. annotations matched ID columns annotation data, serving another crucial input generating dashboard. facilitate automatic feature link generation using KeggID GOID, users set corresponding column names beforehand. example annotation formats align data inputs. data two layers labels (like cheese dataset), can specify two layers want focus network using gAnnotation() function.","code":"#> ID taxonomic.scope ko_term Pathway #> 1 grp1_24 Bacteria K02706 00195_Photosynthesis #> 2 grp1_25 Bacteria K02706 00195_Photosynthesis #> 3 grp1_26 Bacteria K02706 00195_Photosynthesis #> 4 grp1_1075 Bacteria K02112 00195_Photosynthesis #> 5 grp1_1076 Bacteria K02112 00195_Photosynthesis #> 6 grp1_1358 Bacteria K02112 00195_Photosynthesis Annotation_path_taxon <- gAnnotation(annotations,\"phylum\",\"class\")"},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"network-generation","dir":"Articles","previous_headings":"","what":"Network Generation","title":"Get started","text":"generate network, first scale cluster trajectories across input molecular features. Trajectories clustered using K-means, built-elbow method used select optimal K. build network clustered trajectories adapting GENIE3 algorithm. Specifically, network learned series regressions, cluster centroid predicted expression patterns cluster centroids using random forests. choose random forests potential model interacting features non-linearity without strong assumptions. determine feature clusters predictive given trajectory, Mean Decrease Accuracy subset top predictors whose expression directly influences expression target cluster taken indication putative link.","code":"cluster_result <- data |> gClusters(ncluster = 5,elbow.max=15) network_result <- cluster_result |> gNetwork(ntop = 3) gNetwork_view(network_result) #> Warning: Removed 1 rows containing missing values (`geom_segment()`). #> Warning: Removed 1 rows containing missing values (`geom_point()`)."},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"dashboard-navigation","dir":"Articles","previous_headings":"","what":"Dashboard Navigation","title":"Get started","text":"workflow inputs related gDashboard function. key function processes data runs dashboard, requiring input previous steps. ’ve launched Shiny dashboard, can zoom make adjustments explore interesting findings within data. effectively navigate dashboard generated MolPad, ’ll follow three main steps:","code":"gDashboard(data, cluster_result, Annotation_path_taxon, network_result, id_colname = \"ko_term\" , id_type = \"KEGG\")"},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"choose-a-primary-functional-annotation-and-adjust-edge-density","dir":"Articles","previous_headings":"Dashboard Navigation","what":"1. Choose a Primary Functional Annotation and Adjust Edge Density","title":"Get started","text":"Start selecting primary functional annotation available options. , fine-tune edge density adjusting threshold value importance score. Nodes turn bright green indicate clusters containing features related chosen functional annotation.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"explore-the-network","dir":"Articles","previous_headings":"Dashboard Navigation","what":"2. Explore the Network","title":"Get started","text":"Brushing network unveils patterns taxonomic composition typical trajectories. can also zoom specific taxonomic annotations applying filters.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"investigate-feature-details-and-related-function-annotations","dir":"Articles","previous_headings":"Dashboard Navigation","what":"3. Investigate Feature Details and Related Function Annotations","title":"Get started","text":"Delve feature table examine specifics features within selected clusters. Explore additional related function annotations using drop-options. Click provided links access online information items interest. interface encourages iterative exploration, enabling conduct multiple steps answer specific questions, comparing pattern distributions two functions identifying functionally important community members metabolizing feature interest.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"related-visualization-outputs","dir":"Articles","previous_headings":"","what":"Related Visualization Outputs","title":"Get started","text":"following visualizations integrated dashboard, ’s need run function plot individually. ’ve linked stacked bar plot ribbon plot network facilitate focus context explorations. setup helps detecting similar longitudinal patterns understanding relationships.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"network-plot","dir":"Articles","previous_headings":"Related Visualization Outputs","what":"Network Plot","title":"Get started","text":"network plot powerful visualization tool displays relationships different groups features within data. MolPad, network plot helps identify clusters features share similar patterns, revealing underlying connections might immediately obvious. visualizing connections, users can gain clearer understanding structure within data, making easier pinpoint significant associations trends.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"stack-bar-plot","dir":"Articles","previous_headings":"Related Visualization Outputs","what":"Stack Bar Plot","title":"Get started","text":"stacked bar plot provides detailed view composition cluster group dataset. stacking different categories top within single bar, plot allows quick comparison relative proportions across multiple groups. particularly useful microbiome experiments understanding distribution taxa across different conditions time points crucial. stacked bar plot makes easy see distributions change experimental conditions, facilitating deeper insights.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"ribbon-plot","dir":"Articles","previous_headings":"Related Visualization Outputs","what":"Ribbon Plot","title":"Get started","text":"ribbon plot designed visualize changes time, making ideal tool tracking longitudinal data. MolPad, ribbon plot illustrates abundance expression levels features vary across different time points conditions. smooth, flowing design ribbons helps emphasize trends patterns, enabling users quickly identify periods significant change stability. plot particularly advantageous comparing multiple groups, clearly shows overlapping trends divergences, providing comprehensive view temporal dynamics data.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/whymolpad.html","id":"why-molpad","dir":"Articles","previous_headings":"","what":"Why MolPad?","title":"Why MolPad?","text":"increasing multi-omics data longitudinal designs integrated microbiome experiments, growing need present network, especially complex variations across biological modalities. Network perspective helps detect underlying co-occurrence among microbiome samples, allowing high-level insights global structure. Yet comes experimental data records time series 100,000 features, network collapse entangled clumps therefore unable read.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/whymolpad.html","id":"improvements","dir":"Articles","previous_headings":"","what":"Improvements","title":"Why MolPad?","text":"aim network interpretation, MolPad shows improvements 3 important aspects: Capture groups features share longitudinal patterns functional properties within network. Annotate network complex experimental datasets, way dig specific components notable features. Streamlines exploration modularized functions interactive dashboard. package especially facilitates exploration longitudinal molecular co-expression microbiome experiments using focus-plus-context approach, empowering researchers uncover global patterns insights longitudinal microbiomics data. Unlike existing tools, MolPad stands rich network interactivity support contextualization. also equipped beginner-friendly interface self-contained data processing functions, making accessible users limited programming experience.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/whymolpad.html","id":"key-features","dir":"Articles","previous_headings":"","what":"Key Features","title":"Why MolPad?","text":"Trajectory clustering organizing data clusters, helps identify patterns, relationships, underlying structures within dataset. essence, clustering detects groups features observations behave similarly, allowing deeper insights effective decision-making. Network extracting build network clustered trajectories adapting GENIE3 algorithm. Specifically, network learned series regressions, cluster centroid predicted expression patterns cluster centroids using random forests. choose random forests potential model interacting features non-linearity without strong assumptions. Interactive Dashboard package provides interactive dashboard designed help users explore data uncover relationships within complex networks. utilizing focus-plus-context visualization, dashboard allows users zoom specific details keeping broader dataset view. approach enables deeper understanding data, making easier identify patterns make informed decisions.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/whymolpad.html","id":"what-does-it-look-like","dir":"Articles","previous_headings":"","what":"What does it look like?","title":"Why MolPad?","text":"Now, let’s see can get dashboard. ’ll start overview demonstrate discover patterns within data.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/whymolpad.html","id":"molpad-dashboard-overview","dir":"Articles","previous_headings":"What does it look like?","what":"MolPad Dashboard Overview","title":"Why MolPad?","text":"overview MolPad Dashboard. explore dashboard effectively, can start following sequence -B-C-D. approach guide cluster-level network, taxonomic-level bar plot, type-level line plot, feature-level table. , can modify detailed settings identify investigate special patterns within data.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/whymolpad.html","id":"discovering-related-patterns","dir":"Articles","previous_headings":"What does it look like?","what":"Discovering related patterns","title":"Why MolPad?","text":"short example discovering related patterns using network plot: shade edges represents proximity nodes. brushed area, Groups 1-7-8 (circled solid black lines) Groups 1-2 (circled blue dashed lines) strongly linked. Groups 1, 7, 8, patterns form W-shape noticeable peak time section. Although Group 1 shows higher volatility compared Group 2, groups display highly overlapping increasing trend. detailed instructions, please visit “Get Started” page relevant function reference page.","code":""},{"path":"https://kaiyanm.github.io/MolPad/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Kaiyan Ma. Author, maintainer.","code":""},{"path":"https://kaiyanm.github.io/MolPad/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Ma K (2024). MolPad: MolPad: R-Shiny Package Cluster Co-Expression Analysis Longitudinal Microbiomics. R package version 0.1.0, https://kaiyanm.github.io/MolPad/.","code":"@Manual{, title = {MolPad: MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics}, author = {Kaiyan Ma}, year = {2024}, note = {R package version 0.1.0}, url = {https://kaiyanm.github.io/MolPad/}, }"},{"path":"https://kaiyanm.github.io/MolPad/index.html","id":"molpad-","dir":"","previous_headings":"","what":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","title":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","text":"R-Shiny Package Cluster Co-Expression Analysis Longitudinal Microbiomics","code":""},{"path":"https://kaiyanm.github.io/MolPad/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","text":"MolPad offers visualization dashboard tool designed enhance understanding molecular co-expression works context microbiome data. approach involves using cluster network provide initial overview relationships across multiple omics, added functionality interactively zoom specific areas interest. facilitate analysis, ’ve developed focus-plus-context strategy connects online curated annotations. Additionally, package simplifies entire pipeline creating dashboard. user-friendly design makes accessible even people limited R programming experience. Check cheese-data demo .","code":""},{"path":"https://kaiyanm.github.io/MolPad/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","text":"can either install devtools, {r, eval = FALSE} # Install package R: install.packages(\"devtools\") library(devtools) install_github(\"KaiyanM/MolPad\") clone repository local computer (example, onto ./Github) installing: {r, eval = FALSE} # Download install package R: setwd(\"./GitHub\") install(\"MolPad\") , load package: {r,eval=FALSE} library(MolPad)","code":""},{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/index.html","id":"molpad-could-help-you-with","dir":"","previous_headings":"Usage","what":"MolPad could help you with:","title":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","text":"Clustering data k-means building group network. Find significant trend patterns datasets. Target interaction groups, taxons, pathways. Visualize distribution features specific pathways group network. Search particular features user-defined labels. Check detailed information feature automatically generated hyperlinks. better overall understanding datasets.","code":""},{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/index.html","id":"getting-help","dir":"","previous_headings":"","what":"Getting Help","title":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","text":"need assistance MolPad, two primary ways seek help: Ask us anything related MolPad! add question, create issue repository. Stack Overflow another excellent resource answering common issues R. Remember ’s particularly effective can provide reproducible example shows specific problem ’re .","code":""},{"path":"https://kaiyanm.github.io/MolPad/index.html","id":"contribution","dir":"","previous_headings":"","what":"Contribution","title":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","text":"contribute project, use following workflow: fork repository –> create local copy –> submit pull request.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":null,"dir":"Reference","previous_headings":"","what":"Cheese data — cheese","title":"Cheese data — cheese","text":"context cheese production, regular application brine solution maturation technique promotes uniformity microbial populations cheese's surface facilitates interactions among microorganisms. investigation involved analysis longitudinal dataset encompassing three washed-rind cheese communities sampled ripening process.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Cheese data — cheese","text":"two datasets: cheese annotations","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Cheese data — cheese","text":"Reference: doi:10.1128/msystems.00701-22","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Cheese data — cheese","text":"study uncovered remarkably consistent microbial progression within cheese. bacterial community, Firmicutes dominate outset, Proteobacteria swiftly assuming dominance end ripening period. Additionally, Cheese Cheese C consistently demonstrate establishment Actinobacteria Bacteroidetes, distinct manner. corroborate findings using MolPad dashboard, conducted analysis two cheeses (C) three production batches, spanning weeks 2 13.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":"cheese-data","dir":"Reference","previous_headings":"","what":"cheese data","title":"Cheese data — cheese","text":"data frame 106239 rows 18 variables: ID sample ID A_1~A_5 Time series data measured cheese . C_1~C_5 Time series data measured cheese C. domain category feature phylum category feature class category feature order category feature family category feature genus category feature","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":"annotation-data","dir":"Reference","previous_headings":"","what":"annotation data","title":"Cheese data — cheese","text":"data frame 86156 rows 9 variables: ID sample ID GO_ID GO IDs, represents link gene product type molecular function KEGG_ID KEGG IDs, linking genomic information higher order functional information. domain category feature phylum category feature class category feature order category feature family category feature genus category feature","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cheese data — cheese","text":"","code":"data(cheese) head(annotations) #> # A tibble: 6 × 9 #> ID GO_ID KEGG_ID domain phylum class order family genus #> #> 1 9 - - Bacteria Pseudomonadota Alphaproteobac… Caul… Caulo… Caul… #> 2 10 - - Bacteria Pseudomonadota Alphaproteobac… Caul… Caulo… Caul… #> 3 11 - - Bacteria Pseudomonadota Alphaproteobac… Hyph… Barto… Bart… #> 4 12 - - Bacteria Pseudomonadota Alphaproteobac… Hyph… Bruce… Bruc… #> 5 13 - - Bacteria Pseudomonadota Alphaproteobac… Hyph… Nitro… Brad… #> 6 14 - - Bacteria Pseudomonadota Alphaproteobac… Hyph… Phyll… Meso… head(cheese) #> # A tibble: 6 × 18 #> ID A_1 A_2 A_3 A_4 A_5 C_1 C_3 C_4 C_5 GO_ID KEGG_ID #> #> 1 1 38 23 27 5 11 13 1 3 0 NA NA #> 2 2 23 6 4 0 9 21 1 0 2 NA NA #> 3 3 24 5 37 10 19 3 0 1 4 NA NA #> 4 4 3 2 4 2 16 56 1 7 17 NA NA #> 5 5 58 14 45 13 32 82 3 8 3 NA NA #> 6 6 12 9 14 4 13 2 0 1 4 NA NA #> # ℹ 6 more variables: domain , phylum , class , order , #> # family , genus "},{"path":"https://kaiyanm.github.io/MolPad/reference/color_palettes__.html","id":null,"dir":"Reference","previous_headings":"","what":"Color palettes — color_palettes__","title":"Color palettes — color_palettes__","text":"internal function built-color palettes.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/color_palettes__.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Color palettes — color_palettes__","text":"","code":"color_palettes__(name)"},{"path":"https://kaiyanm.github.io/MolPad/reference/color_palettes__.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Color palettes — color_palettes__","text":"name string two options: \"graytone\" \"darkwarm\".","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/color_palettes__.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Color palettes — color_palettes__","text":"","code":"color_palettes__(\"graytone\") #> [1] \"#c3c5c7\" \"#30beba\" \"#998a73\" \"#807566\" \"#a5b1c9\" \"#5d5232\" \"#9a2b41\" #> [8] \"#d6b7a2\" \"#882db4\" \"#b47a53\" color_palettes__(\"darkwarm\") #> [1] \"#251305\" \"#C70A80\" \"#FBCB0A\" \"#ff1122\" \"#7D7463\" \"#CECE5A\" \"#FF9B9B\" #> [8] \"#A459D1\" \"#00235B\" \"#7E1717\""},{"path":"https://kaiyanm.github.io/MolPad/reference/convert_range.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert range — convert_range","title":"Convert range — convert_range","text":"internal function range conversion. element x, returns distance minimal values divided range.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/convert_range.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert range — convert_range","text":"","code":"convert_range(x)"},{"path":"https://kaiyanm.github.io/MolPad/reference/convert_range.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert range — convert_range","text":"x vector numbers.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/convert_range.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert range — convert_range","text":"vector calculated (x - min(x)) / (max(x) - min(x))","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/convert_range.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert range — convert_range","text":"","code":"convert_range(5:10) #> [1] 0.0 0.2 0.4 0.6 0.8 1.0"},{"path":"https://kaiyanm.github.io/MolPad/reference/extend.color__.html","id":null,"dir":"Reference","previous_headings":"","what":"Extend color palettes — extend.color__","title":"Extend color palettes — extend.color__","text":"internal function designed pair vectors color palettes automatically generate colors longer vectors find match.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/extend.color__.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extend color palettes — extend.color__","text":"","code":"extend.color__(n, colors, extendby = 1, alpha = 1, ...)"},{"path":"https://kaiyanm.github.io/MolPad/reference/extend.color__.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extend color palettes — extend.color__","text":"n number. length feature vector want match colors. colors vector colors (finite). extendby number select 1, 2, 3, 4 5, representing distinct auto-fill schemes. alpha number select range 0,1.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gAnnotation.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate processed annotation — gAnnotation","title":"Generate processed annotation — gAnnotation","text":"gAnnotation() provides standard input format dashboard, allowing users select two columns primary factors wish visualize describe data.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gAnnotation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate processed annotation — gAnnotation","text":"","code":"gAnnotation(data, first_order, second_order)"},{"path":"https://kaiyanm.github.io/MolPad/reference/gAnnotation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate processed annotation — gAnnotation","text":"data data.frame containing annotations used describing features measured time point. also required include ID least two categorical variables. first_order string. name one column categorical variable data. second_order string. name another column categorical variable data.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gAnnotation.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate processed annotation — gAnnotation","text":"Guidelines Selecting Annotations: first-order annotation recommended functional, pathway functional system. parameter primarily serve purpose filtering one network time displaying dashboard. second-order annotation utilized illustrate composition first-order annotation using bar plot. Therefore, better set taxon, class label, etc.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gAnnotation.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate processed annotation — gAnnotation","text":"","code":"data(test_data) test_annotations_processed <- gAnnotation(test_annotations,system,class) head(test_annotations_processed) #> ID #> 1 1 #> 2 2 #> 3 3 #> 4 4 #> 5 5 #> 6 6 #> GO_ID #> 1 #> 2 #> 3 GO:0003674,GO:0003824,GO:0005575,GO:0006629,GO:0006644,GO:0006650,GO:0006655,GO:0006793,GO:0006796,GO:0008150,GO:0008152,GO:0008610,GO:0008654,GO:0008808,GO:0009058,GO:0009987,GO:0016020,GO:0016740,GO:0016772,GO:0016780,GO:0019637,GO:0030572,GO:0032048,GO:0032049,GO:0044237,GO:0044238,GO:0044249,GO:0044255,GO:0045017,GO:0046471,GO:0046474,GO:0046486,GO:0071704,GO:0090407,GO:1901576 #> 4 GO:0003674,GO:0003676,GO:0003723,GO:0003729,GO:0003824,GO:0004654,GO:0005488,GO:0005575,GO:0005622,GO:0005623,GO:0005737,GO:0006139,GO:0006401,GO:0006402,GO:0006725,GO:0006807,GO:0008150,GO:0008152,GO:0009056,GO:0009057,GO:0009892,GO:0009987,GO:0010468,GO:0010605,GO:0010629,GO:0016070,GO:0016071,GO:0016740,GO:0016772,GO:0016779,GO:0019222,GO:0019439,GO:0034641,GO:0034655,GO:0043170,GO:0044237,GO:0044238,GO:0044248,GO:0044260,GO:0044265,GO:0044270,GO:0044424,GO:0044464,GO:0046483,GO:0046700,GO:0048519,GO:0050789,GO:0060255,GO:0065007,GO:0071704,GO:0090304,GO:0097159,GO:1901360,GO:1901361,GO:1901363,GO:1901575 #> 5 #> 6 #> KEGG_ID Pathway taxonomic.scope #> 1 K07124 Integumentary System hormonal proteins #> 2 Skeletal System structural proteins #> 3 K06131 Muscular System enzymes #> 4 K00962 Nervous System contractile proteins #> 5 K02083 Endocrine System contractile proteins #> 6 Cardiovascular System structural proteins"},{"path":"https://kaiyanm.github.io/MolPad/reference/gClusters.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate clusters — gClusters","title":"Generate clusters — gClusters","text":"gClusters() returns clusters generated k-means yield elbow plot way finding optimal parameter.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gClusters.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate clusters — gClusters","text":"","code":"gClusters(data, ncluster = 20, elbow.max = 50, ...)"},{"path":"https://kaiyanm.github.io/MolPad/reference/gClusters.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate clusters — gClusters","text":"data scaled data.frame contain variables ID, value time_1, ..., value time_k, type extracting patterns across time. See also pre_process(). ncluster number clusters. related complexity information network: choosing ncluster, suggest thinking many nodes show visualization representative want clustered pattern. elbow.max number maximum value x-axis elbow method plot. larger expected ncluster smaller sample size. iter.max number maximum iterations allowed k-means. See also stats::kmeans. nstart number random attempts generating initial configurations. k-means algorithm choose best one among attempts. larger data, 'nstart' can set lower just set 1. See also stats::kmeans.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gClusters.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate clusters — gClusters","text":"function return list 2 elements: k-means cluster result elbow method plot.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gClusters.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate clusters — gClusters","text":"determine optimal number clusters (ncluster), advised closely examine elbow plot identify point graph substantial change 'elbow' occurs. often indicative suitable cluster count. cases dataset extensive intricate, might consider increasing value elbow.max ensure comprehensive exploration potential cluster counts. can help achieving accurate meaningful results, especially working larger complex datasets. function can executed data parameter outset. However, achieve best clustering results, adjustments recommended. initial run, users expected adjust function's parameters based clustering outcomes elbow plot analysis.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gClusters.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate clusters — gClusters","text":"","code":"data(test_data) reslist <- gClusters(test_data_processed) # k-means result reslist[[1]] #> K-means clustering with 20 clusters of sizes 6, 3, 6, 5, 5, 8, 6, 5, 5, 6, 4, 11, 3, 3, 3, 2, 4, 2, 2, 11 #> #> Cluster means: #> T1 T2 T3 T4 T5 T6 #> 1 2.0919348 -0.33320285 -0.50786563 -0.5579659 -0.5579659 1.5455977 #> 2 0.8277105 1.32036794 -0.09261332 1.2325986 0.4199176 -0.9576634 #> 3 0.5077879 -0.24564283 0.53503708 0.2363183 1.0793829 1.3583848 #> 4 0.2914366 -0.41308350 -0.76541563 -0.9724353 -0.9724353 1.6040887 #> 5 1.2081037 -0.66790409 -0.66790409 -0.9275526 -0.4204221 1.2719890 #> 6 0.1155971 -0.19000668 -0.25629794 -0.1487697 -0.4383348 2.5830148 #> 7 1.2574004 -0.64914997 -0.87502695 0.9353966 0.4760273 1.2681050 #> 8 0.9389212 -0.86278238 0.43887930 1.0009533 1.3733646 -1.0423264 #> 9 -0.8135539 -0.84696982 -0.32573017 1.1218534 2.0955812 -0.5620731 #> 10 -0.3802219 -0.36840199 0.02254271 1.6459653 1.9633618 -0.3246455 #> 11 -0.1225506 -0.44139873 -0.19548572 1.0299038 2.3840629 -0.2147528 #> 12 2.5784883 -0.02446156 -0.60355041 -0.2547486 -0.5120230 -0.3947043 #> 13 0.9922755 0.32399254 -0.94565021 -0.7125240 -0.5104935 -0.9456502 #> 14 0.2699801 -0.08094677 -0.28926312 -0.7813127 -0.6065051 -0.1494523 #> 15 1.7947637 -0.47746004 -0.52783931 -0.7635958 -0.7635958 -0.6438957 #> 16 0.1789247 1.26157166 0.82115649 0.3899368 1.2707671 -0.2614905 #> 17 1.4517382 0.26236463 1.31600581 -0.2531012 0.2004547 -1.1238942 #> 18 -0.2784769 -0.82258408 -0.82258408 -0.8225841 1.2338761 -0.8225841 #> 19 2.1235865 1.05868538 -0.39467611 0.1315587 -0.1315587 0.3946761 #> 20 -0.2262821 -0.73107613 -0.66098092 -0.4323193 -0.4035074 -0.3836848 #> T7 T8 T9 T10 #> 1 -0.425513174 -0.557965933 -0.30571480 -0.391338269 #> 2 -1.160107459 -0.295057401 -0.64757654 -0.647576540 #> 3 -1.006282312 -0.766188425 -0.64803067 -1.050766722 #> 4 0.301678216 -0.371684852 0.66435231 0.633498705 #> 5 1.209152596 -0.095960244 -0.32347149 -0.586030729 #> 6 -0.006327004 -0.327677972 -0.66559889 -0.665598891 #> 7 -0.354972016 -0.601904415 -0.82778139 -0.628094550 #> 8 -1.159101258 -0.345888528 -0.27147562 -0.070544200 #> 9 -0.651292750 -0.498839228 0.42446013 0.056564270 #> 10 -0.647007074 -0.667438225 -0.63229313 -0.611861976 #> 11 -0.400726188 -0.616324716 -0.71136401 -0.711364008 #> 12 -0.526022073 -0.185303954 -0.06989626 -0.007778173 #> 13 -0.945650212 0.121962031 1.19430596 1.427432164 #> 14 -0.763902351 -0.412975440 0.39220455 2.422173070 #> 15 -0.260645101 0.389799714 -0.08327821 1.335746686 #> 16 -0.912917717 -0.481698036 -1.13312530 -1.133125299 #> 17 -0.987867401 -0.340013670 -0.33085684 -0.194830041 #> 18 -0.822584085 1.233876127 1.23387613 0.689768939 #> 19 -0.795567972 -0.795567972 -0.79556797 -0.795567972 #> 20 -0.612869917 -0.006483243 1.70560793 1.751595921 #> #> Clustering vector: #> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 #> 20 1 18 16 3 20 5 19 10 9 8 14 15 12 2 9 6 20 17 12 #> 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 #> 7 20 6 1 12 9 7 12 6 11 5 11 16 18 10 3 8 13 20 2 #> 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 #> 6 1 20 20 7 20 6 3 1 6 15 5 4 13 6 5 4 10 12 10 #> 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 #> 8 3 7 13 3 1 12 7 17 17 14 9 6 19 14 10 2 11 8 7 #> 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 #> 8 20 15 4 4 1 17 12 4 12 10 5 11 12 20 3 9 12 20 12 #> #> Within cluster sum of squares by cluster: #> [1] 3.0260293 5.0357352 13.2343206 13.1118010 10.3695240 8.0412334 #> [7] 11.7251902 8.3255313 3.4617088 2.4693324 1.6353685 13.0645655 #> [13] 2.9416715 3.1842269 5.0413438 2.4669004 9.9418120 0.9921913 #> [19] 0.9833964 12.4042363 #> (between_SS / total_SS = 83.1 %) #> #> Available components: #> #> [1] \"cluster\" \"centers\" \"totss\" \"withinss\" \"tot.withinss\" #> [6] \"betweenss\" \"size\" \"iter\" \"ifault\" # elbow plot reslist[[2]]"},{"path":"https://kaiyanm.github.io/MolPad/reference/gDashboard.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate shiny dashboard — gDashboard","title":"Generate shiny dashboard — gDashboard","text":"outputs g-functions, ready create custom Molpad dashboard. sure specify web ID columns corresponding column names.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gDashboard.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate shiny dashboard — gDashboard","text":"","code":"gDashboard( data, cluster, annotation, networkres, dashboardtitle = \"MolPad Dashboard\", id_colname = NULL, id_type = NULL )"},{"path":"https://kaiyanm.github.io/MolPad/reference/gDashboard.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate shiny dashboard — gDashboard","text":"data output pre_process():scaled data.frame contain variables ID, value time_1, ..., value time_k, type. cluster output gClusters(): list contains result k-means Cluster means, vectors sum squares. annotation output gAnnotation(): data.frame containing annotations describing features. Variables must include ID, Pathway, taxonomic.scope. Note NA permitted 3 variables. networkres output gNetwork(): data.frame 4 variables weight,IncNodePurity,var_names,. dashboardtitle string customized dashboard name. id_colname single string string vector. column names annotation dataset vector contains external database IDs. id_type single string string vector. corresponding database names columns, must chosen \"KEGG\" \"GO\".","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gDashboard.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate shiny dashboard — gDashboard","text":"Please ensure columns containing external database IDs adhere following standards: KEGG ID: Begin 'K' followed 5 digits, example, K05685 K06671. GO ID: Begin 'GO:' followed 7 digits, GO:0003674.\"","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gDashboard.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate shiny dashboard — gDashboard","text":"","code":"if (FALSE) { data(test_data) gDashboard(test_data_processed,test_cluster,test_annotations_processed,test_network,id_colname = c(\"GO_ID\",\"KEGG_ID\"),id_type = c(\"GO\",\"KEGG\")) }"},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate prediction network — gNetwork","title":"Generate prediction network — gNetwork","text":"gNetwork() generates prediction network functional annotation. every feature, features considered independent variables, top predictors selected based %IncMSE.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate prediction network — gNetwork","text":"","code":"gNetwork(clusters, ntop = 10, method = \"randomforest\")"},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate prediction network — gNetwork","text":"clusters list two outputs gClusters(). first element k-means result (See also stats::kmeans), element plot automatically omitted ease directly passing results function. ntop number pick set top n predictors(default: top 10) feature important ones. used construct network among clustered patterns. seed number random seed.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate prediction network — gNetwork","text":"assess relationships clusters using Mean Squared Error (MSE) changes resulting random shuffling. gNetwork()'s output includes edge weights node pairs, essential inputs dashboard.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate prediction network — gNetwork","text":"","code":"data(test_data) networkres <- gNetwork(test_cluster, ntop = 3) head(networkres) #> weight IncNodePurity var_names from #> 1 1.4215243 1.1776012 Group_5 Group_1 #> 2 0.7911266 0.7011049 Group_3 Group_1 #> 3 -1.9891284 0.5811186 Group_2 Group_1 #> 4 5.8098014 1.1107592 Group_4 Group_2 #> 5 5.4000389 0.8744358 Group_5 Group_2 #> 6 1.2492288 1.1816976 Group_1 Group_2 gNetwork_view(networkres)"},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork_view.html","id":null,"dir":"Reference","previous_headings":"","what":"View the feature importance plot — gNetwork_view","title":"View the feature importance plot — gNetwork_view","text":"gNetwork_view() Generate feature importance plot random forest result. plot visually highlights importance individual cluster within dataset, helping identify key factors predictive modeling.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork_view.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"View the feature importance plot — gNetwork_view","text":"","code":"gNetwork_view(data)"},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork_view.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"View the feature importance plot — gNetwork_view","text":"data dataframe; output gNetwork(), including 4 variables: weight, IncNodePurity, var_names . weight edge value, IncNodePurity(%IncMSE) measure predictions result var_names permuted. dependent variable round random forest.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork_view.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"View the feature importance plot — gNetwork_view","text":"","code":"data(test_data) gNetwork_view(test_network) #> Warning: Removed 1 rows containing missing values (`geom_segment()`). #> Warning: Removed 1 rows containing missing values (`geom_point()`)."},{"path":"https://kaiyanm.github.io/MolPad/reference/make_line_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Make line plot — make_line_plot","title":"Make line plot — make_line_plot","text":"Generate line ribbon plot dashboard.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_line_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make line plot — make_line_plot","text":"","code":"make_line_plot(dfgroup_long, selected_groups, selected_taxa)"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_line_plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Make line plot — make_line_plot","text":"function makes ribbon plot every brush action Shiny show min, max, mean value clustered pattern group across time. ribbon plot grouped colored type variable input datasets. See also ggplot2::geom_ribbon.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_line_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Make line plot — make_line_plot","text":"","code":"data(test_data) make_line_plot(test_maindata, \"Group_5\", c(\"hormonal proteins\",\"structural proteins\",\"enzymes\",\"storage proteins\",\"antibodies\",\"transport proteins\"))"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_stackbar_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Make stackbar plot — make_stackbar_plot","title":"Make stackbar plot — make_stackbar_plot","text":"Generate stackbar plot dashboard.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_stackbar_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make stackbar plot — make_stackbar_plot","text":"","code":"make_stackbar_plot(dfgroup_long, selected_groups, selected_taxa)"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_stackbar_plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Make stackbar plot — make_stackbar_plot","text":"function makes bar plot every brush action Shiny show components clustered pattern group. bar plot colored taxonomic.scope variable processed annotation dataset generated gAnnotation(). See also ggplot2::geom_bar.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_stackbar_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Make stackbar plot — make_stackbar_plot","text":"","code":"data(test_data) make_stackbar_plot(test_maindata, \"Group_5\", c(\"hormonal proteins\",\"structural proteins\",\"enzymes\",\"storage proteins\",\"antibodies\",\"transport proteins\"))"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_graph.html","id":null,"dir":"Reference","previous_headings":"","what":"Make graph plot — make_the_graph","title":"Make graph plot — make_the_graph","text":"Generate graph dashboard.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_graph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make graph plot — make_the_graph","text":"","code":"make_the_graph(ptw, network_output, min_weight, s_ptw, graph_layout)"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_graph.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Make graph plot — make_the_graph","text":"function makes network plot every Pathway selection Shiny show relationship among clustered pattern groups. network plot built dataset generated gNetwork() can adjusted layout minimum weights. See also ggraph::ggraph.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_graph.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Make graph plot — make_the_graph","text":"","code":"data(test_data) make_the_graph(test_graphptw, test_network, 0.03, \"Muscular System\",\"kk\")"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_table.html","id":null,"dir":"Reference","previous_headings":"","what":"Make table from brushed region — make_the_table","title":"Make table from brushed region — make_the_table","text":"Generate table selected groups dashboard.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make table from brushed region — make_the_table","text":"","code":"make_the_table(p)"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Make table from brushed region — make_the_table","text":"p ggplot output, see also ggplot2::ggplot_build.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_table.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Make table from brushed region — make_the_table","text":"function simply aims collect position information brushed area network plot returns annotation table corresponding features. takes plot object produce object information table.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/mass_produce_lm__.html","id":null,"dir":"Reference","previous_headings":"","what":"Mass produce linear model — mass_produce_lm__","title":"Mass produce linear model — mass_produce_lm__","text":"Take column dependent vairable time, produce n linear models n columns dataset.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/mass_produce_lm__.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mass produce linear model — mass_produce_lm__","text":"","code":"mass_produce_lm__(data)"},{"path":"https://kaiyanm.github.io/MolPad/reference/mass_produce_lm__.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mass produce linear model — mass_produce_lm__","text":"data dataframe.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/mass_produce_lm__.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Mass produce linear model — mass_produce_lm__","text":"internal function designed automatically generate list functions regression. column considered response variable columns. resulting functions can utilized inputs random forest regression gNetwork().","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/match.color__.html","id":null,"dir":"Reference","previous_headings":"","what":"Match color — match.color__","title":"Match color — match.color__","text":"Match vector finite list colors.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/match.color__.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Match color — match.color__","text":"","code":"match.color__(valist, mycolors, extendby = 5)"},{"path":"https://kaiyanm.github.io/MolPad/reference/match.color__.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Match color — match.color__","text":"valist vector want specify colors element. mycolors vector colors. extendby number select 1, 2, 3, 4, 5, representing distinct auto-fill schemes.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/match.color__.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Match color — match.color__","text":"","code":"my_vector <- paste0(\"N_\",1:10) match.color__(my_vector,c(\"red\",\"yellow\",\"blue\")) #> N_1 N_2 N_3 N_4 N_5 #> \"red\" \"yellow\" \"blue\" \"white\" \"antiquewhite3\" #> N_6 N_7 N_8 N_9 N_10 #> \"aquamarine3\" \"azure3\" \"bisque2\" \"blue\" \"blueviolet\""},{"path":"https://kaiyanm.github.io/MolPad/reference/match_database.html","id":null,"dir":"Reference","previous_headings":"","what":"Match database — match_database","title":"Match database — match_database","text":"matches selected columns online database names.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/match_database.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Match database — match_database","text":"","code":"match_database(data, id_colname, id_type)"},{"path":"https://kaiyanm.github.io/MolPad/reference/match_database.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Match database — match_database","text":"match_database() internal function matches selected columns dataset corresponding databases. Currently MolPad support two types annotation database: GO KEGG. want custom URL platform, download modify paste_URL() function.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/match_database.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Match database — match_database","text":"","code":"data(test_data) head(test_annotations_processed) #> ID #> 1 1 #> 2 2 #> 3 3 #> 4 4 #> 5 5 #> 6 6 #> GO_ID #> 1 #> 2 #> 3 GO:0003674,GO:0003824,GO:0005575,GO:0006629,GO:0006644,GO:0006650,GO:0006655,GO:0006793,GO:0006796,GO:0008150,GO:0008152,GO:0008610,GO:0008654,GO:0008808,GO:0009058,GO:0009987,GO:0016020,GO:0016740,GO:0016772,GO:0016780,GO:0019637,GO:0030572,GO:0032048,GO:0032049,GO:0044237,GO:0044238,GO:0044249,GO:0044255,GO:0045017,GO:0046471,GO:0046474,GO:0046486,GO:0071704,GO:0090407,GO:1901576 #> 4 GO:0003674,GO:0003676,GO:0003723,GO:0003729,GO:0003824,GO:0004654,GO:0005488,GO:0005575,GO:0005622,GO:0005623,GO:0005737,GO:0006139,GO:0006401,GO:0006402,GO:0006725,GO:0006807,GO:0008150,GO:0008152,GO:0009056,GO:0009057,GO:0009892,GO:0009987,GO:0010468,GO:0010605,GO:0010629,GO:0016070,GO:0016071,GO:0016740,GO:0016772,GO:0016779,GO:0019222,GO:0019439,GO:0034641,GO:0034655,GO:0043170,GO:0044237,GO:0044238,GO:0044248,GO:0044260,GO:0044265,GO:0044270,GO:0044424,GO:0044464,GO:0046483,GO:0046700,GO:0048519,GO:0050789,GO:0060255,GO:0065007,GO:0071704,GO:0090304,GO:0097159,GO:1901360,GO:1901361,GO:1901363,GO:1901575 #> 5 #> 6 #> KEGG_ID Pathway taxonomic.scope #> 1 K07124 Integumentary System hormonal proteins #> 2 Skeletal System structural proteins #> 3 K06131 Muscular System enzymes #> 4 K00962 Nervous System contractile proteins #> 5 K02083 Endocrine System contractile proteins #> 6 Cardiovascular System structural proteins head(match_database(test_annotations_processed,id_colname = c(\"GO_ID\",\"KEGG_ID\"),id_type = c(\"GO\",\"KEGG\"))) #> ID #> 1 1 #> 2 2 #> 3 3 #> 4 4 #> 5 5 #> 6 6 #> GO_ID #> 1 #> 2 #> 3 GO:0003674<\/a>
GO:0003824<\/a>
GO:0005575<\/a>
GO:0006629<\/a>
GO:0006644<\/a>
GO:0006650<\/a>
GO:0006655<\/a>
GO:0006793<\/a>
GO:0006796<\/a>
GO:0008150<\/a>
GO:0008152<\/a>
GO:0008610<\/a>
GO:0008654<\/a>
GO:0008808<\/a>
GO:0009058<\/a>
GO:0009987<\/a>
GO:0016020<\/a>
GO:0016740<\/a>
GO:0016772<\/a>
GO:0016780<\/a>
GO:0019637<\/a>
GO:0030572<\/a>
GO:0032048<\/a>
GO:0032049<\/a>
GO:0044237<\/a>
GO:0044238<\/a>
GO:0044249<\/a>
GO:0044255<\/a>
GO:0045017<\/a>
GO:0046471<\/a>
GO:0046474<\/a>
GO:0046486<\/a>
GO:0071704<\/a>
GO:0090407<\/a>
GO:1901576<\/a> #> 4 GO:0003674<\/a>
GO:0003676<\/a>
GO:0003723<\/a>
GO:0003729<\/a>
GO:0003824<\/a>
GO:0004654<\/a>
GO:0005488<\/a>
GO:0005575<\/a>
GO:0005622<\/a>
GO:0005623<\/a>
GO:0005737<\/a>
GO:0006139<\/a>
GO:0006401<\/a>
GO:0006402<\/a>
GO:0006725<\/a>
GO:0006807<\/a>
GO:0008150<\/a>
GO:0008152<\/a>
GO:0009056<\/a>
GO:0009057<\/a>
GO:0009892<\/a>
GO:0009987<\/a>
GO:0010468<\/a>
GO:0010605<\/a>
GO:0010629<\/a>
GO:0016070<\/a>
GO:0016071<\/a>
GO:0016740<\/a>
GO:0016772<\/a>
GO:0016779<\/a>
GO:0019222<\/a>
GO:0019439<\/a>
GO:0034641<\/a>
GO:0034655<\/a>
GO:0043170<\/a>
GO:0044237<\/a>
GO:0044238<\/a>
GO:0044248<\/a>
GO:0044260<\/a>
GO:0044265<\/a>
GO:0044270<\/a>
GO:0044424<\/a>
GO:0044464<\/a>
GO:0046483<\/a>
GO:0046700<\/a>
GO:0048519<\/a>
GO:0050789<\/a>
GO:0060255<\/a>
GO:0065007<\/a>
GO:0071704<\/a>
GO:0090304<\/a>
GO:0097159<\/a>
GO:1901360<\/a>
GO:1901361<\/a>
GO:1901363<\/a>
GO:1901575<\/a> #> 5 #> 6 #> KEGG_ID #> 1 K07124<\/a> #> 2 #> 3 K06131<\/a> #> 4 K00962<\/a> #> 5 K02083<\/a> #> 6 #> Pathway taxonomic.scope #> 1 Integumentary System hormonal proteins #> 2 Skeletal System structural proteins #> 3 Muscular System enzymes #> 4 Nervous System contractile proteins #> 5 Endocrine System contractile proteins #> 6 Cardiovascular System structural proteins"},{"path":"https://kaiyanm.github.io/MolPad/reference/paste_URL.html","id":null,"dir":"Reference","previous_headings":"","what":"Paste URL — paste_URL","title":"Paste URL — paste_URL","text":"Retrieve database IDs associate respective URLs.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/paste_URL.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Paste URL — paste_URL","text":"","code":"paste_URL(x, id_type)"},{"path":"https://kaiyanm.github.io/MolPad/reference/paste_URL.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Paste URL — paste_URL","text":"","code":"data(test_data) paste_URL(test_annotations$GO_ID[1:4], id_type = \"GO\") #> [1] NA #> [2] NA #> [3] \"GO:0003674<\/a>
GO:0003824<\/a>
GO:0005575<\/a>
GO:0006629<\/a>
GO:0006644<\/a>
GO:0006650<\/a>
GO:0006655<\/a>
GO:0006793<\/a>
GO:0006796<\/a>
GO:0008150<\/a>
GO:0008152<\/a>
GO:0008610<\/a>
GO:0008654<\/a>
GO:0008808<\/a>
GO:0009058<\/a>
GO:0009987<\/a>
GO:0016020<\/a>
GO:0016740<\/a>
GO:0016772<\/a>
GO:0016780<\/a>
GO:0019637<\/a>
GO:0030572<\/a>
GO:0032048<\/a>
GO:0032049<\/a>
GO:0044237<\/a>
GO:0044238<\/a>
GO:0044249<\/a>
GO:0044255<\/a>
GO:0045017<\/a>
GO:0046471<\/a>
GO:0046474<\/a>
GO:0046486<\/a>
GO:0071704<\/a>
GO:0090407<\/a>
GO:1901576<\/a>\" #> [4] \"GO:0003674<\/a>
GO:0003676<\/a>
GO:0003723<\/a>
GO:0003729<\/a>
GO:0003824<\/a>
GO:0004654<\/a>
GO:0005488<\/a>
GO:0005575<\/a>
GO:0005622<\/a>
GO:0005623<\/a>
GO:0005737<\/a>
GO:0006139<\/a>
GO:0006401<\/a>
GO:0006402<\/a>
GO:0006725<\/a>
GO:0006807<\/a>
GO:0008150<\/a>
GO:0008152<\/a>
GO:0009056<\/a>
GO:0009057<\/a>
GO:0009892<\/a>
GO:0009987<\/a>
GO:0010468<\/a>
GO:0010605<\/a>
GO:0010629<\/a>
GO:0016070<\/a>
GO:0016071<\/a>
GO:0016740<\/a>
GO:0016772<\/a>
GO:0016779<\/a>
GO:0019222<\/a>
GO:0019439<\/a>
GO:0034641<\/a>
GO:0034655<\/a>
GO:0043170<\/a>
GO:0044237<\/a>
GO:0044238<\/a>
GO:0044248<\/a>
GO:0044260<\/a>
GO:0044265<\/a>
GO:0044270<\/a>
GO:0044424<\/a>
GO:0044464<\/a>
GO:0046483<\/a>
GO:0046700<\/a>
GO:0048519<\/a>
GO:0050789<\/a>
GO:0060255<\/a>
GO:0065007<\/a>
GO:0071704<\/a>
GO:0090304<\/a>
GO:0097159<\/a>
GO:1901360<\/a>
GO:1901361<\/a>
GO:1901363<\/a>
GO:1901575<\/a>\""},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":null,"dir":"Reference","previous_headings":"","what":"Pre-processing datasets — pre_process","title":"Pre-processing datasets — pre_process","text":"pre_process() function aids processing data inputs automatically establishes standardized format future use. allows two types data input: list datasets different sources long dataset containing specified last column type.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pre-processing datasets — pre_process","text":"","code":"pre_process( data, typenameList = NULL, replaceNA = TRUE, scale = TRUE, autoColName = \"Sec_\" )"},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pre-processing datasets — pre_process","text":"data data.frame describe feature one row. data contain variables ID value time_1, ..., value time_k, type extracting patterns across time. Note initial last column must exactly ID type. multiple data.frame format needs analyzed, also put list data.frame argument. case, variable type required generated next argument typenameList. typenameList vector strings. parameter used clarify source names data.frame, applicable input data list data.frame. default, set \"Dataset_1\", \"Dataset_2\", ..., etc. scale Logical; scale TRUE (default), standardize data.frame row base::scale. converts original value z-score. See also scale_by_row__(). autoColName string; autoColName -NULL (default), automatically set uniform column names data.frames. parameter applicable input data list data.frame. replaceNa Logical; replaceNa TRUE (default), replace NA 0.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pre-processing datasets — pre_process","text":"function returns long data.frame columns ID, value time_1, ..., value time_k, type.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Pre-processing datasets — pre_process","text":"consider two distinct scenarios application: one scenario, individuals collect several datasets various aspects instruments objects. example, might separately detecting lipids, metabolites, peptides specific soil sample. scenario, data uniform quality, can categorized larger groups exhibit significant differences. cases, pre_process() function serves valuable versatile tool. Yet, function optional generating dashboard. Users can perform processing long format matches required output. However, mindful number samples (timepoints) must greater 5 avoid potential errors subsequent prediction section.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pre-processing datasets — pre_process","text":"","code":"data(test_data) head(test_data, 10) #> ID T1 T2 T3 T4 T5 T6 T7 T8 T9 T10 type #> 1 1 1 0 0 1 1 0 0 1 6 6 type_A #> 2 2 6 0 0 0 0 3 1 0 2 1 type_A #> 3 3 1 0 0 0 2 0 0 2 2 1 type_A #> 4 4 4 5 3 3 7 2 1 1 0 0 type_A #> 5 5 4 3 NA 2 5 5 0 0 0 0 type_A #> 6 6 4 1 0 1 3 1 3 5 11 14 type_A #> 7 7 1 0 0 0 1 3 3 1 1 1 type_A #> 8 8 4 2 1 1 1 1 0 0 0 0 type_A #> 9 9 1 1 1 19 22 1 2 1 1 2 type_A #> 10 10 1 1 3 5 8 2 2 2 5 2 type_A a <- pre_process(test_data) head(a, 10) #> ID T1 T2 T3 T4 T5 T6 #> 1 1 -0.25354628 -0.6761234 -0.67612340 -0.25354628 -0.25354628 -0.6761234 #> 2 2 2.41458180 -0.6678631 -0.66786305 -0.66786305 -0.66786305 0.8733594 #> 3 3 0.21764288 -0.8705715 -0.87057150 -0.87057150 1.30585725 -0.8705715 #> 4 4 0.61658123 1.0569964 0.17616607 0.17616607 1.93782672 -0.2642491 #> 5 5 0.96186009 0.5038315 -0.87025436 0.04580286 1.41988870 1.4198887 #> 6 6 -0.06459959 -0.7105955 -0.92592741 -0.71059546 -0.27993154 -0.7105955 #> 7 7 -0.09086738 -0.9995412 -0.99954118 -0.99954118 -0.09086738 1.7264802 #> 8 8 2.40535118 0.8017837 0.00000000 0.00000000 0.00000000 0.0000000 #> 9 9 -0.50260633 -0.5026063 -0.50260633 1.70395805 2.07171878 -0.5026063 #> 10 10 -0.94019379 -0.9401938 -0.04477113 0.85065153 2.19378551 -0.4924825 #> T7 T8 T9 T10 type #> 1 -0.6761234 -0.25354628 1.85933936 1.85933936 type_A #> 2 -0.1541222 -0.66786305 0.35961857 -0.15412224 type_A #> 3 -0.8705715 1.30585725 1.30585725 0.21764288 type_A #> 4 -0.7046643 -0.70466426 -1.14507943 -1.14507943 type_A #> 5 -0.8702544 -0.87025436 -0.87025436 -0.87025436 type_A #> 6 -0.2799315 0.15073237 1.44272411 2.08871998 type_A #> 7 1.7264802 -0.09086738 -0.09086738 -0.09086738 type_A #> 8 -0.8017837 -0.80178373 -0.80178373 -0.80178373 type_A #> 9 -0.3800194 -0.50260633 -0.50260633 -0.38001942 type_A #> 10 -0.4924825 -0.49248246 0.85065153 -0.49248246 type_A"},{"path":"https://kaiyanm.github.io/MolPad/reference/reshape_for_make_functions.html","id":null,"dir":"Reference","previous_headings":"","what":"Reshape for 'make' functions — reshape_for_make_functions","title":"Reshape for 'make' functions — reshape_for_make_functions","text":"internal function produces three primary datasets dashboard intended \"make\" functions.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/reshape_for_make_functions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reshape for 'make' functions — reshape_for_make_functions","text":"","code":"reshape_for_make_functions(data, cluster, annotation, id_colname, id_type)"},{"path":"https://kaiyanm.github.io/MolPad/reference/reshape_for_make_functions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reshape for 'make' functions — reshape_for_make_functions","text":"data output pre_process() cluster output gClusters() annotation output gPathway() id_colname columns contain database IDs. id_type corresponding database names columns.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/reshape_for_make_functions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reshape for 'make' functions — reshape_for_make_functions","text":"","code":"data(test_data) l <- reshape_for_make_functions(test_data_processed, test_cluster, test_annotations_processed, id_colname = c(\"GO_ID\",\"KEGG_ID\"),id_type = c(\"GO\",\"KEGG\")) head(l[[1]]) #> ID cluster #> 1 1 Group_2 #> 2 2 Group_5 #> 3 3 Group_2 #> 4 4 Group_4 #> 5 5 Group_4 #> 6 6 Group_2 #> GO_ID #> 1 #> 2 #> 3 GO:0003674,GO:0003824,GO:0005575,GO:0006629,GO:0006644,GO:0006650,GO:0006655,GO:0006793,GO:0006796,GO:0008150,GO:0008152,GO:0008610,GO:0008654,GO:0008808,GO:0009058,GO:0009987,GO:0016020,GO:0016740,GO:0016772,GO:0016780,GO:0019637,GO:0030572,GO:0032048,GO:0032049,GO:0044237,GO:0044238,GO:0044249,GO:0044255,GO:0045017,GO:0046471,GO:0046474,GO:0046486,GO:0071704,GO:0090407,GO:1901576 #> 4 GO:0003674,GO:0003676,GO:0003723,GO:0003729,GO:0003824,GO:0004654,GO:0005488,GO:0005575,GO:0005622,GO:0005623,GO:0005737,GO:0006139,GO:0006401,GO:0006402,GO:0006725,GO:0006807,GO:0008150,GO:0008152,GO:0009056,GO:0009057,GO:0009892,GO:0009987,GO:0010468,GO:0010605,GO:0010629,GO:0016070,GO:0016071,GO:0016740,GO:0016772,GO:0016779,GO:0019222,GO:0019439,GO:0034641,GO:0034655,GO:0043170,GO:0044237,GO:0044238,GO:0044248,GO:0044260,GO:0044265,GO:0044270,GO:0044424,GO:0044464,GO:0046483,GO:0046700,GO:0048519,GO:0050789,GO:0060255,GO:0065007,GO:0071704,GO:0090304,GO:0097159,GO:1901360,GO:1901361,GO:1901363,GO:1901575 #> 5 #> 6 #> KEGG_ID Pathway taxonomic.scope #> 1 K07124 Integumentary System hormonal proteins #> 2 Skeletal System structural proteins #> 3 K06131 Muscular System enzymes #> 4 K00962 Nervous System contractile proteins #> 5 K02083 Endocrine System contractile proteins #> 6 Cardiovascular System structural proteins head(l[[2]]) #> # A tibble: 6 × 6 #> ID type cluster day value taxonomic.scope #> #> 1 1 type_A Group_2 T1 -0.254 hormonal proteins #> 2 1 type_A Group_2 T2 -0.676 hormonal proteins #> 3 1 type_A Group_2 T3 -0.676 hormonal proteins #> 4 1 type_A Group_2 T4 -0.254 hormonal proteins #> 5 1 type_A Group_2 T5 -0.254 hormonal proteins #> 6 1 type_A Group_2 T6 -0.676 hormonal proteins head(l[[3]]) #> ID cluster #> 1 1 Group_2 #> 2 2 Group_5 #> 3 3 Group_2 #> 4 4 Group_4 #> 5 5 Group_4 #> 6 6 Group_2 #> GO_ID #> 1 #> 2 #> 3 GO:0003674<\/a>
GO:0003824<\/a>
GO:0005575<\/a>
GO:0006629<\/a>
GO:0006644<\/a>
GO:0006650<\/a>
GO:0006655<\/a>
GO:0006793<\/a>
GO:0006796<\/a>
GO:0008150<\/a>
GO:0008152<\/a>
GO:0008610<\/a>
GO:0008654<\/a>
GO:0008808<\/a>
GO:0009058<\/a>
GO:0009987<\/a>
GO:0016020<\/a>
GO:0016740<\/a>
GO:0016772<\/a>
GO:0016780<\/a>
GO:0019637<\/a>
GO:0030572<\/a>
GO:0032048<\/a>
GO:0032049<\/a>
GO:0044237<\/a>
GO:0044238<\/a>
GO:0044249<\/a>
GO:0044255<\/a>
GO:0045017<\/a>
GO:0046471<\/a>
GO:0046474<\/a>
GO:0046486<\/a>
GO:0071704<\/a>
GO:0090407<\/a>
GO:1901576<\/a> #> 4 GO:0003674<\/a>
GO:0003676<\/a>
GO:0003723<\/a>
GO:0003729<\/a>
GO:0003824<\/a>
GO:0004654<\/a>
GO:0005488<\/a>
GO:0005575<\/a>
GO:0005622<\/a>
GO:0005623<\/a>
GO:0005737<\/a>
GO:0006139<\/a>
GO:0006401<\/a>
GO:0006402<\/a>
GO:0006725<\/a>
GO:0006807<\/a>
GO:0008150<\/a>
GO:0008152<\/a>
GO:0009056<\/a>
GO:0009057<\/a>
GO:0009892<\/a>
GO:0009987<\/a>
GO:0010468<\/a>
GO:0010605<\/a>
GO:0010629<\/a>
GO:0016070<\/a>
GO:0016071<\/a>
GO:0016740<\/a>
GO:0016772<\/a>
GO:0016779<\/a>
GO:0019222<\/a>
GO:0019439<\/a>
GO:0034641<\/a>
GO:0034655<\/a>
GO:0043170<\/a>
GO:0044237<\/a>
GO:0044238<\/a>
GO:0044248<\/a>
GO:0044260<\/a>
GO:0044265<\/a>
GO:0044270<\/a>
GO:0044424<\/a>
GO:0044464<\/a>
GO:0046483<\/a>
GO:0046700<\/a>
GO:0048519<\/a>
GO:0050789<\/a>
GO:0060255<\/a>
GO:0065007<\/a>
GO:0071704<\/a>
GO:0090304<\/a>
GO:0097159<\/a>
GO:1901360<\/a>
GO:1901361<\/a>
GO:1901363<\/a>
GO:1901575<\/a> #> 5 #> 6 #> KEGG_ID #> 1 K07124<\/a> #> 2 #> 3 K06131<\/a> #> 4 K00962<\/a> #> 5 K02083<\/a> #> 6 #> Pathway taxonomic.scope #> 1 Integumentary System hormonal proteins #> 2 Skeletal System structural proteins #> 3 Muscular System enzymes #> 4 Nervous System contractile proteins #> 5 Endocrine System contractile proteins #> 6 Cardiovascular System structural proteins"},{"path":"https://kaiyanm.github.io/MolPad/reference/scale_by_row__.html","id":null,"dir":"Reference","previous_headings":"","what":"Scale by row — scale_by_row__","title":"Scale by row — scale_by_row__","text":"Scales values sample, row independently processed.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/scale_by_row__.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Scale by row — scale_by_row__","text":"","code":"scale_by_row__(data)"},{"path":"https://kaiyanm.github.io/MolPad/reference/scale_by_row__.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Scale by row — scale_by_row__","text":"input expected data frame first column ID following columns containing observations different time points. ID column remains unaltered, columns double (dbl) format scaled.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/scale_by_row__.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Scale by row — scale_by_row__","text":"","code":"data(test_data) scale_by_row__(test_data[1:5,1:10]) #> ID T1 T2 T3 T4 T5 T6 #> 1 1 -0.05847053 -0.5847053 -0.58470535 -0.05847053 -0.05847053 -0.5847053 #> 2 2 2.26366583 -0.6467617 -0.64676167 -0.64676167 -0.64676167 0.8084521 #> 3 3 0.22866478 -0.8003267 -0.80032673 -0.80032673 1.25765629 -0.8003267 #> 4 4 0.50395263 0.9575100 0.05039526 0.05039526 1.86462473 -0.4031621 #> 5 5 0.73869087 0.2841119 NA -0.17046712 1.19326987 1.1932699 #> T7 T8 T9 #> 1 -0.5847053 -0.05847053 2.5727035 #> 2 -0.1616904 -0.64676167 0.3233808 #> 3 -0.8003267 1.25765629 1.2576563 #> 4 -0.8567195 -0.85671947 -1.3102768 #> 5 -1.0796251 -1.07962512 -1.0796251"},{"path":"https://kaiyanm.github.io/MolPad/reference/test_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Test data — test_data","title":"Test data — test_data","text":"synthetically generated dataset created basic testing purposes.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/test_data.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Test data — test_data","text":"two datasets: data annotations","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/test_data.html","id":"data","dir":"Reference","previous_headings":"","what":"data","title":"Test data — test_data","text":"data frame 100 rows 12 variables: ID row ID T1~T10 count value 10 timepoints type type ~D","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/test_data.html","id":"annotations","dir":"Reference","previous_headings":"","what":"annotations","title":"Test data — test_data","text":"data frame 100 rows 5 variables: ID row ID GO_ID go ID KEGG_ID kegg ID system primary lable: 'Integumentary System', 'Skeletal System', 'Muscular System', 'Nervous System', 'Endocrine System', 'Cardiovascular System', 'Lymphatic System', 'Respiratory System', 'Digestive System', 'Urinary System' class secondary label: 'antibodies', 'contractile proteins', 'enzymes', 'hormonal proteins', 'structural proteins', 'storage proteins', 'transport proteins'","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/test_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Test data — test_data","text":"","code":"data(test_data)"},{"path":"https://kaiyanm.github.io/MolPad/reference/transpose_dataframe__.html","id":null,"dir":"Reference","previous_headings":"","what":"Transpose dataframe — transpose_dataframe__","title":"Transpose dataframe — transpose_dataframe__","text":"function transposes provided data frame, using values first column new column names.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/transpose_dataframe__.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transpose dataframe — transpose_dataframe__","text":"","code":"transpose_dataframe__(data)"},{"path":"https://kaiyanm.github.io/MolPad/reference/transpose_dataframe__.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transpose dataframe — transpose_dataframe__","text":"expected input data frame first column serves Time, subsequent columns contain observations various features.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/transpose_dataframe__.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transpose dataframe — transpose_dataframe__","text":"","code":"a <- data.frame(\"Day\"=c(\"Day1\",\"Day2\",\"Day3\"),\"feature_1\" =c(1,2,3),\"feature_2\" =c(0,4,1),\"feature_3\" =c(1,1,0)) a #> Day feature_1 feature_2 feature_3 #> 1 Day1 1 0 1 #> 2 Day2 2 4 1 #> 3 Day3 3 1 0 transpose_dataframe__(a) #> Day1 Day2 Day3 #> 1 1 2 3 #> 2 0 4 1 #> 3 1 1 0"}]
diff --git a/man/figures/.DS_Store b/man/figures/.DS_Store
index bbe3881..862bd8d 100644
Binary files a/man/figures/.DS_Store and b/man/figures/.DS_Store differ
diff --git a/man/figures/cheesecase.png b/man/figures/cheesecase.png
deleted file mode 100644
index d91f265..0000000
Binary files a/man/figures/cheesecase.png and /dev/null differ
diff --git a/man/figures/cheesecase1.png b/man/figures/cheesecase1.png
new file mode 100644
index 0000000..6d4d8aa
Binary files /dev/null and b/man/figures/cheesecase1.png differ
diff --git a/vignettes/cheese.Rmd b/vignettes/cheese.Rmd
index a26acd4..18967b8 100644
--- a/vignettes/cheese.Rmd
+++ b/vignettes/cheese.Rmd
@@ -98,3 +98,18 @@ gDashboard(cheesedata,
id_colname = c("GO_ID","KEGG_ID"),
id_type = c("GO","KEGG"))
```
+
+# Results
+
+For example, when we filter for Actinomycetota (Actinobacteria) as the functional group, we see that there are no edges connecting to group 10 and group 3- the clusters that have the most features associated with Actinomycetoa for Cheese sample A (Fig 1.3.A).
+
+
+
+ { width=100% }
+ *Fig 1: Dashboard showing Actinomycetota filtered network (A) with enrichment pattern for Cheese Sample-A (B) and Cheese Sample-C (C); Cluster pattern for Group 9, which also is enriched for Type IV secretion genes (D).*
+
+
+
+Looking at the pattern traces of these groups, (Fig 1.3.B), there is a peak in samples A4 (week 9) and A5 (week 13), which mirrors the 16S rRNA results of Saak et al. Since these two clusters do not have edges connecting them to other groups, this suggests that the Actinomycetoa features found in these groups follow distinct longitudinal succession patterns that are independent. When looking at Actinomycetoa within Cheese Sample C we see a different pattern. Groups 2 and 5, have the most features associated with Actinomycetoa, but they are highly connected to the other groups (Fig 1.3.A). From these results, we can hypothesize that though Actinomycetoa features are more abundant in later time points for both cheese samples, their dynamics are differentially influenced.
+
+The authors found that Type VI secretion was enriched in Pseudomonadota bacteria (specifically, Psychrobacter), and hypothesized this enrichment was due to the importance of physical species interactions that occur with this habitat. Using MolPad, we searched for other secretion systems associated genes, to understand their dynamics within the community. Focusing on KEGG annotated Type IV secretion genes, we found that Group 9 contained 12/13 of these genes. Within this group, features that cluster are ones that peak in Cheese sample C5 (week 13, Fig 1.3.D). This is also the most taxonomically diverse sample. From this, we can hypothesize that increased taxonomic diversity is also associated with increases in genes that are related to competitive species interactions.
\ No newline at end of file
diff --git a/vignettes/getstarted.Rmd b/vignettes/getstarted.Rmd
index b9ec236..5724477 100644
--- a/vignettes/getstarted.Rmd
+++ b/vignettes/getstarted.Rmd
@@ -45,6 +45,8 @@ library(MolPad)
load("/Users/hazelma/Documents/GitHub/MolPad_Data/multiomics_simulate.RData")
```
+
+
# Before Input
We recommend carefully reviewing your data and considering applying normalization (standardization, quantile normalization, log transformation, ...) and imputation (KNN, PMM, Mean/Median/Mode, ...) to address issues related to library size and missing data beforehead, otherwise the `pre_processing()` function will default to converting NAs to 0s.
@@ -57,6 +59,7 @@ We recommend carefully reviewing your data and considering applying normalizatio
* If it's hard to decide, you can always experiment with different methods and use cross-validation to compare their effectiveness. Let your choice be guided by domain knowledge and the specific requirements of your analysis to increase the quality of the following steps.
+
# Data Format
@@ -72,14 +75,17 @@ If your datasets are not multi-omics, you can simply apply it to our dashboard b
### Data Format and Pre-process
+
{ width=70% }
-
+
In the figure, A, B, and C can represent datasets from different sources or different aspects of measurements. Below are two examples of what the input data might look like and how to transform it into the required format through `pre_process()` modules.
+
+
#### If your input is a list of datasets:
Here is a list of datasets, each following the same format: they all have an ID column and identical column names.
@@ -105,6 +111,8 @@ data <- pre_process(fungal_dlist, typenameList = c('DataSource_A','DataSource_B'
head(data,10)
```
+
+
#### If your input is a single dataset:
Here is a dataset that already includes a labeled 'type' column:
@@ -121,6 +129,7 @@ x1 <- pre_process(x, replaceNA = TRUE,scale = TRUE)
head(x1,10)
```
+
# Choose Your Annotation
@@ -137,15 +146,18 @@ If your data has more than two layers of labels (like the cheese dataset), you c
Annotation_path_taxon <- gAnnotation(annotations,"phylum","class")
```
+
+
# Network Generation
To generate the network, we first scale and cluster the trajectories across the input molecular features. Trajectories are clustered using K-means, and a built-in elbow method is used to select the optimal K. We build network between clustered trajectories by adapting the GENIE3 algorithm. Specifically, the network is learned through a series of regressions, where each cluster centroid is predicted from the expression patterns of all the other cluster centroids using random forests. We choose random forests because of their potential to model interacting features and non-linearity without strong assumptions.
+
{ width=50% }
-
+
To determine which feature clusters are predictive of a given trajectory, the Mean Decrease Accuracy of a subset of top predictors whose expression directly influences the expression of the target cluster is taken as an indication of a putative link.
@@ -159,6 +171,8 @@ network_result <- cluster_result |>
gNetwork_view(network_result)
```
+
+
# Dashboard Navigation
@@ -173,46 +187,68 @@ gDashboard(data,
id_type = "KEGG")
```
+
{ width=70% }
+
Once you've launched the Shiny dashboard, you can zoom in or make adjustments to explore interesting findings within your data. To effectively navigate the dashboard generated by MolPad, you'll follow three main steps:
-### Choose a Primary Functional Annotation and Adjust Edge Density
+### 1. Choose a Primary Functional Annotation and Adjust Edge Density
Start by selecting a primary functional annotation from the available options. Then, fine-tune the edge density by adjusting the threshold value on the importance score. Nodes that turn bright green indicate clusters containing the most features related to the chosen functional annotation.
-### Explore the Network
+### 2. Explore the Network
Brushing on the network unveils patterns of taxonomic composition and typical trajectories. You can also zoom into specific taxonomic annotations by applying filters.
-### Investigate Feature Details and Related Function Annotations
+### 3. Investigate Feature Details and Related Function Annotations
Delve into the feature table to examine the specifics of the features within the selected clusters. Explore additional related function annotations using the drop-down options. Click on the provided links to access online information about the items of interest. The interface encourages iterative exploration, enabling you to conduct multiple steps to answer specific questions, such as comparing pattern distributions between two functions or identifying functionally important community members metabolizing a feature of interest.
+
+
+
{ width=100% }
+
# Related Visualization Outputs
The following visualizations are integrated into the dashboard, so there's no need to run function for each plot individually. We've linked a stacked bar plot and a ribbon plot with the network to facilitate focus and context explorations. This setup helps in detecting similar longitudinal patterns and understanding their relationships.
+
+
+
### Network Plot
+
+The network plot is a powerful visualization tool that displays the relationships between different groups or features within your data. In MolPad, the network plot helps to identify clusters of features that share similar patterns, revealing underlying connections that might not be immediately obvious. By visualizing these connections, users can gain a clearer understanding of the structure within their data, making it easier to pinpoint significant associations and trends.
+
```{r,echo=FALSE}
data("test_data")
make_the_graph(test_graphptw, test_network, 0.03, "Muscular System","nicely")
```
+
+
### Stack Bar Plot
+
+The stacked bar plot provides a detailed view of the composition of each cluster or group in your dataset. By stacking different categories on top of each other within a single bar, this plot allows for a quick comparison of relative proportions across multiple groups. This is particularly useful in microbiome experiments where understanding the distribution of taxa across different conditions or time points is crucial. The stacked bar plot makes it easy to see how these distributions change between experimental conditions, facilitating deeper insights.
+
```{r,echo=FALSE}
make_stackbar_plot(test_maindata, c("Group_1","Group_2","Group_3","Group_4"), c("hormonal proteins","structural proteins","enzymes","storage proteins","antibodies","transport proteins"))
```
+
+
+
### Ribbon Plot
+The ribbon plot is designed to visualize changes over time, making it an ideal tool for tracking longitudinal data. In MolPad, the ribbon plot illustrates how the abundance or expression levels of features vary across different time points or conditions. The smooth, flowing design of the ribbons helps to emphasize trends and patterns, enabling users to quickly identify periods of significant change or stability. This plot is particularly advantageous when comparing multiple groups, as it clearly shows overlapping trends and divergences, providing a comprehensive view of temporal dynamics in the data.
+
```{r,echo=FALSE}
make_line_plot(test_maindata, c("Group_1","Group_2","Group_3","Group_4"), c("hormonal proteins","structural proteins","enzymes","storage proteins","antibodies","transport proteins"))
diff --git a/vignettes/whymolpad.Rmd b/vignettes/whymolpad.Rmd
index c308dba..38cb484 100644
--- a/vignettes/whymolpad.Rmd
+++ b/vignettes/whymolpad.Rmd
@@ -24,11 +24,17 @@ library(dplyr)
# Why MolPad?
With the increasing multi-omics data and longitudinal designs integrated into microbiome experiments, there is a growing need to present the network, especially with complex variations across biological modalities. Network perspective helps detect the underlying co-occurrence among microbiome samples, allowing for high-level insights into the global structure. Yet when it comes to experimental data that records time series for 100,000 features, the network will collapse into some entangled clumps and therefore unable to read.
+
+
{ width=50% }
+
+
+# Improvements
+
For the aim of network interpretation, MolPad shows improvements in 3 important aspects:
* Capture groups of features that share longitudinal patterns and functional properties within the network.
@@ -39,6 +45,7 @@ This package especially facilitates the exploration of longitudinal molecular co
Unlike existing tools, MolPad stands out for its rich network interactivity and support for contextualization. It is also equipped with a beginner-friendly interface and self-contained data processing functions, making it accessible to users with limited programming experience.
+
# Key Features
@@ -54,25 +61,31 @@ We build network between clustered trajectories by adapting the GENIE3 algorithm
This package provides an interactive dashboard designed to help users explore data and uncover relationships within complex networks. By utilizing focus-plus-context visualization, the dashboard allows users to zoom in on specific details while keeping the broader dataset in view. This approach enables a deeper understanding of the data, making it easier to identify patterns and make informed decisions.
+
+
# What does it look like?
+Now, let’s see what you can get from the dashboard. We’ll start with an overview and then demonstrate how to discover patterns within your data.
+
+
### MolPad Dashboard Overview
+
{ width=100% }
-
+
Above is the overview of the MolPad Dashboard. To explore the dashboard effectively, you can start by following the sequence A-B-C-D. This approach will guide you through the cluster-level network, taxonomic-level bar plot, type-level line plot, and feature-level table. From there, you can modify detailed settings to identify and investigate special patterns within your data.
-
### Discovering related patterns
{ width=100% }
+
Here is a short example of discovering related patterns using the network plot: The shade of the edges represents the proximity of nodes. In the brushed area, Groups 1-7-8 (circled by solid black lines) and Groups 1-2 (circled by blue dashed lines) are strongly linked. For Groups 1, 7, and 8, the patterns form a W-shape with a noticeable peak at the same time section. Although Group 1 shows higher volatility compared to Group 2, both groups display a highly overlapping increasing trend.
library(MolPad)Before Input
@@ -136,6 +137,7 @@ Tips for normalization and imputa
of your analysis to increase the quality of the following
steps.
+
Multi-omics: Yes or No
Data Format and Pre-process
-
+
Fig 1: Reshape a list of datasets into a
standard format.
+
In the figure, A, B, and C can represent datasets from different
sources or different aspects of measurements. Below are two examples of
what the input data might look like and how to transform it into the
required format through pre_process() modules.
+
If your input is a list of datasets:
@@ -293,61 +297,63 @@ If your input is a list of datasets
#> 8 1.2235141 -1.2241931 0.6790383 -0.28487806 -0.74817082 DataSource_A
#> 9 1.1466328 -1.4265286 0.5357111 -0.51799361 -0.52413804 DataSource_A
#> 10 1.1813273 -1.2944121 0.6417621 -0.46906150 -0.66447461 DataSource_A
+
Fig 1: Reshape a list of datasets into a
standard format.
In the figure, A, B, and C can represent datasets from different
sources or different aspects of measurements. Below are two examples of
what the input data might look like and how to transform it into the
required format through pre_process() modules.
If your input is a list of datasets:
@@ -293,61 +297,63 @@If your input is a list of datasets #> 8 1.2235141 -1.2241931 0.6790383 -0.28487806 -0.74817082 DataSource_A #> 9 1.1466328 -1.4265286 0.5357111 -0.51799361 -0.52413804 DataSource_A #> 10 1.1813273 -1.2944121 0.6417621 -0.46906150 -0.66447461 DataSource_A
If your input is a single dataset:
Here is a dataset that already includes a labeled ‘type’ column:
-#> ID Day_1 Day_2 Day_3 Day_4 Day_5 Day_6
-#> 1 1 NA NA 0.104083550 -1.07999900 1.3416214 -1.733564471
-#> 2 2 NA NA 0.882578509 0.08440181 0.7165712 0.004811372
-#> 3 3 0.40203917 NA 0.675076590 0.97017740 -0.8210872 0.659073443
-#> 4 4 1.02859068 0.30178418 0.412617771 0.39306149 -0.3427640 0.837009714
-#> 5 5 -2.14036218 -0.06315709 0.656982200 -0.18301850 -1.3967562 -0.123327229
-#> 6 6 -2.04354192 1.30960484 1.371349945 -1.67403067 -1.2946150 0.760315504
-#> 7 7 0.31164367 2.26615374 0.972843085 -0.03956293 1.0190965 1.180680430
-#> 8 8 -0.79692858 -0.86038791 -3.417872877 0.62118867 -2.1983197 -0.104229763
-#> 9 9 -0.12033042 1.17585682 -0.001075426 0.01122359 0.3430992 -0.189234532
-#> 10 10 0.02592915 0.48551867 0.841254390 -0.46430744 0.6891800 0.751766756
-#> Day_7 Day_8 type
-#> 1 0.4638261 2.1318716 peptide
-#> 2 -0.2949926 -0.9310538 peptide
-#> 3 -0.2520195 0.7809977 peptide
-#> 4 -1.0120371 -0.1162361 peptide
-#> 5 -2.0085970 -0.8959521 peptide
-#> 6 0.7874222 -0.5106016 peptide
-#> 7 -0.7719437 -1.4686258 peptide
-#> 8 0.3541200 0.8224521 lipid
-#> 9 0.5674059 -0.9313882 lipid
-#> 10 -0.4097464 NA metabolite#> ID Day_1 Day_2 Day_3 Day_4 Day_5 Day_6
+#> 1 1 NA NA -0.82919412 0.08237778 1.30438699 0.4875914
+#> 2 2 NA NA -0.41606256 1.42523475 -0.44792066 1.1425154
+#> 3 3 1.0354708 NA 0.78841026 -1.49585251 -0.41361867 -0.3271995
+#> 4 4 -0.3005323 0.6998150 -1.86018783 1.14167433 -1.04204735 -2.4930409
+#> 5 5 -0.1307903 0.7839983 0.25834857 0.62421147 -0.07202124 -0.9214203
+#> 6 6 -0.9910321 1.8059465 -1.25395397 -0.45260649 -0.57303842 0.9990113
+#> 7 7 -0.9160734 1.4475673 -3.10452531 -0.06560360 -1.27374744 -0.5703149
+#> 8 8 -0.7381729 -0.4844302 1.10281675 0.12049188 0.20892638 -0.3115689
+#> 9 9 -0.2581716 -0.5906591 0.09794986 2.44249606 1.13135657 0.4886958
+#> 10 10 1.8831483 -1.2490571 0.55981559 -2.31731640 0.97323395 -0.7994677
+#> Day_7 Day_8 type
+#> 1 -1.4632070 0.05832042 peptide
+#> 2 -0.4292379 -0.79047610 peptide
+#> 3 -0.3508094 -0.34995898 peptide
+#> 4 -0.3011731 0.67869535 peptide
+#> 5 1.1761504 -0.86303628 peptide
+#> 6 -0.8993647 -0.44801227 peptide
+#> 7 1.1593812 -0.90149427 peptide
+#> 8 0.5463595 -1.51687039 lipid
+#> 9 -0.5016034 -0.08241911 lipid
+#> 10 -0.9442708 NA metabolite- after
pre_process():
x1 <- pre_process(x, replaceNA = TRUE,scale = TRUE)
head(x1,10)
-#> ID Day_1 Day_2 Day_3 Day_4 Day_5 Day_6
-#> 1 1 -0.12499488 -0.1249949 -0.04022866 -1.00455207 0.9676290 -1.53681943
-#> 2 2 -0.10246242 -0.1024624 1.46237249 0.04718422 1.1680372 -0.09393173
-#> 3 3 0.16412393 -0.4940273 0.61109508 1.09418472 -1.8381738 0.58489741
-#> 4 4 1.27432720 0.1728189 0.34079228 0.31115385 -0.8040231 0.98397755
-#> 5 5 -1.35954882 0.7001734 1.41425156 0.58132077 -0.6222015 0.64050965
-#> 6 6 -1.35505505 1.0595199 1.10398207 -1.08897287 -0.8157586 0.66398092
-#> 7 7 -0.10291149 1.5438734 0.45418630 -0.39882285 0.4931574 0.62930098
-#> 8 8 -0.06748422 -0.1105542 -1.84632390 0.89499455 -1.0186109 0.40265173
-#> 9 9 -0.36947382 1.7376966 -0.17560476 -0.15561064 0.3839091 -0.48148903
-#> 10 10 -0.41003145 0.4704748 1.15201231 -1.34925296 0.8606601 0.98056711
+#> ID Day_1 Day_2 Day_3 Day_4 Day_5 Day_6
+#> 1 1 0.05451890 0.05451890 -0.9508450 0.1543986 1.6360347 0.64570353
+#> 2 2 -0.07554744 -0.07554744 -0.5950354 1.7039742 -0.6348129 1.35097605
+#> 3 3 1.49874204 0.17759695 1.1835204 -1.7309438 -0.3501343 -0.23987305
+#> 4 4 0.10376561 0.87801613 -1.1033792 1.2200072 -0.4701534 -1.59319589
+#> 5 5 -0.31530188 0.89803359 0.2008348 0.6860993 -0.2373532 -1.36395879
+#> 6 6 -0.71746086 1.90776216 -0.9642373 -0.2120986 -0.3251351 1.15037885
+#> 7 7 -0.27002806 1.37506283 -1.7931878 0.3218978 -0.5189688 -0.02938062
+#> 8 8 -0.74469269 -0.43190500 1.5246885 0.3137802 0.4227931 -0.21881953
+#> 9 9 -0.58919072 -0.91616395 -0.2389755 2.0666865 0.7772922 0.14528989
+#> 10 10 1.56649401 -0.74805373 0.5886154 -1.5374455 0.8941115 -0.41582898
#> Day_7 Day_8 type
-#> 1 0.2527477 1.6112132 peptide
-#> 2 -0.6254920 -1.7532453 peptide
-#> 3 -0.9065915 0.7844915 peptide
-#> 4 -1.8183370 -0.4607098 peptide
-#> 5 -1.2288927 -0.1256124 peptide
-#> 6 0.6835002 -0.2511966 peptide
-#> 7 -1.0158949 -1.6028889 peptide
-#> 8 0.7137346 1.0315924 lipid
-#> 9 0.7485573 -1.6879846 lipid
-#> 10 -1.2447220 -0.4597079 metabolite
Annotation_path_taxon <- gAnnotation(annotations,"phylum","class")Network Generation
@@ -388,12 +395,13 @@ Network Generation
Fig 2: Each cluster centroid is predicted from
all the other cluster centroids.
Fig 2: Each cluster centroid is predicted from
all the other cluster centroids.
Dashboard Navigation
@@ -421,17 +432,19 @@ Dashboard Navigationnetwork_result,
id_colname = "ko_term" ,
id_type = "KEGG")
Fig 3:
gDashboard() functionOnce you’ve launched the Shiny dashboard, you can zoom in or make adjustments to explore interesting findings within your data. To effectively navigate the dashboard generated by MolPad, you’ll follow three main steps:
-Choose a Primary Functional Annotation and Adjust Edge Density
+
Choose a Primary Functional Annotation and Adjust Edge Density
+1. Choose a Primary Functional Annotation and Adjust Edge
+Density
Start by selecting a primary functional annotation from the available options. Then, fine-tune the edge density by adjusting the threshold @@ -440,14 +453,14 @@
Choose a annotation.
-Explore the Network
+
Explore the Network
+2. Explore the Network
Brushing on the network unveils patterns of taxonomic composition and typical trajectories. You can also zoom into specific taxonomic annotations by applying filters.
-Investigate Feature Details and Related Function Annotations
+
+
Investigate Feature Details and Related Function Annotations
+3. Investigate Feature Details and Related Function Annotations
Delve into the feature table to examine the specifics of the features within the selected clusters. Explore additional related function @@ -457,11 +470,13 @@
Investigat to answer specific questions, such as comparing pattern distributions between two functions or identifying functionally important community members metabolizing a feature of interest. +
Fig 4: MolPad Dashboard Overview.
@@ -472,19 +487,47 @@ Related Visualization Outputs
Related Visualization Outputs
Network Plot
+The network plot is a powerful visualization tool that displays the
+relationships between different groups or features within your data. In
+MolPad, the network plot helps to identify clusters of features that
+share similar patterns, revealing underlying connections that might not
+be immediately obvious. By visualizing these connections, users can gain
+a clearer understanding of the structure within their data, making it
+easier to pinpoint significant associations and trends.
+
Stack Bar Plot
+The stacked bar plot provides a detailed view of the composition of +each cluster or group in your dataset. By stacking different categories +on top of each other within a single bar, this plot allows for a quick +comparison of relative proportions across multiple groups. This is +particularly useful in microbiome experiments where understanding the +distribution of taxa across different conditions or time points is +crucial. The stacked bar plot makes it easy to see how these +distributions change between experimental conditions, facilitating +deeper insights.
Ribbon Plot
+The ribbon plot is designed to visualize changes over time, making it +an ideal tool for tracking longitudinal data. In MolPad, the ribbon plot +illustrates how the abundance or expression levels of features vary +across different time points or conditions. The smooth, flowing design +of the ribbons helps to emphasize trends and patterns, enabling users to +quickly identify periods of significant change or stability. This plot +is particularly advantageous when comparing multiple groups, as it +clearly shows overlapping trends and divergences, providing a +comprehensive view of temporal dynamics in the data.
Why MolPad?
-With the increasing multi-omics data and longitudinal designs integrated -into microbiome experiments, there is a growing need to present the -network, especially with complex variations across biological -modalities. Network perspective helps detect the underlying +With the increasing multi-omics data and longitudinal designs +integrated into microbiome experiments, there is a growing need to +present the network, especially with complex variations across +biological modalities. Network perspective helps detect the underlying co-occurrence among microbiome samples, allowing for high-level insights into the global structure. Yet when it comes to experimental data that records time series for 100,000 features, the network will collapse into -some entangled clumps and therefore unable to read. -
+
Improvements +
For the aim of network interpretation, MolPad shows improvements in 3 important aspects:
-
@@ -117,6 +122,7 @@
Why MolPad?
Key Features
@@ -147,16 +153,21 @@ Key Features
What does it look like?
+Now, let’s see what you can get from the dashboard. We’ll start with
+an overview and then demonstrate how to discover patterns within your
+data.
MolPad Dashboard Overview
+
Above is the overview of the MolPad Dashboard. To explore the
dashboard effectively, you can start by following the sequence A-B-C-D.
This approach will guide you through the cluster-level network,
@@ -170,6 +181,7 @@
Discovering related patterns
+
Here is a short example of discovering related patterns using the
network plot: The shade of the edges represents the proximity of nodes.
In the brushed area, Groups 1-7-8 (circled by solid black lines) and
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
index 24820a2..106fea2 100644
--- a/docs/pkgdown.yml
+++ b/docs/pkgdown.yml
@@ -6,7 +6,7 @@ articles:
cheese: cheese.html
getstarted: getstarted.html
whymolpad: whymolpad.html
-last_built: 2024-08-15T06:34Z
+last_built: 2024-08-15T07:35Z
urls:
reference: https://kaiyanm.github.io/MolPad/reference
article: https://kaiyanm.github.io/MolPad/articles
diff --git a/docs/reference/figures/cheesecase.png b/docs/reference/figures/cheesecase.png
deleted file mode 100644
index d91f265..0000000
Binary files a/docs/reference/figures/cheesecase.png and /dev/null differ
diff --git a/docs/reference/figures/cheesecase1.png b/docs/reference/figures/cheesecase1.png
new file mode 100644
index 0000000..6d4d8aa
Binary files /dev/null and b/docs/reference/figures/cheesecase1.png differ
diff --git a/docs/reference/gClusters-1.png b/docs/reference/gClusters-1.png
index 993d2a4..463b550 100644
Binary files a/docs/reference/gClusters-1.png and b/docs/reference/gClusters-1.png differ
diff --git a/docs/reference/gClusters.html b/docs/reference/gClusters.html
index 28642a8..566cc47 100644
--- a/docs/reference/gClusters.html
+++ b/docs/reference/gClusters.html
@@ -102,70 +102,70 @@
Examplesreslist <- gClusters(test_data_processed)
# k-means result
reslist[[1]]
-#> K-means clustering with 20 clusters of sizes 7, 6, 7, 10, 8, 2, 3, 5, 4, 6, 10, 3, 3, 4, 4, 6, 4, 3, 2, 3
+#> K-means clustering with 20 clusters of sizes 6, 3, 6, 5, 5, 8, 6, 5, 5, 6, 4, 11, 3, 3, 3, 2, 4, 2, 2, 11
#>
#> Cluster means:
-#> T1 T2 T3 T4 T5 T6
-#> 1 2.22789830 0.02950883 -0.75836275 -0.2619747 -0.5953255 -0.70974025
-#> 2 2.77349939 -0.21724464 -0.35972402 -0.4172516 -0.5000122 -0.11678355
-#> 3 0.93534606 -0.39771771 0.31238452 1.1531782 1.1995301 -1.05203742
-#> 4 -0.27715339 -0.39760069 -0.06466866 1.3995407 2.1316423 -0.28068841
-#> 5 0.11559709 -0.19000668 -0.25629794 -0.1487697 -0.4383348 2.58301481
-#> 6 -0.27847690 -0.82258408 -0.82258408 -0.8225841 1.2338761 -0.82258408
-#> 7 0.26998015 -0.08094677 -0.28926312 -0.7813127 -0.6065051 -0.14945230
-#> 8 -0.81355388 -0.84696982 -0.32573017 1.1218534 2.0955812 -0.56207309
-#> 9 1.45173820 0.26236463 1.31600581 -0.2531012 0.2004547 -1.12389420
-#> 10 0.34700978 0.13673201 0.82464908 0.2571569 1.2656031 0.63541302
-#> 11 -0.19020083 -0.69654961 -0.66836949 -0.4657663 -0.3851487 -0.55904213
-#> 12 1.62582942 1.51619556 -0.35316244 0.2978109 -0.1777508 0.02299732
-#> 13 -0.09187847 -0.87257293 -0.87257293 -0.8725729 -0.5696817 2.05668333
-#> 14 0.85679806 -0.37993358 -0.46099690 0.3934155 0.5857582 1.85027957
-#> 15 1.12205113 0.09185659 -0.89815993 -0.7233153 -0.5717924 -0.86037548
-#> 16 2.09193480 -0.33320285 -0.50786563 -0.5579659 -0.5579659 1.54559773
-#> 17 1.56599593 -0.49119311 -0.83000857 -0.6160199 -0.2092753 1.19151568
-#> 18 0.89463785 0.01698143 -0.57023879 -1.1815834 -1.1815834 1.18824796
-#> 19 -0.13543375 -0.90710305 -0.66248003 -0.4178570 -0.6624800 1.37010464
-#> 20 1.31631350 -0.76059916 -0.76059916 1.5193444 0.6006058 0.84385791
-#> T7 T8 T9 T10
-#> 1 -0.567081109 0.132060988 0.03929621 0.46372005
-#> 2 -0.357532815 -0.298895212 -0.29119673 -0.21485863
-#> 3 -1.222209771 -0.334925678 -0.36853535 -0.22501291
-#> 4 -0.548494719 -0.646992821 -0.66392148 -0.65166279
-#> 5 -0.006327004 -0.327677972 -0.66559889 -0.66559889
-#> 6 -0.822584085 1.233876127 1.23387613 0.68976894
-#> 7 -0.763902351 -0.412975440 0.39220455 2.42217307
-#> 8 -0.651292750 -0.498839228 0.42446013 0.05656427
-#> 9 -0.987867401 -0.340013670 -0.33085684 -0.19483004
-#> 10 -1.004339966 -0.620506186 -0.71949086 -1.12222690
-#> 11 -0.566522777 0.002653353 1.73917982 1.78976662
-#> 12 -0.770498738 -0.620423681 -0.77049874 -0.77049874
-#> 13 0.882337606 0.086536293 0.12686085 0.12686085
-#> 14 -0.526130916 -0.896529515 -0.89652951 -0.52613092
-#> 15 -0.822591022 0.091471523 1.00908283 1.56177203
-#> 16 -0.425513174 -0.557965933 -0.30571480 -0.39133827
-#> 17 0.774154665 0.196302112 -0.62663627 -0.95483532
-#> 18 0.894637851 -0.836550610 0.41343688 0.36201421
-#> 19 -0.907103054 -0.417856999 1.37010464 1.37010464
-#> 20 -0.666108041 -0.666108041 -0.66610804 -0.76059916
+#> T1 T2 T3 T4 T5 T6
+#> 1 2.0919348 -0.33320285 -0.50786563 -0.5579659 -0.5579659 1.5455977
+#> 2 0.8277105 1.32036794 -0.09261332 1.2325986 0.4199176 -0.9576634
+#> 3 0.5077879 -0.24564283 0.53503708 0.2363183 1.0793829 1.3583848
+#> 4 0.2914366 -0.41308350 -0.76541563 -0.9724353 -0.9724353 1.6040887
+#> 5 1.2081037 -0.66790409 -0.66790409 -0.9275526 -0.4204221 1.2719890
+#> 6 0.1155971 -0.19000668 -0.25629794 -0.1487697 -0.4383348 2.5830148
+#> 7 1.2574004 -0.64914997 -0.87502695 0.9353966 0.4760273 1.2681050
+#> 8 0.9389212 -0.86278238 0.43887930 1.0009533 1.3733646 -1.0423264
+#> 9 -0.8135539 -0.84696982 -0.32573017 1.1218534 2.0955812 -0.5620731
+#> 10 -0.3802219 -0.36840199 0.02254271 1.6459653 1.9633618 -0.3246455
+#> 11 -0.1225506 -0.44139873 -0.19548572 1.0299038 2.3840629 -0.2147528
+#> 12 2.5784883 -0.02446156 -0.60355041 -0.2547486 -0.5120230 -0.3947043
+#> 13 0.9922755 0.32399254 -0.94565021 -0.7125240 -0.5104935 -0.9456502
+#> 14 0.2699801 -0.08094677 -0.28926312 -0.7813127 -0.6065051 -0.1494523
+#> 15 1.7947637 -0.47746004 -0.52783931 -0.7635958 -0.7635958 -0.6438957
+#> 16 0.1789247 1.26157166 0.82115649 0.3899368 1.2707671 -0.2614905
+#> 17 1.4517382 0.26236463 1.31600581 -0.2531012 0.2004547 -1.1238942
+#> 18 -0.2784769 -0.82258408 -0.82258408 -0.8225841 1.2338761 -0.8225841
+#> 19 2.1235865 1.05868538 -0.39467611 0.1315587 -0.1315587 0.3946761
+#> 20 -0.2262821 -0.73107613 -0.66098092 -0.4323193 -0.4035074 -0.3836848
+#> T7 T8 T9 T10
+#> 1 -0.425513174 -0.557965933 -0.30571480 -0.391338269
+#> 2 -1.160107459 -0.295057401 -0.64757654 -0.647576540
+#> 3 -1.006282312 -0.766188425 -0.64803067 -1.050766722
+#> 4 0.301678216 -0.371684852 0.66435231 0.633498705
+#> 5 1.209152596 -0.095960244 -0.32347149 -0.586030729
+#> 6 -0.006327004 -0.327677972 -0.66559889 -0.665598891
+#> 7 -0.354972016 -0.601904415 -0.82778139 -0.628094550
+#> 8 -1.159101258 -0.345888528 -0.27147562 -0.070544200
+#> 9 -0.651292750 -0.498839228 0.42446013 0.056564270
+#> 10 -0.647007074 -0.667438225 -0.63229313 -0.611861976
+#> 11 -0.400726188 -0.616324716 -0.71136401 -0.711364008
+#> 12 -0.526022073 -0.185303954 -0.06989626 -0.007778173
+#> 13 -0.945650212 0.121962031 1.19430596 1.427432164
+#> 14 -0.763902351 -0.412975440 0.39220455 2.422173070
+#> 15 -0.260645101 0.389799714 -0.08327821 1.335746686
+#> 16 -0.912917717 -0.481698036 -1.13312530 -1.133125299
+#> 17 -0.987867401 -0.340013670 -0.33085684 -0.194830041
+#> 18 -0.822584085 1.233876127 1.23387613 0.689768939
+#> 19 -0.795567972 -0.795567972 -0.79556797 -0.795567972
+#> 20 -0.612869917 -0.006483243 1.70560793 1.751595921
#>
#> Clustering vector:
#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
-#> 11 16 6 10 10 11 13 12 4 8 3 7 15 2 3 8 5 11 9 1
+#> 20 1 18 16 3 20 5 19 10 9 8 14 15 12 2 9 6 20 17 12
#> 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40
-#> 17 11 5 16 2 8 20 1 5 4 17 4 10 6 4 10 3 15 11 12
+#> 7 20 6 1 12 9 7 12 6 11 5 11 16 18 10 3 8 13 20 2
#> 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
-#> 5 16 11 11 14 11 5 14 16 5 1 17 18 15 5 17 13 4 1 4
+#> 6 1 20 20 7 20 6 3 1 6 15 5 4 13 6 5 4 10 12 10
#> 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80
-#> 3 14 20 15 10 16 2 14 9 9 7 8 5 12 7 4 3 4 3 20
+#> 8 3 7 13 3 1 12 7 17 17 14 9 6 19 14 10 2 11 8 7
#> 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100
-#> 3 19 1 13 19 16 9 2 18 2 4 18 4 1 11 10 8 2 11 1
+#> 8 20 15 4 4 1 17 12 4 12 10 5 11 12 20 3 9 12 20 12
#>
#> Within cluster sum of squares by cluster:
-#> [1] 13.8467580 2.0713631 15.4102772 5.9461649 8.0412334 0.9921913
-#> [7] 3.1842269 3.4617088 9.9418120 17.6305488 8.0515257 4.9390800
-#> [13] 4.0041862 7.3057948 4.7723673 3.0260293 7.3290085 5.6033175
-#> [19] 0.9549143 2.4583646
-#> (between_SS / total_SS = 83.4 %)
+#> [1] 3.0260293 5.0357352 13.2343206 13.1118010 10.3695240 8.0412334
+#> [7] 11.7251902 8.3255313 3.4617088 2.4693324 1.6353685 13.0645655
+#> [13] 2.9416715 3.1842269 5.0413438 2.4669004 9.9418120 0.9921913
+#> [19] 0.9833964 12.4042363
+#> (between_SS / total_SS = 83.1 %)
#>
#> Available components:
#>
diff --git a/docs/reference/gNetwork-1.png b/docs/reference/gNetwork-1.png
index efb4385..14d64f3 100644
Binary files a/docs/reference/gNetwork-1.png and b/docs/reference/gNetwork-1.png differ
diff --git a/docs/reference/gNetwork.html b/docs/reference/gNetwork.html
index dc8f058..f0279e4 100644
--- a/docs/reference/gNetwork.html
+++ b/docs/reference/gNetwork.html
@@ -83,12 +83,12 @@ Examplesnetworkres <- gNetwork(test_cluster, ntop = 3)
head(networkres)
#> weight IncNodePurity var_names from
-#> 1 1.6429045 0.7371260 Group_3 Group_1
-#> 2 0.4588961 0.9310697 Group_5 Group_1
-#> 3 -0.7833169 0.6761286 Group_2 Group_1
-#> 4 5.0076415 0.8821582 Group_5 Group_2
-#> 5 4.1618215 1.1976058 Group_4 Group_2
-#> 6 2.5714583 1.2820391 Group_1 Group_2
+#> 1 1.4215243 1.1776012 Group_5 Group_1
+#> 2 0.7911266 0.7011049 Group_3 Group_1
+#> 3 -1.9891284 0.5811186 Group_2 Group_1
+#> 4 5.8098014 1.1107592 Group_4 Group_2
+#> 5 5.4000389 0.8744358 Group_5 Group_2
+#> 6 1.2492288 1.1816976 Group_1 Group_2
gNetwork_view(networkres)
diff --git a/docs/reference/make_the_graph-1.png b/docs/reference/make_the_graph-1.png
index 061c537..76d9b9c 100644
Binary files a/docs/reference/make_the_graph-1.png and b/docs/reference/make_the_graph-1.png differ
diff --git a/docs/search.json b/docs/search.json
index 782a610..33c4d18 100644
--- a/docs/search.json
+++ b/docs/search.json
@@ -1 +1 @@
-[{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"CC0 1.0 Universal","title":"CC0 1.0 Universal","text":"CREATIVE COMMONS CORPORATION LAW FIRM PROVIDE LEGAL SERVICES. DISTRIBUTION DOCUMENT CREATE ATTORNEY-CLIENT RELATIONSHIP. CREATIVE COMMONS PROVIDES INFORMATION “-” BASIS. CREATIVE COMMONS MAKES WARRANTIES REGARDING USE DOCUMENT INFORMATION WORKS PROVIDED HEREUNDER, DISCLAIMS LIABILITY DAMAGES RESULTING USE DOCUMENT INFORMATION WORKS PROVIDED HEREUNDER.","code":""},{"path":"https://kaiyanm.github.io/MolPad/LICENSE.html","id":"statement-of-purpose","dir":"","previous_headings":"","what":"Statement of Purpose","title":"CC0 1.0 Universal","text":"laws jurisdictions throughout world automatically confer exclusive Copyright Related Rights (defined ) upon creator subsequent owner(s) (, “owner”) original work authorship /database (, “Work”). Certain owners wish permanently relinquish rights Work purpose contributing commons creative, cultural scientific works (“Commons”) public can reliably without fear later claims infringement build upon, modify, incorporate works, reuse redistribute freely possible form whatsoever purposes, including without limitation commercial purposes. owners may contribute Commons promote ideal free culture production creative, cultural scientific works, gain reputation greater distribution Work part use efforts others. /purposes motivations, without expectation additional consideration compensation, person associating CC0 Work (“Affirmer”), extent owner Copyright Related Rights Work, voluntarily elects apply CC0 Work publicly distribute Work terms, knowledge Copyright Related Rights Work meaning intended legal effect CC0 rights. Copyright Related Rights. Work made available CC0 may protected copyright related neighboring rights (“Copyright Related Rights”). 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Affirmer disclaims responsibility clearing rights persons may apply Work use thereof, including without limitation person’s Copyright Related Rights Work. , Affirmer disclaims responsibility obtaining necessary consents, permissions rights required use Work. Affirmer understands acknowledges Creative Commons party document duty obligation respect CC0 use Work.","code":""},{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/articles/FAQ.html","id":"how-can-i-tell-if-my-dataset-is-suitable-for-using-this-dashboard","dir":"Articles","previous_headings":"Common Questions:","what":"How can I tell if my dataset is suitable for using this dashboard?","title":"FAQs","text":"lot longitudinal data least two descriptive columns (like types), match minimal needs starting dashboard. addition, data specific web-ID columns, MolPad make convenient createing links databases automatically.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/FAQ.html","id":"how-can-i-customize-my-dashboard","dir":"Articles","previous_headings":"Common Questions:","what":"How can I customize my dashboard?","title":"FAQs","text":"can edit title within gDashboard() function. additional personalization, adjusting colors sizes figures, can fork repository, create copy, make modifications dashboard.css file.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/FAQ.html","id":"what-should-i-do-if-i-only-have-a-few-time-points5","dir":"Articles","previous_headings":"Common Questions:","what":"What should I do if I only have a few time points(<5)?","title":"FAQs","text":"Firstly, strongly recommend extending length longitudinal samples. can achieve increasing number measurements two days incorporating simulated data. options don’t suit needs, can also consider using “linear” method gNetwork() function.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Case Study: Cheese Communities","text":"vignette provides comprehensive guide using MolPad case study, including data pre-processing, network generation, result analysis.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"washed-rind-cheese-microbial-communities","dir":"Articles","previous_headings":"","what":"Washed-Rind Cheese Microbial Communities","title":"Case Study: Cheese Communities","text":"Cheese making ancient craft involves coagulation milk proteins form curds, separated liquid whey. curds processed, shaped, aged develop desired texture flavor. Various factors, type milk, specific cultures bacteria, aging conditions, contribute unique characteristics cheese. One crucial step process aging practice, regular washing brine solution plays significant role. process producing cheese, regular washing brine solution aging practice can homogenize microbial communities cheese’ surface facilitate intermicrobial interactions. following parts, analyze longitudinal data set three washed-rind cheese communities collected cheese ripening.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"data","dir":"Articles","previous_headings":"","what":"Data","title":"Case Study: Cheese Communities","text":"analysis, use data set contained study Smith et al. (2022) microbial communities cheese. original research investigated successional dynamics occur within cheese rind microbial communities using combination 16S rRNA amplicon, Illumina, PacBio sequencing. functionally taxonomically annotate (using eggNOG (21) MMseqs2 (22)) contigs generated Illumina reads, demonstrate utility MolPad using single-omic. Specifically, focus Cheese Sample Cheese Sample C. detailed information attached data, please check documentation.","code":"data(\"cheese\") str(cheese) #> tibble [106,239 × 18] (S3: tbl_df/tbl/data.frame) #> $ ID : chr [1:106239] \"1\" \"2\" \"3\" \"4\" ... #> $ A_1 : int [1:106239] 38 23 24 3 58 12 1 14 1 3 ... #> $ A_2 : int [1:106239] 23 6 5 2 14 9 1 7 1 1 ... #> $ A_3 : int [1:106239] 27 4 37 4 45 14 0 14 3 5 ... #> $ A_4 : int [1:106239] 5 0 10 2 13 4 1 4 4 0 ... #> $ A_5 : int [1:106239] 11 9 19 16 32 13 0 4 1 1 ... #> $ C_1 : int [1:106239] 13 21 3 56 82 2 4 17 7 2 ... #> $ C_3 : int [1:106239] 1 1 0 1 3 0 0 0 0 1 ... #> $ C_4 : int [1:106239] 3 0 1 7 8 1 0 1 2 2 ... #> $ C_5 : int [1:106239] 0 2 4 17 3 4 0 1 8 6 ... #> $ GO_ID : chr [1:106239] NA NA NA NA ... #> $ KEGG_ID: chr [1:106239] NA NA NA NA ... #> $ domain : chr [1:106239] NA NA NA NA ... #> $ phylum : chr [1:106239] NA NA NA NA ... #> $ class : chr [1:106239] NA NA NA NA ... #> $ order : chr [1:106239] NA NA NA NA ... #> $ family : chr [1:106239] NA NA NA NA ... #> $ genus : chr [1:106239] NA NA NA NA ... str(annotations) #> tibble [86,156 × 9] (S3: tbl_df/tbl/data.frame) #> $ ID : chr [1:86156] \"9\" \"10\" \"11\" \"12\" ... #> $ GO_ID : chr [1:86156] \"-\" \"-\" \"-\" \"-\" ... #> $ KEGG_ID: chr [1:86156] \"-\" \"-\" \"-\" \"-\" ... #> $ domain : chr [1:86156] \"Bacteria\" \"Bacteria\" \"Bacteria\" \"Bacteria\" ... #> $ phylum : chr [1:86156] \"Pseudomonadota\" \"Pseudomonadota\" \"Pseudomonadota\" \"Pseudomonadota\" ... #> $ class : chr [1:86156] \"Alphaproteobacteria\" \"Alphaproteobacteria\" \"Alphaproteobacteria\" \"Alphaproteobacteria\" ... #> $ order : chr [1:86156] \"Caulobacterales\" \"Caulobacterales\" \"Hyphomicrobiales\" \"Hyphomicrobiales\" ... #> $ family : chr [1:86156] \"Caulobacteraceae\" \"Caulobacteraceae\" \"Bartonellaceae\" \"Brucellaceae;-_Brucella/Ochrobactrum group\" ... #> $ genus : chr [1:86156] \"Caulobacter\" \"Caulobacter;-_unclassified Caulobacter\" \"Bartonella\" \"Brucella\" ..."},{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"data-and-annotations","dir":"Articles","previous_headings":"Data > Pre-process","what":"Data and Annotations","title":"Case Study: Cheese Communities","text":"select ‘type’ column phylum describe characteristic cheese data. Also, columns phylum class taken tags elemental composition. section, introduce data preparing steps analysis. annotations dataset contains various columns describe characteristics properties samples. First, select ‘type’ column phylum provide broad categorization microbial communities present cheese surface. categorization helps understanding overall composition diversity microbes high taxonomic level. run pre_process() function clean standardize data. annotate dataset, also use columns phylum class tags elemental composition microbial communities. phylum column represents major taxonomic rank, giving us broad overview microbial distribution. class column provides detailed information, allowing us delve deeper specific types microbes present. pre-processing, two datasets put dashboard look like:","code":"cheesedata <- cheese |> select(ID, A_1:C_5, phylum) |> rename(type=phylum) |> pre_process() pathchee <- gAnnotation(annotations,\"phylum\",\"class\") # data cheesedata[112:115,] #> # A tibble: 4 × 11 #> ID A_1 A_2 A_3 A_4 A_5 C_1 C_3 C_4 C_5 type #> #> 1 112 0.943 -0.471 -0.471 -0.471 -0.471 2.36 -0.471 -0.471 -0.471 Other #> 2 113 0.786 -0.124 0.126 -0.623 -0.637 2.33 -0.667 -0.593 -0.593 Other #> 3 114 1.22 -1.09 -0.430 -0.829 -0.170 1.43 -1.30 0.455 0.715 Other #> 4 115 2.67 -0.333 -0.333 -0.333 -0.333 -0.333 -0.333 -0.333 -0.333 Other # annotation pathchee[112:115,] #> # A tibble: 4 × 9 #> ID GO_ID KEGG_ID domain Pathway taxonomic.scope order family genus #> #> 1 145 - ko:K00004 Bacte… Pseudo… Unknown NA NA NA #> 2 147 - ko:K00004 Bacte… Actino… Actinomycetes NA NA NA #> 3 148 - ko:K00004 Bacte… Bacill… Bacilli Baci… Bacil… Lysi… #> 4 149 - ko:K00004,ko:K0… Bacte… Actino… Actinomycetes NA NA NA"},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"cluster-input","dir":"Articles","previous_headings":"Data","what":"Cluster Input","title":"Case Study: Cheese Communities","text":"section, generate clusters first dataset using gClusters function. function takes cheese dataset (cheesedata) input generates clusters based specified parameters. , set number clusters 10 (ncluster = 10) specify maximum number clusters consider determining optimal number clusters (elbow.max=15).","code":"cluschee <- gClusters(cheesedata,ncluster = 10,elbow.max=15)"},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"network-input","dir":"Articles","previous_headings":"Data","what":"network input","title":"Case Study: Cheese Communities","text":"generating clusters major patterns, proceed obtain network results clusters. Taking cluster centroids nodes, prediction process edges divided individual regression tasks, cluster centroid independentally predicted expression cluster centroids, using random forests. pick top 3 related predictors cluster centroid save network output future use. achieved using gNetwork() function. gain insight network results, can visualize details using gNetwork_view() function, shown .","code":"networkchee <- gNetwork(cluschee,ntop = 3) gNetwork_view(networkchee)"},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"run-dashboard","dir":"Articles","previous_headings":"","what":"Run Dashboard","title":"Case Study: Cheese Communities","text":"clusters network results obtained, can proceed run dashboard. involves declaring annotations executing dashboard using gDashboard() function. , pass cheese dataset (cheesedata), cluster results (cluschee), network results (networkchee), specify column names types annotation identifiers.","code":"gDashboard(cheesedata, cluschee, pathchee, networkchee, id_colname = c(\"GO_ID\",\"KEGG_ID\"), id_type = c(\"GO\",\"KEGG\"))"},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"installation","dir":"Articles","previous_headings":"","what":"Installation","title":"Get started","text":"can either install MolPad devtools, clone repository local computer (example, onto ./Github) installing: , load package:","code":"# Install the package in R: install.packages(\"devtools\") library(devtools) install_github(\"KaiyanM/MolPad\") # Download through Github and install the package in R: setwd(\"./GitHub\") install(\"MolPad\") library(MolPad)"},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"before-input","dir":"Articles","previous_headings":"","what":"Before Input","title":"Get started","text":"recommend carefully reviewing data considering applying normalization (standardization, quantile normalization, log transformation, …) imputation (KNN, PMM, Mean/Median/Mode, …) address issues related library size missing data beforehead, otherwise pre_processing() function default converting NAs 0s.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"tips-for-normalization-and-imputation","dir":"Articles","previous_headings":"Before Input","what":"Tips for normalization and imputation","title":"Get started","text":"choose normalization method, first consider data distribution. Use Z-score normalization data normally distributed, log transformation robust scaling ’s skewed. analysis requires data within specific range, like [0, 1], opt Min-Max normalization. datasets outliers, robust scaling rank normalization better choices. selecting imputation method, assess amount pattern missing data. small amount missing data, simple methods like mean, median, mode imputation effective. missing data related variables, consider sophisticated approaches like Predictive Mean Matching k-NN imputation. computational resources concern, simpler imputation methods might practical, resources allow, advanced methods can improve data quality. ’s hard decide, can always experiment different methods use cross-validation compare effectiveness. Let choice guided domain knowledge specific requirements analysis increase quality following steps.","code":""},{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"multi-omics-yes-or-no","dir":"Articles","previous_headings":"Data Format","what":"Multi-omics: Yes or No","title":"Get started","text":"visualization pipeline starts pre-processing module includes built-functions reshape data inputs standard format streamline data preparation. Depending nature datasets, two primary conditions consider: research object includes multi-omics measurements, assume provide list dataframes, collected different omics type. example, might datasets peptides, metabolites, lipids. combine together standard long dataset row represents feature, simply run pre_process() function, conduct data preparation yield standard input format demonstrated . Datasets multi-omics, can still utilize dashboard long ensuring data inputs reformatted standard longitudinal format. situation, can manually assign type column category label describe major groups data, input data pre_processing() module data preparation . case study, utilized “Kingdom” type label column cheese data. datasets multi-omics, can simply apply dashboard reformatting data standard longitudinal format. case, manually assign category label type column represent major groups. can still input data pre_processing() module cleaning preparation. case study, used “Kingdom” type label cheese datasets.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"data-format-and-pre-process","dir":"Articles","previous_headings":"Data Format","what":"Data Format and Pre-process","title":"Get started","text":"figure, , B, C can represent datasets different sources different aspects measurements. two examples input data might look like transform required format pre_process() modules.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"if-your-input-is-a-list-of-datasets","dir":"Articles","previous_headings":"Data Format > Data Format and Pre-process","what":"If your input is a list of datasets:","title":"Get started","text":"list datasets, following format: ID column identical column names. pre_process():","code":"str(fungal_dlist) #> List of 3 #> $ :'data.frame': 149786 obs. of 12 variables: #> ..$ ID : chr [1:149786] \"grp1_1\" \"grp1_2\" \"grp1_6\" \"grp1_7\" ... #> ..$ D0_01 : num [1:149786] -0.33 -0.33 1.591 1.106 0.659 ... #> ..$ D1_02 : num [1:149786] 0.628 0.628 3.087 3.618 2.843 ... #> ..$ D2_03 : num [1:149786] -1.223 -1.223 0.846 1.178 1.435 ... #> ..$ D3_04 : num [1:149786] -2.411 -1.808 1.239 1.753 0.258 ... #> ..$ D4_05 : num [1:149786] -1.41 -1.18 2.05 1.64 1.21 ... #> ..$ D5_06 : num [1:149786] -2.245 -2.309 0.83 1.64 0.395 ... #> ..$ D6_07 : num [1:149786] -0.784 -1.125 2.758 2.888 2.516 ... #> ..$ D7_08 : num [1:149786] -2.61197 -2.68724 0.42236 0.92846 0.00892 ... #> ..$ D8_09 : num [1:149786] -0.951 -1.3 2.417 2.454 1.418 ... #> ..$ D9_10 : num [1:149786] -1.91 -2.61 1.33 1.8 2.25 ... #> ..$ D10_11: num [1:149786] -2.871 -3.417 1.101 0.619 1.275 ... #> $ :'data.frame': 150 obs. of 12 variables: #> ..$ ID : chr [1:150] \"grp2_1\" \"grp2_2\" \"grp2_3\" \"grp2_4\" ... #> ..$ D0_01 : num [1:150] 0.947 1.012 0.156 0.31 0.138 ... #> ..$ D1_02 : num [1:150] -1.616 -0.917 -1.581 -2.717 -2.578 ... #> ..$ D2_03 : num [1:150] -2.4 -1.67 -2.31 -3.02 -3.66 ... #> ..$ D3_04 : num [1:150] -0.5734 0.0336 -0.351 -1.3052 -1.737 ... #> ..$ D4_05 : num [1:150] 0.662 0.854 1.002 0.12 -0.371 ... #> ..$ D5_06 : num [1:150] -1.09 -1.28 -1.04 -1.82 -3.15 ... #> ..$ D6_07 : num [1:150] -0.212 0.718 0.189 -0.627 -1.081 ... #> ..$ D7_08 : num [1:150] 0.969 2.117 1.462 0.339 -0.038 ... #> ..$ D8_09 : num [1:150] -0.712 0.43 -0.281 -1.297 -1.822 ... #> ..$ D9_10 : num [1:150] -0.649 0.45 -0.124 -1.77 -1.962 ... #> ..$ D10_11: num [1:150] -1.05 -0.85 -1.58 -1.91 -4.05 ... #> $ :'data.frame': 369 obs. of 12 variables: #> ..$ ID : chr [1:369] \"grp3_1\" \"grp3_2\" \"grp3_3\" \"grp3_4\" ... #> ..$ D0_01 : num [1:369] -0.918 -2.112 -1.372 -0.4 -1.75 ... #> ..$ D1_02 : num [1:369] -1.219 -3.502 -1.504 -0.615 -2.11 ... #> ..$ D2_03 : num [1:369] -0.542 -2.324 -0.592 0.161 -1.342 ... #> ..$ D3_04 : num [1:369] -0.101 -1.495 0.1 0.603 -1.058 ... #> ..$ D4_05 : num [1:369] 1.715 1.557 1.89 2.296 0.653 ... #> ..$ D5_06 : num [1:369] -1.452 -2.37 -1.182 -0.846 -2.531 ... #> ..$ D6_07 : num [1:369] 0.0879 -0.9982 0.4382 0.6215 -0.9464 ... #> ..$ D7_08 : num [1:369] 0.0502 0.4668 0.5378 0.8353 -0.9213 ... #> ..$ D8_09 : num [1:369] -0.0268 -0.4226 0.4528 0.6328 -1.0998 ... #> ..$ D9_10 : num [1:369] -0.5807 -1.7755 -0.1956 0.0866 -1.5876 ... #> ..$ D10_11: num [1:369] 0.118 -0.16 0.687 0.572 -1.037 ... #> ID D0_01 D1_02 D2_03 D3_04 D4_05 D5_06 #> 1 grp1_1 -0.3297741 0.6284682 -1.2232968 -2.411339 -1.413062 -2.2445487 #> 2 grp1_2 -0.3297741 0.6284682 -1.2232968 -1.807533 -1.183498 -2.3088022 #> 3 grp1_6 1.5908054 3.0871284 0.8460835 1.239411 2.054109 0.8300876 #> 4 grp1_7 1.1061795 3.6176250 1.1781012 1.753078 1.643661 1.6400102 #> D6_07 D7_08 D8_09 D9_10 D10_11 #> 1 -0.7839412 -2.6119663 -0.9511467 -1.914273 -2.8711810 #> 2 -1.1251434 -2.6872417 -1.2996614 -2.610647 -3.4165861 #> 3 2.7576288 0.4223618 2.4165602 1.333387 1.1012825 #> 4 2.8877343 0.9284640 2.4538561 1.799019 0.6187648 #> ID D0_01 D1_02 D2_03 D3_04 D4_05 D5_06 #> 1 grp2_1 0.9467431 -1.6162900 -2.400394 -0.57337845 0.6616711 -1.090914 #> 2 grp2_2 1.0120048 -0.9165994 -1.674330 0.03361378 0.8540950 -1.283964 #> 3 grp2_3 0.1561394 -1.5805490 -2.307281 -0.35095093 1.0018741 -1.035030 #> 4 grp2_4 0.3104578 -2.7173471 -3.022886 -1.30521496 0.1198075 -1.816677 #> D6_07 D7_08 D8_09 D9_10 D10_11 #> 1 -0.2116778 0.9688582 -0.7123773 -0.6489204 -1.0504845 #> 2 0.7181970 2.1174460 0.4302981 0.4504066 -0.8503481 #> 3 0.1885422 1.4619677 -0.2805926 -0.1235090 -1.5757556 #> 4 -0.6271954 0.3393109 -1.2967938 -1.7697248 -1.9073101 #> ID D0_01 D1_02 D2_03 D3_04 D4_05 D5_06 #> 1 grp3_1 -0.9178218 -1.2193128 -0.5415780 -0.1007493 1.715495 -1.4515238 #> 2 grp3_2 -2.1120636 -3.5015326 -2.3236475 -1.4949366 1.557463 -2.3698578 #> 3 grp3_3 -1.3724446 -1.5043101 -0.5922380 0.1004978 1.889573 -1.1815469 #> 4 grp3_4 -0.4002445 -0.6145252 0.1613003 0.6031744 2.295751 -0.8464012 #> D6_07 D7_08 D8_09 D9_10 D10_11 #> 1 0.08792489 0.05020749 -0.02681783 -0.58065562 0.1177374 #> 2 -0.99822332 0.46681389 -0.42255125 -1.77554842 -0.1603138 #> 3 0.43822354 0.53780990 0.45276561 -0.19558682 0.6870272 #> 4 0.62145080 0.83528878 0.63275758 0.08663959 0.5720591 data <- pre_process(fungal_dlist, typenameList = c('DataSource_A','DataSource_B','DataSource_C'), replaceNA = TRUE, scale = TRUE, autoColName = \"Section_\") #> [1] \"Reformat a list of datasets:\" head(data,10) #> ID D0_01 D1_02 D2_03 D3_04 D4_05 D5_06 #> 1 grp1_1 1.06146675 1.956656 0.2267383 -0.88312967 0.04945980 -0.7273146 #> 2 grp1_2 1.08465667 1.916983 0.3085454 -0.19892034 0.34311497 -0.6343214 #> 3 grp1_6 -0.01899274 1.717871 -0.8834318 -0.42687491 0.51878884 -0.9019992 #> 4 grp1_7 -0.75869657 2.051470 -0.6782202 -0.03485388 -0.15728499 -0.1613700 #> 5 grp1_8 -0.68132827 1.648062 0.1465161 -1.10875822 -0.09101615 -0.9629427 #> 6 grp1_11 0.27709905 1.981042 -0.5786717 0.02888008 0.54647685 -0.7013406 #> 7 grp1_12 0.90487968 2.180079 -0.6439891 -0.14841544 -0.69040101 -0.3834938 #> 8 grp1_13 0.46335031 1.695812 -1.0938459 -0.76868293 0.74415950 -0.6861036 #> 9 grp1_14 0.72014702 1.708604 -0.9344900 -0.35796963 0.63730277 -0.9872778 #> 10 grp1_15 0.54526798 1.784716 -0.8846753 -0.49538649 0.62916563 -0.9742293 #> D6_07 D7_08 D8_09 D9_10 D10_11 type #> 1 0.6371841 -1.0705560 0.4809808 -0.41877137 -1.31271417 DataSource_A #> 2 0.3938012 -0.9630327 0.2422154 -0.89650312 -1.59653914 DataSource_A #> 3 1.3354026 -1.3752687 0.9395058 -0.31779210 -0.58720856 DataSource_A #> 4 1.2347631 -0.9575502 0.7492778 0.01655139 -1.30408627 DataSource_A #> 5 1.2995756 -1.3741657 0.1285139 1.01932257 -0.02377948 DataSource_A #> 6 1.1406352 -1.6339536 0.2041455 -0.45793129 -0.80638109 DataSource_A #> 7 0.4794959 -1.6340692 0.5360766 -0.31430867 -0.28585374 DataSource_A #> 8 1.2235141 -1.2241931 0.6790383 -0.28487806 -0.74817082 DataSource_A #> 9 1.1466328 -1.4265286 0.5357111 -0.51799361 -0.52413804 DataSource_A #> 10 1.1813273 -1.2944121 0.6417621 -0.46906150 -0.66447461 DataSource_A"},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"if-your-input-is-a-single-dataset","dir":"Articles","previous_headings":"Data Format > Data Format and Pre-process","what":"If your input is a single dataset:","title":"Get started","text":"dataset already includes labeled ‘type’ column: pre_process():","code":"#> ID Day_1 Day_2 Day_3 Day_4 Day_5 Day_6 #> 1 1 NA NA 0.104083550 -1.07999900 1.3416214 -1.733564471 #> 2 2 NA NA 0.882578509 0.08440181 0.7165712 0.004811372 #> 3 3 0.40203917 NA 0.675076590 0.97017740 -0.8210872 0.659073443 #> 4 4 1.02859068 0.30178418 0.412617771 0.39306149 -0.3427640 0.837009714 #> 5 5 -2.14036218 -0.06315709 0.656982200 -0.18301850 -1.3967562 -0.123327229 #> 6 6 -2.04354192 1.30960484 1.371349945 -1.67403067 -1.2946150 0.760315504 #> 7 7 0.31164367 2.26615374 0.972843085 -0.03956293 1.0190965 1.180680430 #> 8 8 -0.79692858 -0.86038791 -3.417872877 0.62118867 -2.1983197 -0.104229763 #> 9 9 -0.12033042 1.17585682 -0.001075426 0.01122359 0.3430992 -0.189234532 #> 10 10 0.02592915 0.48551867 0.841254390 -0.46430744 0.6891800 0.751766756 #> Day_7 Day_8 type #> 1 0.4638261 2.1318716 peptide #> 2 -0.2949926 -0.9310538 peptide #> 3 -0.2520195 0.7809977 peptide #> 4 -1.0120371 -0.1162361 peptide #> 5 -2.0085970 -0.8959521 peptide #> 6 0.7874222 -0.5106016 peptide #> 7 -0.7719437 -1.4686258 peptide #> 8 0.3541200 0.8224521 lipid #> 9 0.5674059 -0.9313882 lipid #> 10 -0.4097464 NA metabolite x1 <- pre_process(x, replaceNA = TRUE,scale = TRUE) head(x1,10) #> ID Day_1 Day_2 Day_3 Day_4 Day_5 Day_6 #> 1 1 -0.12499488 -0.1249949 -0.04022866 -1.00455207 0.9676290 -1.53681943 #> 2 2 -0.10246242 -0.1024624 1.46237249 0.04718422 1.1680372 -0.09393173 #> 3 3 0.16412393 -0.4940273 0.61109508 1.09418472 -1.8381738 0.58489741 #> 4 4 1.27432720 0.1728189 0.34079228 0.31115385 -0.8040231 0.98397755 #> 5 5 -1.35954882 0.7001734 1.41425156 0.58132077 -0.6222015 0.64050965 #> 6 6 -1.35505505 1.0595199 1.10398207 -1.08897287 -0.8157586 0.66398092 #> 7 7 -0.10291149 1.5438734 0.45418630 -0.39882285 0.4931574 0.62930098 #> 8 8 -0.06748422 -0.1105542 -1.84632390 0.89499455 -1.0186109 0.40265173 #> 9 9 -0.36947382 1.7376966 -0.17560476 -0.15561064 0.3839091 -0.48148903 #> 10 10 -0.41003145 0.4704748 1.15201231 -1.34925296 0.8606601 0.98056711 #> Day_7 Day_8 type #> 1 0.2527477 1.6112132 peptide #> 2 -0.6254920 -1.7532453 peptide #> 3 -0.9065915 0.7844915 peptide #> 4 -1.8183370 -0.4607098 peptide #> 5 -1.2288927 -0.1256124 peptide #> 6 0.6835002 -0.2511966 peptide #> 7 -1.0158949 -1.6028889 peptide #> 8 0.7137346 1.0315924 lipid #> 9 0.7485573 -1.6879846 lipid #> 10 -1.2447220 -0.4597079 metabolite"},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"choose-your-annotation","dir":"Articles","previous_headings":"","what":"Choose Your Annotation","title":"Get started","text":"addition specifying data type mentioned , methods support three levels information: functional annotation, taxonomy annotation, feature annotation. annotations matched ID columns annotation data, serving another crucial input generating dashboard. facilitate automatic feature link generation using KeggID GOID, users set corresponding column names beforehand. example annotation formats align data inputs. data two layers labels (like cheese dataset), can specify two layers want focus network using gAnnotation() function.","code":"#> ID taxonomic.scope ko_term Pathway #> 1 grp1_24 Bacteria K02706 00195_Photosynthesis #> 2 grp1_25 Bacteria K02706 00195_Photosynthesis #> 3 grp1_26 Bacteria K02706 00195_Photosynthesis #> 4 grp1_1075 Bacteria K02112 00195_Photosynthesis #> 5 grp1_1076 Bacteria K02112 00195_Photosynthesis #> 6 grp1_1358 Bacteria K02112 00195_Photosynthesis Annotation_path_taxon <- gAnnotation(annotations,\"phylum\",\"class\")"},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"network-generation","dir":"Articles","previous_headings":"","what":"Network Generation","title":"Get started","text":"generate network, first scale cluster trajectories across input molecular features. Trajectories clustered using K-means, built-elbow method used select optimal K. build network clustered trajectories adapting GENIE3 algorithm. Specifically, network learned series regressions, cluster centroid predicted expression patterns cluster centroids using random forests. choose random forests potential model interacting features non-linearity without strong assumptions. determine feature clusters predictive given trajectory, Mean Decrease Accuracy subset top predictors whose expression directly influences expression target cluster taken indication putative link.","code":"cluster_result <- data |> gClusters(ncluster = 5,elbow.max=15) network_result <- cluster_result |> gNetwork(ntop = 3) gNetwork_view(network_result)"},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"dashboard-navigation","dir":"Articles","previous_headings":"","what":"Dashboard Navigation","title":"Get started","text":"workflow inputs related gDashboard function. key function processes data runs dashboard, requiring input previous steps. ’ve launched Shiny dashboard, can zoom make adjustments explore interesting findings within data. effectively navigate dashboard generated MolPad, ’ll follow three main steps:","code":"gDashboard(data, cluster_result, Annotation_path_taxon, network_result, id_colname = \"ko_term\" , id_type = \"KEGG\")"},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"choose-a-primary-functional-annotation-and-adjust-edge-density","dir":"Articles","previous_headings":"Dashboard Navigation","what":"Choose a Primary Functional Annotation and Adjust Edge Density","title":"Get started","text":"Start selecting primary functional annotation available options. , fine-tune edge density adjusting threshold value importance score. Nodes turn bright green indicate clusters containing features related chosen functional annotation.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"explore-the-network","dir":"Articles","previous_headings":"Dashboard Navigation","what":"Explore the Network","title":"Get started","text":"Brushing network unveils patterns taxonomic composition typical trajectories. can also zoom specific taxonomic annotations applying filters.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"investigate-feature-details-and-related-function-annotations","dir":"Articles","previous_headings":"Dashboard Navigation","what":"Investigate Feature Details and Related Function Annotations","title":"Get started","text":"Delve feature table examine specifics features within selected clusters. Explore additional related function annotations using drop-options. Click provided links access online information items interest. interface encourages iterative exploration, enabling conduct multiple steps answer specific questions, comparing pattern distributions two functions identifying functionally important community members metabolizing feature interest.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"related-visualization-outputs","dir":"Articles","previous_headings":"","what":"Related Visualization Outputs","title":"Get started","text":"following visualizations integrated dashboard, ’s need run function plot individually. ’ve linked stacked bar plot ribbon plot network facilitate focus context explorations. setup helps detecting similar longitudinal patterns understanding relationships.","code":""},{"path":[]},{"path":[]},{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/articles/whymolpad.html","id":"why-molpad","dir":"Articles","previous_headings":"","what":"Why MolPad?","title":"Why MolPad?","text":"aim network interpretation, MolPad shows improvements 3 important aspects: Capture groups features share longitudinal patterns functional properties within network. Annotate network complex experimental datasets, way dig specific components notable features. Streamlines exploration modularized functions interactive dashboard. package especially facilitates exploration longitudinal molecular co-expression microbiome experiments using focus-plus-context approach, empowering researchers uncover global patterns insights longitudinal microbiomics data. Unlike existing tools, MolPad stands rich network interactivity support contextualization. also equipped beginner-friendly interface self-contained data processing functions, making accessible users limited programming experience.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/whymolpad.html","id":"key-features","dir":"Articles","previous_headings":"","what":"Key Features","title":"Why MolPad?","text":"Trajectory clustering organizing data clusters, helps identify patterns, relationships, underlying structures within dataset. essence, clustering detects groups features observations behave similarly, allowing deeper insights effective decision-making. Network extracting build network clustered trajectories adapting GENIE3 algorithm. Specifically, network learned series regressions, cluster centroid predicted expression patterns cluster centroids using random forests. choose random forests potential model interacting features non-linearity without strong assumptions. Interactive Dashboard package provides interactive dashboard designed help users explore data uncover relationships within complex networks. utilizing focus-plus-context visualization, dashboard allows users zoom specific details keeping broader dataset view. approach enables deeper understanding data, making easier identify patterns make informed decisions.","code":""},{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/articles/whymolpad.html","id":"molpad-dashboard-overview","dir":"Articles","previous_headings":"What does it look like?","what":"MolPad Dashboard Overview","title":"Why MolPad?","text":"overview MolPad Dashboard. explore dashboard effectively, can start following sequence -B-C-D. approach guide cluster-level network, taxonomic-level bar plot, type-level line plot, feature-level table. , can modify detailed settings identify investigate special patterns within data.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/whymolpad.html","id":"discovering-related-patterns","dir":"Articles","previous_headings":"What does it look like?","what":"Discovering related patterns","title":"Why MolPad?","text":"short example discovering related patterns using network plot: shade edges represents proximity nodes. brushed area, Groups 1-7-8 (circled solid black lines) Groups 1-2 (circled blue dashed lines) strongly linked. Groups 1, 7, 8, patterns form W-shape noticeable peak time section. Although Group 1 shows higher volatility compared Group 2, groups display highly overlapping increasing trend. detailed instructions, please visit “Get Started” page relevant function reference page.","code":""},{"path":"https://kaiyanm.github.io/MolPad/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Kaiyan Ma. Author, maintainer.","code":""},{"path":"https://kaiyanm.github.io/MolPad/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Ma K (2024). MolPad: MolPad: R-Shiny Package Cluster Co-Expression Analysis Longitudinal Microbiomics. R package version 0.1.0, https://kaiyanm.github.io/MolPad/.","code":"@Manual{, title = {MolPad: MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics}, author = {Kaiyan Ma}, year = {2024}, note = {R package version 0.1.0}, url = {https://kaiyanm.github.io/MolPad/}, }"},{"path":"https://kaiyanm.github.io/MolPad/index.html","id":"molpad-","dir":"","previous_headings":"","what":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","title":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","text":"R-Shiny Package Cluster Co-Expression Analysis Longitudinal Microbiomics","code":""},{"path":"https://kaiyanm.github.io/MolPad/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","text":"MolPad offers visualization dashboard tool designed enhance understanding molecular co-expression works context microbiome data. approach involves using cluster network provide initial overview relationships across multiple omics, added functionality interactively zoom specific areas interest. facilitate analysis, ’ve developed focus-plus-context strategy connects online curated annotations. Additionally, package simplifies entire pipeline creating dashboard. user-friendly design makes accessible even people limited R programming experience. Check cheese-data demo .","code":""},{"path":"https://kaiyanm.github.io/MolPad/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","text":"can either install devtools, {r, eval = FALSE} # Install package R: install.packages(\"devtools\") library(devtools) install_github(\"KaiyanM/MolPad\") clone repository local computer (example, onto ./Github) installing: {r, eval = FALSE} # Download install package R: setwd(\"./GitHub\") install(\"MolPad\") , load package: {r,eval=FALSE} library(MolPad)","code":""},{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/index.html","id":"molpad-could-help-you-with","dir":"","previous_headings":"Usage","what":"MolPad could help you with:","title":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","text":"Clustering data k-means building group network. Find significant trend patterns datasets. Target interaction groups, taxons, pathways. Visualize distribution features specific pathways group network. Search particular features user-defined labels. Check detailed information feature automatically generated hyperlinks. better overall understanding datasets.","code":""},{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/index.html","id":"getting-help","dir":"","previous_headings":"","what":"Getting Help","title":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","text":"need assistance MolPad, two primary ways seek help: Ask us anything related MolPad! add question, create issue repository. Stack Overflow another excellent resource answering common issues R. Remember ’s particularly effective can provide reproducible example shows specific problem ’re .","code":""},{"path":"https://kaiyanm.github.io/MolPad/index.html","id":"contribution","dir":"","previous_headings":"","what":"Contribution","title":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","text":"contribute project, use following workflow: fork repository –> create local copy –> submit pull request.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":null,"dir":"Reference","previous_headings":"","what":"Cheese data — cheese","title":"Cheese data — cheese","text":"context cheese production, regular application brine solution maturation technique promotes uniformity microbial populations cheese's surface facilitates interactions among microorganisms. investigation involved analysis longitudinal dataset encompassing three washed-rind cheese communities sampled ripening process.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Cheese data — cheese","text":"two datasets: cheese annotations","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Cheese data — cheese","text":"Reference: doi:10.1128/msystems.00701-22","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Cheese data — cheese","text":"study uncovered remarkably consistent microbial progression within cheese. bacterial community, Firmicutes dominate outset, Proteobacteria swiftly assuming dominance end ripening period. Additionally, Cheese Cheese C consistently demonstrate establishment Actinobacteria Bacteroidetes, distinct manner. corroborate findings using MolPad dashboard, conducted analysis two cheeses (C) three production batches, spanning weeks 2 13.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":"cheese-data","dir":"Reference","previous_headings":"","what":"cheese data","title":"Cheese data — cheese","text":"data frame 106239 rows 18 variables: ID sample ID A_1~A_5 Time series data measured cheese . C_1~C_5 Time series data measured cheese C. domain category feature phylum category feature class category feature order category feature family category feature genus category feature","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":"annotation-data","dir":"Reference","previous_headings":"","what":"annotation data","title":"Cheese data — cheese","text":"data frame 86156 rows 9 variables: ID sample ID GO_ID GO IDs, represents link gene product type molecular function KEGG_ID KEGG IDs, linking genomic information higher order functional information. domain category feature phylum category feature class category feature order category feature family category feature genus category feature","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cheese data — cheese","text":"","code":"data(cheese) head(annotations) #> # A tibble: 6 × 9 #> ID GO_ID KEGG_ID domain phylum class order family genus #> #> 1 9 - - Bacteria Pseudomonadota Alphaproteobac… Caul… Caulo… Caul… #> 2 10 - - Bacteria Pseudomonadota Alphaproteobac… Caul… Caulo… Caul… #> 3 11 - - Bacteria Pseudomonadota Alphaproteobac… Hyph… Barto… Bart… #> 4 12 - - Bacteria Pseudomonadota Alphaproteobac… Hyph… Bruce… Bruc… #> 5 13 - - Bacteria Pseudomonadota Alphaproteobac… Hyph… Nitro… Brad… #> 6 14 - - Bacteria Pseudomonadota Alphaproteobac… Hyph… Phyll… Meso… head(cheese) #> # A tibble: 6 × 18 #> ID A_1 A_2 A_3 A_4 A_5 C_1 C_3 C_4 C_5 GO_ID KEGG_ID #> #> 1 1 38 23 27 5 11 13 1 3 0 NA NA #> 2 2 23 6 4 0 9 21 1 0 2 NA NA #> 3 3 24 5 37 10 19 3 0 1 4 NA NA #> 4 4 3 2 4 2 16 56 1 7 17 NA NA #> 5 5 58 14 45 13 32 82 3 8 3 NA NA #> 6 6 12 9 14 4 13 2 0 1 4 NA NA #> # ℹ 6 more variables: domain , phylum , class , order , #> # family , genus "},{"path":"https://kaiyanm.github.io/MolPad/reference/color_palettes__.html","id":null,"dir":"Reference","previous_headings":"","what":"Color palettes — color_palettes__","title":"Color palettes — color_palettes__","text":"internal function built-color palettes.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/color_palettes__.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Color palettes — color_palettes__","text":"","code":"color_palettes__(name)"},{"path":"https://kaiyanm.github.io/MolPad/reference/color_palettes__.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Color palettes — color_palettes__","text":"name string two options: \"graytone\" \"darkwarm\".","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/color_palettes__.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Color palettes — color_palettes__","text":"","code":"color_palettes__(\"graytone\") #> [1] \"#c3c5c7\" \"#30beba\" \"#998a73\" \"#807566\" \"#a5b1c9\" \"#5d5232\" \"#9a2b41\" #> [8] \"#d6b7a2\" \"#882db4\" \"#b47a53\" color_palettes__(\"darkwarm\") #> [1] \"#251305\" \"#C70A80\" \"#FBCB0A\" \"#ff1122\" \"#7D7463\" \"#CECE5A\" \"#FF9B9B\" #> [8] \"#A459D1\" \"#00235B\" \"#7E1717\""},{"path":"https://kaiyanm.github.io/MolPad/reference/convert_range.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert range — convert_range","title":"Convert range — convert_range","text":"internal function range conversion. element x, returns distance minimal values divided range.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/convert_range.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert range — convert_range","text":"","code":"convert_range(x)"},{"path":"https://kaiyanm.github.io/MolPad/reference/convert_range.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert range — convert_range","text":"x vector numbers.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/convert_range.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert range — convert_range","text":"vector calculated (x - min(x)) / (max(x) - min(x))","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/convert_range.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert range — convert_range","text":"","code":"convert_range(5:10) #> [1] 0.0 0.2 0.4 0.6 0.8 1.0"},{"path":"https://kaiyanm.github.io/MolPad/reference/extend.color__.html","id":null,"dir":"Reference","previous_headings":"","what":"Extend color palettes — extend.color__","title":"Extend color palettes — extend.color__","text":"internal function designed pair vectors color palettes automatically generate colors longer vectors find match.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/extend.color__.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extend color palettes — extend.color__","text":"","code":"extend.color__(n, colors, extendby = 1, alpha = 1, ...)"},{"path":"https://kaiyanm.github.io/MolPad/reference/extend.color__.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extend color palettes — extend.color__","text":"n number. length feature vector want match colors. colors vector colors (finite). extendby number select 1, 2, 3, 4 5, representing distinct auto-fill schemes. alpha number select range 0,1.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gAnnotation.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate processed annotation — gAnnotation","title":"Generate processed annotation — gAnnotation","text":"gAnnotation() provides standard input format dashboard, allowing users select two columns primary factors wish visualize describe data.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gAnnotation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate processed annotation — gAnnotation","text":"","code":"gAnnotation(data, first_order, second_order)"},{"path":"https://kaiyanm.github.io/MolPad/reference/gAnnotation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate processed annotation — gAnnotation","text":"data data.frame containing annotations used describing features measured time point. also required include ID least two categorical variables. first_order string. name one column categorical variable data. second_order string. name another column categorical variable data.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gAnnotation.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate processed annotation — gAnnotation","text":"Guidelines Selecting Annotations: first-order annotation recommended functional, pathway functional system. parameter primarily serve purpose filtering one network time displaying dashboard. second-order annotation utilized illustrate composition first-order annotation using bar plot. Therefore, better set taxon, class label, etc.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gAnnotation.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate processed annotation — gAnnotation","text":"","code":"data(test_data) test_annotations_processed <- gAnnotation(test_annotations,system,class) head(test_annotations_processed) #> ID #> 1 1 #> 2 2 #> 3 3 #> 4 4 #> 5 5 #> 6 6 #> GO_ID #> 1 #> 2 #> 3 GO:0003674,GO:0003824,GO:0005575,GO:0006629,GO:0006644,GO:0006650,GO:0006655,GO:0006793,GO:0006796,GO:0008150,GO:0008152,GO:0008610,GO:0008654,GO:0008808,GO:0009058,GO:0009987,GO:0016020,GO:0016740,GO:0016772,GO:0016780,GO:0019637,GO:0030572,GO:0032048,GO:0032049,GO:0044237,GO:0044238,GO:0044249,GO:0044255,GO:0045017,GO:0046471,GO:0046474,GO:0046486,GO:0071704,GO:0090407,GO:1901576 #> 4 GO:0003674,GO:0003676,GO:0003723,GO:0003729,GO:0003824,GO:0004654,GO:0005488,GO:0005575,GO:0005622,GO:0005623,GO:0005737,GO:0006139,GO:0006401,GO:0006402,GO:0006725,GO:0006807,GO:0008150,GO:0008152,GO:0009056,GO:0009057,GO:0009892,GO:0009987,GO:0010468,GO:0010605,GO:0010629,GO:0016070,GO:0016071,GO:0016740,GO:0016772,GO:0016779,GO:0019222,GO:0019439,GO:0034641,GO:0034655,GO:0043170,GO:0044237,GO:0044238,GO:0044248,GO:0044260,GO:0044265,GO:0044270,GO:0044424,GO:0044464,GO:0046483,GO:0046700,GO:0048519,GO:0050789,GO:0060255,GO:0065007,GO:0071704,GO:0090304,GO:0097159,GO:1901360,GO:1901361,GO:1901363,GO:1901575 #> 5 #> 6 #> KEGG_ID Pathway taxonomic.scope #> 1 K07124 Integumentary System hormonal proteins #> 2 Skeletal System structural proteins #> 3 K06131 Muscular System enzymes #> 4 K00962 Nervous System contractile proteins #> 5 K02083 Endocrine System contractile proteins #> 6 Cardiovascular System structural proteins"},{"path":"https://kaiyanm.github.io/MolPad/reference/gClusters.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate clusters — gClusters","title":"Generate clusters — gClusters","text":"gClusters() returns clusters generated k-means yield elbow plot way finding optimal parameter.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gClusters.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate clusters — gClusters","text":"","code":"gClusters(data, ncluster = 20, elbow.max = 50, ...)"},{"path":"https://kaiyanm.github.io/MolPad/reference/gClusters.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate clusters — gClusters","text":"data scaled data.frame contain variables ID, value time_1, ..., value time_k, type extracting patterns across time. See also pre_process(). ncluster number clusters. related complexity information network: choosing ncluster, suggest thinking many nodes show visualization representative want clustered pattern. elbow.max number maximum value x-axis elbow method plot. larger expected ncluster smaller sample size. iter.max number maximum iterations allowed k-means. See also stats::kmeans. nstart number random attempts generating initial configurations. k-means algorithm choose best one among attempts. larger data, 'nstart' can set lower just set 1. See also stats::kmeans.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gClusters.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate clusters — gClusters","text":"function return list 2 elements: k-means cluster result elbow method plot.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gClusters.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate clusters — gClusters","text":"determine optimal number clusters (ncluster), advised closely examine elbow plot identify point graph substantial change 'elbow' occurs. often indicative suitable cluster count. cases dataset extensive intricate, might consider increasing value elbow.max ensure comprehensive exploration potential cluster counts. can help achieving accurate meaningful results, especially working larger complex datasets. function can executed data parameter outset. However, achieve best clustering results, adjustments recommended. initial run, users expected adjust function's parameters based clustering outcomes elbow plot analysis.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gClusters.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate clusters — gClusters","text":"","code":"data(test_data) reslist <- gClusters(test_data_processed) # k-means result reslist[[1]] #> K-means clustering with 20 clusters of sizes 7, 6, 7, 10, 8, 2, 3, 5, 4, 6, 10, 3, 3, 4, 4, 6, 4, 3, 2, 3 #> #> Cluster means: #> T1 T2 T3 T4 T5 T6 #> 1 2.22789830 0.02950883 -0.75836275 -0.2619747 -0.5953255 -0.70974025 #> 2 2.77349939 -0.21724464 -0.35972402 -0.4172516 -0.5000122 -0.11678355 #> 3 0.93534606 -0.39771771 0.31238452 1.1531782 1.1995301 -1.05203742 #> 4 -0.27715339 -0.39760069 -0.06466866 1.3995407 2.1316423 -0.28068841 #> 5 0.11559709 -0.19000668 -0.25629794 -0.1487697 -0.4383348 2.58301481 #> 6 -0.27847690 -0.82258408 -0.82258408 -0.8225841 1.2338761 -0.82258408 #> 7 0.26998015 -0.08094677 -0.28926312 -0.7813127 -0.6065051 -0.14945230 #> 8 -0.81355388 -0.84696982 -0.32573017 1.1218534 2.0955812 -0.56207309 #> 9 1.45173820 0.26236463 1.31600581 -0.2531012 0.2004547 -1.12389420 #> 10 0.34700978 0.13673201 0.82464908 0.2571569 1.2656031 0.63541302 #> 11 -0.19020083 -0.69654961 -0.66836949 -0.4657663 -0.3851487 -0.55904213 #> 12 1.62582942 1.51619556 -0.35316244 0.2978109 -0.1777508 0.02299732 #> 13 -0.09187847 -0.87257293 -0.87257293 -0.8725729 -0.5696817 2.05668333 #> 14 0.85679806 -0.37993358 -0.46099690 0.3934155 0.5857582 1.85027957 #> 15 1.12205113 0.09185659 -0.89815993 -0.7233153 -0.5717924 -0.86037548 #> 16 2.09193480 -0.33320285 -0.50786563 -0.5579659 -0.5579659 1.54559773 #> 17 1.56599593 -0.49119311 -0.83000857 -0.6160199 -0.2092753 1.19151568 #> 18 0.89463785 0.01698143 -0.57023879 -1.1815834 -1.1815834 1.18824796 #> 19 -0.13543375 -0.90710305 -0.66248003 -0.4178570 -0.6624800 1.37010464 #> 20 1.31631350 -0.76059916 -0.76059916 1.5193444 0.6006058 0.84385791 #> T7 T8 T9 T10 #> 1 -0.567081109 0.132060988 0.03929621 0.46372005 #> 2 -0.357532815 -0.298895212 -0.29119673 -0.21485863 #> 3 -1.222209771 -0.334925678 -0.36853535 -0.22501291 #> 4 -0.548494719 -0.646992821 -0.66392148 -0.65166279 #> 5 -0.006327004 -0.327677972 -0.66559889 -0.66559889 #> 6 -0.822584085 1.233876127 1.23387613 0.68976894 #> 7 -0.763902351 -0.412975440 0.39220455 2.42217307 #> 8 -0.651292750 -0.498839228 0.42446013 0.05656427 #> 9 -0.987867401 -0.340013670 -0.33085684 -0.19483004 #> 10 -1.004339966 -0.620506186 -0.71949086 -1.12222690 #> 11 -0.566522777 0.002653353 1.73917982 1.78976662 #> 12 -0.770498738 -0.620423681 -0.77049874 -0.77049874 #> 13 0.882337606 0.086536293 0.12686085 0.12686085 #> 14 -0.526130916 -0.896529515 -0.89652951 -0.52613092 #> 15 -0.822591022 0.091471523 1.00908283 1.56177203 #> 16 -0.425513174 -0.557965933 -0.30571480 -0.39133827 #> 17 0.774154665 0.196302112 -0.62663627 -0.95483532 #> 18 0.894637851 -0.836550610 0.41343688 0.36201421 #> 19 -0.907103054 -0.417856999 1.37010464 1.37010464 #> 20 -0.666108041 -0.666108041 -0.66610804 -0.76059916 #> #> Clustering vector: #> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 #> 11 16 6 10 10 11 13 12 4 8 3 7 15 2 3 8 5 11 9 1 #> 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 #> 17 11 5 16 2 8 20 1 5 4 17 4 10 6 4 10 3 15 11 12 #> 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 #> 5 16 11 11 14 11 5 14 16 5 1 17 18 15 5 17 13 4 1 4 #> 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 #> 3 14 20 15 10 16 2 14 9 9 7 8 5 12 7 4 3 4 3 20 #> 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 #> 3 19 1 13 19 16 9 2 18 2 4 18 4 1 11 10 8 2 11 1 #> #> Within cluster sum of squares by cluster: #> [1] 13.8467580 2.0713631 15.4102772 5.9461649 8.0412334 0.9921913 #> [7] 3.1842269 3.4617088 9.9418120 17.6305488 8.0515257 4.9390800 #> [13] 4.0041862 7.3057948 4.7723673 3.0260293 7.3290085 5.6033175 #> [19] 0.9549143 2.4583646 #> (between_SS / total_SS = 83.4 %) #> #> Available components: #> #> [1] \"cluster\" \"centers\" \"totss\" \"withinss\" \"tot.withinss\" #> [6] \"betweenss\" \"size\" \"iter\" \"ifault\" # elbow plot reslist[[2]]"},{"path":"https://kaiyanm.github.io/MolPad/reference/gDashboard.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate shiny dashboard — gDashboard","title":"Generate shiny dashboard — gDashboard","text":"outputs g-functions, ready create custom Molpad dashboard. sure specify web ID columns corresponding column names.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gDashboard.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate shiny dashboard — gDashboard","text":"","code":"gDashboard( data, cluster, annotation, networkres, dashboardtitle = \"MolPad Dashboard\", id_colname = NULL, id_type = NULL )"},{"path":"https://kaiyanm.github.io/MolPad/reference/gDashboard.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate shiny dashboard — gDashboard","text":"data output pre_process():scaled data.frame contain variables ID, value time_1, ..., value time_k, type. cluster output gClusters(): list contains result k-means Cluster means, vectors sum squares. annotation output gAnnotation(): data.frame containing annotations describing features. Variables must include ID, Pathway, taxonomic.scope. Note NA permitted 3 variables. networkres output gNetwork(): data.frame 4 variables weight,IncNodePurity,var_names,. dashboardtitle string customized dashboard name. id_colname single string string vector. column names annotation dataset vector contains external database IDs. id_type single string string vector. corresponding database names columns, must chosen \"KEGG\" \"GO\".","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gDashboard.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate shiny dashboard — gDashboard","text":"Please ensure columns containing external database IDs adhere following standards: KEGG ID: Begin 'K' followed 5 digits, example, K05685 K06671. GO ID: Begin 'GO:' followed 7 digits, GO:0003674.\"","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gDashboard.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate shiny dashboard — gDashboard","text":"","code":"if (FALSE) { data(test_data) gDashboard(test_data_processed,test_cluster,test_annotations_processed,test_network,id_colname = c(\"GO_ID\",\"KEGG_ID\"),id_type = c(\"GO\",\"KEGG\")) }"},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate prediction network — gNetwork","title":"Generate prediction network — gNetwork","text":"gNetwork() generates prediction network functional annotation. every feature, features considered independent variables, top predictors selected based %IncMSE.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate prediction network — gNetwork","text":"","code":"gNetwork(clusters, ntop = 10, method = \"randomforest\")"},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate prediction network — gNetwork","text":"clusters list two outputs gClusters(). first element k-means result (See also stats::kmeans), element plot automatically omitted ease directly passing results function. ntop number pick set top n predictors(default: top 10) feature important ones. used construct network among clustered patterns. seed number random seed.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate prediction network — gNetwork","text":"assess relationships clusters using Mean Squared Error (MSE) changes resulting random shuffling. gNetwork()'s output includes edge weights node pairs, essential inputs dashboard.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate prediction network — gNetwork","text":"","code":"data(test_data) networkres <- gNetwork(test_cluster, ntop = 3) head(networkres) #> weight IncNodePurity var_names from #> 1 1.6429045 0.7371260 Group_3 Group_1 #> 2 0.4588961 0.9310697 Group_5 Group_1 #> 3 -0.7833169 0.6761286 Group_2 Group_1 #> 4 5.0076415 0.8821582 Group_5 Group_2 #> 5 4.1618215 1.1976058 Group_4 Group_2 #> 6 2.5714583 1.2820391 Group_1 Group_2 gNetwork_view(networkres)"},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork_view.html","id":null,"dir":"Reference","previous_headings":"","what":"View the feature importance plot — gNetwork_view","title":"View the feature importance plot — gNetwork_view","text":"gNetwork_view() Generate feature importance plot random forest result. plot visually highlights importance individual cluster within dataset, helping identify key factors predictive modeling.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork_view.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"View the feature importance plot — gNetwork_view","text":"","code":"gNetwork_view(data)"},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork_view.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"View the feature importance plot — gNetwork_view","text":"data dataframe; output gNetwork(), including 4 variables: weight, IncNodePurity, var_names . weight edge value, IncNodePurity(%IncMSE) measure predictions result var_names permuted. dependent variable round random forest.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork_view.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"View the feature importance plot — gNetwork_view","text":"","code":"data(test_data) gNetwork_view(test_network) #> Warning: Removed 1 rows containing missing values (`geom_segment()`). #> Warning: Removed 1 rows containing missing values (`geom_point()`)."},{"path":"https://kaiyanm.github.io/MolPad/reference/make_line_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Make line plot — make_line_plot","title":"Make line plot — make_line_plot","text":"Generate line ribbon plot dashboard.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_line_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make line plot — make_line_plot","text":"","code":"make_line_plot(dfgroup_long, selected_groups, selected_taxa)"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_line_plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Make line plot — make_line_plot","text":"function makes ribbon plot every brush action Shiny show min, max, mean value clustered pattern group across time. ribbon plot grouped colored type variable input datasets. See also ggplot2::geom_ribbon.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_line_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Make line plot — make_line_plot","text":"","code":"data(test_data) make_line_plot(test_maindata, \"Group_5\", c(\"hormonal proteins\",\"structural proteins\",\"enzymes\",\"storage proteins\",\"antibodies\",\"transport proteins\"))"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_stackbar_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Make stackbar plot — make_stackbar_plot","title":"Make stackbar plot — make_stackbar_plot","text":"Generate stackbar plot dashboard.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_stackbar_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make stackbar plot — make_stackbar_plot","text":"","code":"make_stackbar_plot(dfgroup_long, selected_groups, selected_taxa)"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_stackbar_plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Make stackbar plot — make_stackbar_plot","text":"function makes bar plot every brush action Shiny show components clustered pattern group. bar plot colored taxonomic.scope variable processed annotation dataset generated gAnnotation(). See also ggplot2::geom_bar.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_stackbar_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Make stackbar plot — make_stackbar_plot","text":"","code":"data(test_data) make_stackbar_plot(test_maindata, \"Group_5\", c(\"hormonal proteins\",\"structural proteins\",\"enzymes\",\"storage proteins\",\"antibodies\",\"transport proteins\"))"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_graph.html","id":null,"dir":"Reference","previous_headings":"","what":"Make graph plot — make_the_graph","title":"Make graph plot — make_the_graph","text":"Generate graph dashboard.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_graph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make graph plot — make_the_graph","text":"","code":"make_the_graph(ptw, network_output, min_weight, s_ptw, graph_layout)"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_graph.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Make graph plot — make_the_graph","text":"function makes network plot every Pathway selection Shiny show relationship among clustered pattern groups. network plot built dataset generated gNetwork() can adjusted layout minimum weights. See also ggraph::ggraph.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_graph.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Make graph plot — make_the_graph","text":"","code":"data(test_data) make_the_graph(test_graphptw, test_network, 0.03, \"Muscular System\",\"kk\")"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_table.html","id":null,"dir":"Reference","previous_headings":"","what":"Make table from brushed region — make_the_table","title":"Make table from brushed region — make_the_table","text":"Generate table selected groups dashboard.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make table from brushed region — make_the_table","text":"","code":"make_the_table(p)"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Make table from brushed region — make_the_table","text":"p ggplot output, see also ggplot2::ggplot_build.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_table.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Make table from brushed region — make_the_table","text":"function simply aims collect position information brushed area network plot returns annotation table corresponding features. takes plot object produce object information table.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/mass_produce_lm__.html","id":null,"dir":"Reference","previous_headings":"","what":"Mass produce linear model — mass_produce_lm__","title":"Mass produce linear model — mass_produce_lm__","text":"Take column dependent vairable time, produce n linear models n columns dataset.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/mass_produce_lm__.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mass produce linear model — mass_produce_lm__","text":"","code":"mass_produce_lm__(data)"},{"path":"https://kaiyanm.github.io/MolPad/reference/mass_produce_lm__.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mass produce linear model — mass_produce_lm__","text":"data dataframe.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/mass_produce_lm__.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Mass produce linear model — mass_produce_lm__","text":"internal function designed automatically generate list functions regression. column considered response variable columns. resulting functions can utilized inputs random forest regression gNetwork().","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/match.color__.html","id":null,"dir":"Reference","previous_headings":"","what":"Match color — match.color__","title":"Match color — match.color__","text":"Match vector finite list colors.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/match.color__.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Match color — match.color__","text":"","code":"match.color__(valist, mycolors, extendby = 5)"},{"path":"https://kaiyanm.github.io/MolPad/reference/match.color__.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Match color — match.color__","text":"valist vector want specify colors element. mycolors vector colors. extendby number select 1, 2, 3, 4, 5, representing distinct auto-fill schemes.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/match.color__.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Match color — match.color__","text":"","code":"my_vector <- paste0(\"N_\",1:10) match.color__(my_vector,c(\"red\",\"yellow\",\"blue\")) #> N_1 N_2 N_3 N_4 N_5 #> \"red\" \"yellow\" \"blue\" \"white\" \"antiquewhite3\" #> N_6 N_7 N_8 N_9 N_10 #> \"aquamarine3\" \"azure3\" \"bisque2\" \"blue\" \"blueviolet\""},{"path":"https://kaiyanm.github.io/MolPad/reference/match_database.html","id":null,"dir":"Reference","previous_headings":"","what":"Match database — match_database","title":"Match database — match_database","text":"matches selected columns online database names.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/match_database.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Match database — match_database","text":"","code":"match_database(data, id_colname, id_type)"},{"path":"https://kaiyanm.github.io/MolPad/reference/match_database.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Match database — match_database","text":"match_database() internal function matches selected columns dataset corresponding databases. Currently MolPad support two types annotation database: GO KEGG. want custom URL platform, download modify paste_URL() function.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/match_database.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Match database — match_database","text":"","code":"data(test_data) head(test_annotations_processed) #> ID #> 1 1 #> 2 2 #> 3 3 #> 4 4 #> 5 5 #> 6 6 #> GO_ID #> 1 #> 2 #> 3 GO:0003674,GO:0003824,GO:0005575,GO:0006629,GO:0006644,GO:0006650,GO:0006655,GO:0006793,GO:0006796,GO:0008150,GO:0008152,GO:0008610,GO:0008654,GO:0008808,GO:0009058,GO:0009987,GO:0016020,GO:0016740,GO:0016772,GO:0016780,GO:0019637,GO:0030572,GO:0032048,GO:0032049,GO:0044237,GO:0044238,GO:0044249,GO:0044255,GO:0045017,GO:0046471,GO:0046474,GO:0046486,GO:0071704,GO:0090407,GO:1901576 #> 4 GO:0003674,GO:0003676,GO:0003723,GO:0003729,GO:0003824,GO:0004654,GO:0005488,GO:0005575,GO:0005622,GO:0005623,GO:0005737,GO:0006139,GO:0006401,GO:0006402,GO:0006725,GO:0006807,GO:0008150,GO:0008152,GO:0009056,GO:0009057,GO:0009892,GO:0009987,GO:0010468,GO:0010605,GO:0010629,GO:0016070,GO:0016071,GO:0016740,GO:0016772,GO:0016779,GO:0019222,GO:0019439,GO:0034641,GO:0034655,GO:0043170,GO:0044237,GO:0044238,GO:0044248,GO:0044260,GO:0044265,GO:0044270,GO:0044424,GO:0044464,GO:0046483,GO:0046700,GO:0048519,GO:0050789,GO:0060255,GO:0065007,GO:0071704,GO:0090304,GO:0097159,GO:1901360,GO:1901361,GO:1901363,GO:1901575 #> 5 #> 6 #> KEGG_ID Pathway taxonomic.scope #> 1 K07124 Integumentary System hormonal proteins #> 2 Skeletal System structural proteins #> 3 K06131 Muscular System enzymes #> 4 K00962 Nervous System contractile proteins #> 5 K02083 Endocrine System contractile proteins #> 6 Cardiovascular System structural proteins head(match_database(test_annotations_processed,id_colname = c(\"GO_ID\",\"KEGG_ID\"),id_type = c(\"GO\",\"KEGG\"))) #> ID #> 1 1 #> 2 2 #> 3 3 #> 4 4 #> 5 5 #> 6 6 #> GO_ID #> 1 #> 2 #> 3 GO:0003674<\/a>
GO:0003824<\/a>
GO:0005575<\/a>
GO:0006629<\/a>
GO:0006644<\/a>
GO:0006650<\/a>
GO:0006655<\/a>
GO:0006793<\/a>
GO:0006796<\/a>
GO:0008150<\/a>
GO:0008152<\/a>
GO:0008610<\/a>
GO:0008654<\/a>
GO:0008808<\/a>
GO:0009058<\/a>
GO:0009987<\/a>
GO:0016020<\/a>
GO:0016740<\/a>
GO:0016772<\/a>
GO:0016780<\/a>
GO:0019637<\/a>
GO:0030572<\/a>
GO:0032048<\/a>
GO:0032049<\/a>
GO:0044237<\/a>
GO:0044238<\/a>
GO:0044249<\/a>
GO:0044255<\/a>
GO:0045017<\/a>
GO:0046471<\/a>
GO:0046474<\/a>
GO:0046486<\/a>
GO:0071704<\/a>
GO:0090407<\/a>
GO:1901576<\/a> #> 4 GO:0003674<\/a>
GO:0003676<\/a>
GO:0003723<\/a>
GO:0003729<\/a>
GO:0003824<\/a>
GO:0004654<\/a>
GO:0005488<\/a>
GO:0005575<\/a>
GO:0005622<\/a>
GO:0005623<\/a>
GO:0005737<\/a>
GO:0006139<\/a>
GO:0006401<\/a>
GO:0006402<\/a>
GO:0006725<\/a>
GO:0006807<\/a>
GO:0008150<\/a>
GO:0008152<\/a>
GO:0009056<\/a>
GO:0009057<\/a>
GO:0009892<\/a>
GO:0009987<\/a>
GO:0010468<\/a>
GO:0010605<\/a>
GO:0010629<\/a>
GO:0016070<\/a>
GO:0016071<\/a>
GO:0016740<\/a>
GO:0016772<\/a>
GO:0016779<\/a>
GO:0019222<\/a>
GO:0019439<\/a>
GO:0034641<\/a>
GO:0034655<\/a>
GO:0043170<\/a>
GO:0044237<\/a>
GO:0044238<\/a>
GO:0044248<\/a>
GO:0044260<\/a>
GO:0044265<\/a>
GO:0044270<\/a>
GO:0044424<\/a>
GO:0044464<\/a>
GO:0046483<\/a>
GO:0046700<\/a>
GO:0048519<\/a>
GO:0050789<\/a>
GO:0060255<\/a>
GO:0065007<\/a>
GO:0071704<\/a>
GO:0090304<\/a>
GO:0097159<\/a>
GO:1901360<\/a>
GO:1901361<\/a>
GO:1901363<\/a>
GO:1901575<\/a> #> 5 #> 6 #> KEGG_ID #> 1 K07124<\/a> #> 2 #> 3 K06131<\/a> #> 4 K00962<\/a> #> 5 K02083<\/a> #> 6 #> Pathway taxonomic.scope #> 1 Integumentary System hormonal proteins #> 2 Skeletal System structural proteins #> 3 Muscular System enzymes #> 4 Nervous System contractile proteins #> 5 Endocrine System contractile proteins #> 6 Cardiovascular System structural proteins"},{"path":"https://kaiyanm.github.io/MolPad/reference/paste_URL.html","id":null,"dir":"Reference","previous_headings":"","what":"Paste URL — paste_URL","title":"Paste URL — paste_URL","text":"Retrieve database IDs associate respective URLs.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/paste_URL.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Paste URL — paste_URL","text":"","code":"paste_URL(x, id_type)"},{"path":"https://kaiyanm.github.io/MolPad/reference/paste_URL.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Paste URL — paste_URL","text":"","code":"data(test_data) paste_URL(test_annotations$GO_ID[1:4], id_type = \"GO\") #> [1] NA #> [2] NA #> [3] \"GO:0003674<\/a>
GO:0003824<\/a>
GO:0005575<\/a>
GO:0006629<\/a>
GO:0006644<\/a>
GO:0006650<\/a>
GO:0006655<\/a>
GO:0006793<\/a>
GO:0006796<\/a>
GO:0008150<\/a>
GO:0008152<\/a>
GO:0008610<\/a>
GO:0008654<\/a>
GO:0008808<\/a>
GO:0009058<\/a>
GO:0009987<\/a>
GO:0016020<\/a>
GO:0016740<\/a>
GO:0016772<\/a>
GO:0016780<\/a>
GO:0019637<\/a>
GO:0030572<\/a>
GO:0032048<\/a>
GO:0032049<\/a>
GO:0044237<\/a>
GO:0044238<\/a>
GO:0044249<\/a>
GO:0044255<\/a>
GO:0045017<\/a>
GO:0046471<\/a>
GO:0046474<\/a>
GO:0046486<\/a>
GO:0071704<\/a>
GO:0090407<\/a>
GO:1901576<\/a>\" #> [4] \"GO:0003674<\/a>
GO:0003676<\/a>
GO:0003723<\/a>
GO:0003729<\/a>
GO:0003824<\/a>
GO:0004654<\/a>
GO:0005488<\/a>
GO:0005575<\/a>
GO:0005622<\/a>
GO:0005623<\/a>
GO:0005737<\/a>
GO:0006139<\/a>
GO:0006401<\/a>
GO:0006402<\/a>
GO:0006725<\/a>
GO:0006807<\/a>
GO:0008150<\/a>
GO:0008152<\/a>
GO:0009056<\/a>
GO:0009057<\/a>
GO:0009892<\/a>
GO:0009987<\/a>
GO:0010468<\/a>
GO:0010605<\/a>
GO:0010629<\/a>
GO:0016070<\/a>
GO:0016071<\/a>
GO:0016740<\/a>
GO:0016772<\/a>
GO:0016779<\/a>
GO:0019222<\/a>
GO:0019439<\/a>
GO:0034641<\/a>
GO:0034655<\/a>
GO:0043170<\/a>
GO:0044237<\/a>
GO:0044238<\/a>
GO:0044248<\/a>
GO:0044260<\/a>
GO:0044265<\/a>
GO:0044270<\/a>
GO:0044424<\/a>
GO:0044464<\/a>
GO:0046483<\/a>
GO:0046700<\/a>
GO:0048519<\/a>
GO:0050789<\/a>
GO:0060255<\/a>
GO:0065007<\/a>
GO:0071704<\/a>
GO:0090304<\/a>
GO:0097159<\/a>
GO:1901360<\/a>
GO:1901361<\/a>
GO:1901363<\/a>
GO:1901575<\/a>\""},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":null,"dir":"Reference","previous_headings":"","what":"Pre-processing datasets — pre_process","title":"Pre-processing datasets — pre_process","text":"pre_process() function aids processing data inputs automatically establishes standardized format future use. allows two types data input: list datasets different sources long dataset containing specified last column type.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pre-processing datasets — pre_process","text":"","code":"pre_process( data, typenameList = NULL, replaceNA = TRUE, scale = TRUE, autoColName = \"Sec_\" )"},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pre-processing datasets — pre_process","text":"data data.frame describe feature one row. data contain variables ID value time_1, ..., value time_k, type extracting patterns across time. Note initial last column must exactly ID type. multiple data.frame format needs analyzed, also put list data.frame argument. case, variable type required generated next argument typenameList. typenameList vector strings. parameter used clarify source names data.frame, applicable input data list data.frame. default, set \"Dataset_1\", \"Dataset_2\", ..., etc. scale Logical; scale TRUE (default), standardize data.frame row base::scale. converts original value z-score. See also scale_by_row__(). autoColName string; autoColName -NULL (default), automatically set uniform column names data.frames. parameter applicable input data list data.frame. replaceNa Logical; replaceNa TRUE (default), replace NA 0.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pre-processing datasets — pre_process","text":"function returns long data.frame columns ID, value time_1, ..., value time_k, type.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Pre-processing datasets — pre_process","text":"consider two distinct scenarios application: one scenario, individuals collect several datasets various aspects instruments objects. example, might separately detecting lipids, metabolites, peptides specific soil sample. scenario, data uniform quality, can categorized larger groups exhibit significant differences. cases, pre_process() function serves valuable versatile tool. Yet, function optional generating dashboard. Users can perform processing long format matches required output. However, mindful number samples (timepoints) must greater 5 avoid potential errors subsequent prediction section.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pre-processing datasets — pre_process","text":"","code":"data(test_data) head(test_data, 10) #> ID T1 T2 T3 T4 T5 T6 T7 T8 T9 T10 type #> 1 1 1 0 0 1 1 0 0 1 6 6 type_A #> 2 2 6 0 0 0 0 3 1 0 2 1 type_A #> 3 3 1 0 0 0 2 0 0 2 2 1 type_A #> 4 4 4 5 3 3 7 2 1 1 0 0 type_A #> 5 5 4 3 NA 2 5 5 0 0 0 0 type_A #> 6 6 4 1 0 1 3 1 3 5 11 14 type_A #> 7 7 1 0 0 0 1 3 3 1 1 1 type_A #> 8 8 4 2 1 1 1 1 0 0 0 0 type_A #> 9 9 1 1 1 19 22 1 2 1 1 2 type_A #> 10 10 1 1 3 5 8 2 2 2 5 2 type_A a <- pre_process(test_data) head(a, 10) #> ID T1 T2 T3 T4 T5 T6 #> 1 1 -0.25354628 -0.6761234 -0.67612340 -0.25354628 -0.25354628 -0.6761234 #> 2 2 2.41458180 -0.6678631 -0.66786305 -0.66786305 -0.66786305 0.8733594 #> 3 3 0.21764288 -0.8705715 -0.87057150 -0.87057150 1.30585725 -0.8705715 #> 4 4 0.61658123 1.0569964 0.17616607 0.17616607 1.93782672 -0.2642491 #> 5 5 0.96186009 0.5038315 -0.87025436 0.04580286 1.41988870 1.4198887 #> 6 6 -0.06459959 -0.7105955 -0.92592741 -0.71059546 -0.27993154 -0.7105955 #> 7 7 -0.09086738 -0.9995412 -0.99954118 -0.99954118 -0.09086738 1.7264802 #> 8 8 2.40535118 0.8017837 0.00000000 0.00000000 0.00000000 0.0000000 #> 9 9 -0.50260633 -0.5026063 -0.50260633 1.70395805 2.07171878 -0.5026063 #> 10 10 -0.94019379 -0.9401938 -0.04477113 0.85065153 2.19378551 -0.4924825 #> T7 T8 T9 T10 type #> 1 -0.6761234 -0.25354628 1.85933936 1.85933936 type_A #> 2 -0.1541222 -0.66786305 0.35961857 -0.15412224 type_A #> 3 -0.8705715 1.30585725 1.30585725 0.21764288 type_A #> 4 -0.7046643 -0.70466426 -1.14507943 -1.14507943 type_A #> 5 -0.8702544 -0.87025436 -0.87025436 -0.87025436 type_A #> 6 -0.2799315 0.15073237 1.44272411 2.08871998 type_A #> 7 1.7264802 -0.09086738 -0.09086738 -0.09086738 type_A #> 8 -0.8017837 -0.80178373 -0.80178373 -0.80178373 type_A #> 9 -0.3800194 -0.50260633 -0.50260633 -0.38001942 type_A #> 10 -0.4924825 -0.49248246 0.85065153 -0.49248246 type_A"},{"path":"https://kaiyanm.github.io/MolPad/reference/reshape_for_make_functions.html","id":null,"dir":"Reference","previous_headings":"","what":"Reshape for 'make' functions — reshape_for_make_functions","title":"Reshape for 'make' functions — reshape_for_make_functions","text":"internal function produces three primary datasets dashboard intended \"make\" functions.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/reshape_for_make_functions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reshape for 'make' functions — reshape_for_make_functions","text":"","code":"reshape_for_make_functions(data, cluster, annotation, id_colname, id_type)"},{"path":"https://kaiyanm.github.io/MolPad/reference/reshape_for_make_functions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reshape for 'make' functions — reshape_for_make_functions","text":"data output pre_process() cluster output gClusters() annotation output gPathway() id_colname columns contain database IDs. id_type corresponding database names columns.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/reshape_for_make_functions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reshape for 'make' functions — reshape_for_make_functions","text":"","code":"data(test_data) l <- reshape_for_make_functions(test_data_processed, test_cluster, test_annotations_processed, id_colname = c(\"GO_ID\",\"KEGG_ID\"),id_type = c(\"GO\",\"KEGG\")) head(l[[1]]) #> ID cluster #> 1 1 Group_2 #> 2 2 Group_5 #> 3 3 Group_2 #> 4 4 Group_4 #> 5 5 Group_4 #> 6 6 Group_2 #> GO_ID #> 1 #> 2 #> 3 GO:0003674,GO:0003824,GO:0005575,GO:0006629,GO:0006644,GO:0006650,GO:0006655,GO:0006793,GO:0006796,GO:0008150,GO:0008152,GO:0008610,GO:0008654,GO:0008808,GO:0009058,GO:0009987,GO:0016020,GO:0016740,GO:0016772,GO:0016780,GO:0019637,GO:0030572,GO:0032048,GO:0032049,GO:0044237,GO:0044238,GO:0044249,GO:0044255,GO:0045017,GO:0046471,GO:0046474,GO:0046486,GO:0071704,GO:0090407,GO:1901576 #> 4 GO:0003674,GO:0003676,GO:0003723,GO:0003729,GO:0003824,GO:0004654,GO:0005488,GO:0005575,GO:0005622,GO:0005623,GO:0005737,GO:0006139,GO:0006401,GO:0006402,GO:0006725,GO:0006807,GO:0008150,GO:0008152,GO:0009056,GO:0009057,GO:0009892,GO:0009987,GO:0010468,GO:0010605,GO:0010629,GO:0016070,GO:0016071,GO:0016740,GO:0016772,GO:0016779,GO:0019222,GO:0019439,GO:0034641,GO:0034655,GO:0043170,GO:0044237,GO:0044238,GO:0044248,GO:0044260,GO:0044265,GO:0044270,GO:0044424,GO:0044464,GO:0046483,GO:0046700,GO:0048519,GO:0050789,GO:0060255,GO:0065007,GO:0071704,GO:0090304,GO:0097159,GO:1901360,GO:1901361,GO:1901363,GO:1901575 #> 5 #> 6 #> KEGG_ID Pathway taxonomic.scope #> 1 K07124 Integumentary System hormonal proteins #> 2 Skeletal System structural proteins #> 3 K06131 Muscular System enzymes #> 4 K00962 Nervous System contractile proteins #> 5 K02083 Endocrine System contractile proteins #> 6 Cardiovascular System structural proteins head(l[[2]]) #> # A tibble: 6 × 6 #> ID type cluster day value taxonomic.scope #> #> 1 1 type_A Group_2 T1 -0.254 hormonal proteins #> 2 1 type_A Group_2 T2 -0.676 hormonal proteins #> 3 1 type_A Group_2 T3 -0.676 hormonal proteins #> 4 1 type_A Group_2 T4 -0.254 hormonal proteins #> 5 1 type_A Group_2 T5 -0.254 hormonal proteins #> 6 1 type_A Group_2 T6 -0.676 hormonal proteins head(l[[3]]) #> ID cluster #> 1 1 Group_2 #> 2 2 Group_5 #> 3 3 Group_2 #> 4 4 Group_4 #> 5 5 Group_4 #> 6 6 Group_2 #> GO_ID #> 1 #> 2 #> 3 GO:0003674<\/a>
GO:0003824<\/a>
GO:0005575<\/a>
GO:0006629<\/a>
GO:0006644<\/a>
GO:0006650<\/a>
GO:0006655<\/a>
GO:0006793<\/a>
GO:0006796<\/a>
GO:0008150<\/a>
GO:0008152<\/a>
GO:0008610<\/a>
GO:0008654<\/a>
GO:0008808<\/a>
GO:0009058<\/a>
GO:0009987<\/a>
GO:0016020<\/a>
GO:0016740<\/a>
GO:0016772<\/a>
GO:0016780<\/a>
GO:0019637<\/a>
GO:0030572<\/a>
GO:0032048<\/a>
GO:0032049<\/a>
GO:0044237<\/a>
GO:0044238<\/a>
GO:0044249<\/a>
GO:0044255<\/a>
GO:0045017<\/a>
GO:0046471<\/a>
GO:0046474<\/a>
GO:0046486<\/a>
GO:0071704<\/a>
GO:0090407<\/a>
GO:1901576<\/a> #> 4 GO:0003674<\/a>
GO:0003676<\/a>
GO:0003723<\/a>
GO:0003729<\/a>
GO:0003824<\/a>
GO:0004654<\/a>
GO:0005488<\/a>
GO:0005575<\/a>
GO:0005622<\/a>
GO:0005623<\/a>
GO:0005737<\/a>
GO:0006139<\/a>
GO:0006401<\/a>
GO:0006402<\/a>
GO:0006725<\/a>
GO:0006807<\/a>
GO:0008150<\/a>
GO:0008152<\/a>
GO:0009056<\/a>
GO:0009057<\/a>
GO:0009892<\/a>
GO:0009987<\/a>
GO:0010468<\/a>
GO:0010605<\/a>
GO:0010629<\/a>
GO:0016070<\/a>
GO:0016071<\/a>
GO:0016740<\/a>
GO:0016772<\/a>
GO:0016779<\/a>
GO:0019222<\/a>
GO:0019439<\/a>
GO:0034641<\/a>
GO:0034655<\/a>
GO:0043170<\/a>
GO:0044237<\/a>
GO:0044238<\/a>
GO:0044248<\/a>
GO:0044260<\/a>
GO:0044265<\/a>
GO:0044270<\/a>
GO:0044424<\/a>
GO:0044464<\/a>
GO:0046483<\/a>
GO:0046700<\/a>
GO:0048519<\/a>
GO:0050789<\/a>
GO:0060255<\/a>
GO:0065007<\/a>
GO:0071704<\/a>
GO:0090304<\/a>
GO:0097159<\/a>
GO:1901360<\/a>
GO:1901361<\/a>
GO:1901363<\/a>
GO:1901575<\/a> #> 5 #> 6 #> KEGG_ID #> 1 K07124<\/a> #> 2 #> 3 K06131<\/a> #> 4 K00962<\/a> #> 5 K02083<\/a> #> 6 #> Pathway taxonomic.scope #> 1 Integumentary System hormonal proteins #> 2 Skeletal System structural proteins #> 3 Muscular System enzymes #> 4 Nervous System contractile proteins #> 5 Endocrine System contractile proteins #> 6 Cardiovascular System structural proteins"},{"path":"https://kaiyanm.github.io/MolPad/reference/scale_by_row__.html","id":null,"dir":"Reference","previous_headings":"","what":"Scale by row — scale_by_row__","title":"Scale by row — scale_by_row__","text":"Scales values sample, row independently processed.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/scale_by_row__.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Scale by row — scale_by_row__","text":"","code":"scale_by_row__(data)"},{"path":"https://kaiyanm.github.io/MolPad/reference/scale_by_row__.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Scale by row — scale_by_row__","text":"input expected data frame first column ID following columns containing observations different time points. ID column remains unaltered, columns double (dbl) format scaled.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/scale_by_row__.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Scale by row — scale_by_row__","text":"","code":"data(test_data) scale_by_row__(test_data[1:5,1:10]) #> ID T1 T2 T3 T4 T5 T6 #> 1 1 -0.05847053 -0.5847053 -0.58470535 -0.05847053 -0.05847053 -0.5847053 #> 2 2 2.26366583 -0.6467617 -0.64676167 -0.64676167 -0.64676167 0.8084521 #> 3 3 0.22866478 -0.8003267 -0.80032673 -0.80032673 1.25765629 -0.8003267 #> 4 4 0.50395263 0.9575100 0.05039526 0.05039526 1.86462473 -0.4031621 #> 5 5 0.73869087 0.2841119 NA -0.17046712 1.19326987 1.1932699 #> T7 T8 T9 #> 1 -0.5847053 -0.05847053 2.5727035 #> 2 -0.1616904 -0.64676167 0.3233808 #> 3 -0.8003267 1.25765629 1.2576563 #> 4 -0.8567195 -0.85671947 -1.3102768 #> 5 -1.0796251 -1.07962512 -1.0796251"},{"path":"https://kaiyanm.github.io/MolPad/reference/test_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Test data — test_data","title":"Test data — test_data","text":"synthetically generated dataset created basic testing purposes.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/test_data.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Test data — test_data","text":"two datasets: data annotations","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/test_data.html","id":"data","dir":"Reference","previous_headings":"","what":"data","title":"Test data — test_data","text":"data frame 100 rows 12 variables: ID row ID T1~T10 count value 10 timepoints type type ~D","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/test_data.html","id":"annotations","dir":"Reference","previous_headings":"","what":"annotations","title":"Test data — test_data","text":"data frame 100 rows 5 variables: ID row ID GO_ID go ID KEGG_ID kegg ID system primary lable: 'Integumentary System', 'Skeletal System', 'Muscular System', 'Nervous System', 'Endocrine System', 'Cardiovascular System', 'Lymphatic System', 'Respiratory System', 'Digestive System', 'Urinary System' class secondary label: 'antibodies', 'contractile proteins', 'enzymes', 'hormonal proteins', 'structural proteins', 'storage proteins', 'transport proteins'","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/test_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Test data — test_data","text":"","code":"data(test_data)"},{"path":"https://kaiyanm.github.io/MolPad/reference/transpose_dataframe__.html","id":null,"dir":"Reference","previous_headings":"","what":"Transpose dataframe — transpose_dataframe__","title":"Transpose dataframe — transpose_dataframe__","text":"function transposes provided data frame, using values first column new column names.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/transpose_dataframe__.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transpose dataframe — transpose_dataframe__","text":"","code":"transpose_dataframe__(data)"},{"path":"https://kaiyanm.github.io/MolPad/reference/transpose_dataframe__.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transpose dataframe — transpose_dataframe__","text":"expected input data frame first column serves Time, subsequent columns contain observations various features.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/transpose_dataframe__.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transpose dataframe — transpose_dataframe__","text":"","code":"a <- data.frame(\"Day\"=c(\"Day1\",\"Day2\",\"Day3\"),\"feature_1\" =c(1,2,3),\"feature_2\" =c(0,4,1),\"feature_3\" =c(1,1,0)) a #> Day feature_1 feature_2 feature_3 #> 1 Day1 1 0 1 #> 2 Day2 2 4 1 #> 3 Day3 3 1 0 transpose_dataframe__(a) #> Day1 Day2 Day3 #> 1 1 2 3 #> 2 0 4 1 #> 3 1 1 0"}]
+[{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"CC0 1.0 Universal","title":"CC0 1.0 Universal","text":"CREATIVE COMMONS CORPORATION LAW FIRM PROVIDE LEGAL SERVICES. DISTRIBUTION DOCUMENT CREATE ATTORNEY-CLIENT RELATIONSHIP. CREATIVE COMMONS PROVIDES INFORMATION “-” BASIS. CREATIVE COMMONS MAKES WARRANTIES REGARDING USE DOCUMENT INFORMATION WORKS PROVIDED HEREUNDER, DISCLAIMS LIABILITY DAMAGES RESULTING USE DOCUMENT INFORMATION WORKS PROVIDED HEREUNDER.","code":""},{"path":"https://kaiyanm.github.io/MolPad/LICENSE.html","id":"statement-of-purpose","dir":"","previous_headings":"","what":"Statement of Purpose","title":"CC0 1.0 Universal","text":"laws jurisdictions throughout world automatically confer exclusive Copyright Related Rights (defined ) upon creator subsequent owner(s) (, “owner”) original work authorship /database (, “Work”). 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Affirmer understands acknowledges Creative Commons party document duty obligation respect CC0 use Work.","code":""},{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/articles/FAQ.html","id":"how-can-i-tell-if-my-dataset-is-suitable-for-using-this-dashboard","dir":"Articles","previous_headings":"Common Questions:","what":"How can I tell if my dataset is suitable for using this dashboard?","title":"FAQs","text":"lot longitudinal data least two descriptive columns (like types), match minimal needs starting dashboard. addition, data specific web-ID columns, MolPad make convenient createing links databases automatically.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/FAQ.html","id":"how-can-i-customize-my-dashboard","dir":"Articles","previous_headings":"Common Questions:","what":"How can I customize my dashboard?","title":"FAQs","text":"can edit title within gDashboard() function. additional personalization, adjusting colors sizes figures, can fork repository, create copy, make modifications dashboard.css file.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/FAQ.html","id":"what-should-i-do-if-i-only-have-a-few-time-points5","dir":"Articles","previous_headings":"Common Questions:","what":"What should I do if I only have a few time points(<5)?","title":"FAQs","text":"Firstly, strongly recommend extending length longitudinal samples. can achieve increasing number measurements two days incorporating simulated data. options don’t suit needs, can also consider using “linear” method gNetwork() function.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Case Study: Cheese Communities","text":"vignette provides comprehensive guide using MolPad case study, including data pre-processing, network generation, result analysis.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"washed-rind-cheese-microbial-communities","dir":"Articles","previous_headings":"","what":"Washed-Rind Cheese Microbial Communities","title":"Case Study: Cheese Communities","text":"Cheese making ancient craft involves coagulation milk proteins form curds, separated liquid whey. curds processed, shaped, aged develop desired texture flavor. Various factors, type milk, specific cultures bacteria, aging conditions, contribute unique characteristics cheese. One crucial step process aging practice, regular washing brine solution plays significant role. process producing cheese, regular washing brine solution aging practice can homogenize microbial communities cheese’ surface facilitate intermicrobial interactions. following parts, analyze longitudinal data set three washed-rind cheese communities collected cheese ripening.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"data","dir":"Articles","previous_headings":"","what":"Data","title":"Case Study: Cheese Communities","text":"analysis, use data set contained study Smith et al. (2022) microbial communities cheese. original research investigated successional dynamics occur within cheese rind microbial communities using combination 16S rRNA amplicon, Illumina, PacBio sequencing. functionally taxonomically annotate (using eggNOG (21) MMseqs2 (22)) contigs generated Illumina reads, demonstrate utility MolPad using single-omic. Specifically, focus Cheese Sample Cheese Sample C. detailed information attached data, please check documentation.","code":"data(\"cheese\") str(cheese) #> tibble [106,239 × 18] (S3: tbl_df/tbl/data.frame) #> $ ID : chr [1:106239] \"1\" \"2\" \"3\" \"4\" ... #> $ A_1 : int [1:106239] 38 23 24 3 58 12 1 14 1 3 ... #> $ A_2 : int [1:106239] 23 6 5 2 14 9 1 7 1 1 ... #> $ A_3 : int [1:106239] 27 4 37 4 45 14 0 14 3 5 ... #> $ A_4 : int [1:106239] 5 0 10 2 13 4 1 4 4 0 ... #> $ A_5 : int [1:106239] 11 9 19 16 32 13 0 4 1 1 ... #> $ C_1 : int [1:106239] 13 21 3 56 82 2 4 17 7 2 ... #> $ C_3 : int [1:106239] 1 1 0 1 3 0 0 0 0 1 ... #> $ C_4 : int [1:106239] 3 0 1 7 8 1 0 1 2 2 ... #> $ C_5 : int [1:106239] 0 2 4 17 3 4 0 1 8 6 ... #> $ GO_ID : chr [1:106239] NA NA NA NA ... #> $ KEGG_ID: chr [1:106239] NA NA NA NA ... #> $ domain : chr [1:106239] NA NA NA NA ... #> $ phylum : chr [1:106239] NA NA NA NA ... #> $ class : chr [1:106239] NA NA NA NA ... #> $ order : chr [1:106239] NA NA NA NA ... #> $ family : chr [1:106239] NA NA NA NA ... #> $ genus : chr [1:106239] NA NA NA NA ... str(annotations) #> tibble [86,156 × 9] (S3: tbl_df/tbl/data.frame) #> $ ID : chr [1:86156] \"9\" \"10\" \"11\" \"12\" ... #> $ GO_ID : chr [1:86156] \"-\" \"-\" \"-\" \"-\" ... #> $ KEGG_ID: chr [1:86156] \"-\" \"-\" \"-\" \"-\" ... #> $ domain : chr [1:86156] \"Bacteria\" \"Bacteria\" \"Bacteria\" \"Bacteria\" ... #> $ phylum : chr [1:86156] \"Pseudomonadota\" \"Pseudomonadota\" \"Pseudomonadota\" \"Pseudomonadota\" ... #> $ class : chr [1:86156] \"Alphaproteobacteria\" \"Alphaproteobacteria\" \"Alphaproteobacteria\" \"Alphaproteobacteria\" ... #> $ order : chr [1:86156] \"Caulobacterales\" \"Caulobacterales\" \"Hyphomicrobiales\" \"Hyphomicrobiales\" ... #> $ family : chr [1:86156] \"Caulobacteraceae\" \"Caulobacteraceae\" \"Bartonellaceae\" \"Brucellaceae;-_Brucella/Ochrobactrum group\" ... #> $ genus : chr [1:86156] \"Caulobacter\" \"Caulobacter;-_unclassified Caulobacter\" \"Bartonella\" \"Brucella\" ..."},{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"data-and-annotations","dir":"Articles","previous_headings":"Data > Pre-process","what":"Data and Annotations","title":"Case Study: Cheese Communities","text":"select ‘type’ column phylum describe characteristic cheese data. Also, columns phylum class taken tags elemental composition. section, introduce data preparing steps analysis. annotations dataset contains various columns describe characteristics properties samples. First, select ‘type’ column phylum provide broad categorization microbial communities present cheese surface. categorization helps understanding overall composition diversity microbes high taxonomic level. run pre_process() function clean standardize data. annotate dataset, also use columns phylum class tags elemental composition microbial communities. phylum column represents major taxonomic rank, giving us broad overview microbial distribution. class column provides detailed information, allowing us delve deeper specific types microbes present. pre-processing, two datasets put dashboard look like:","code":"cheesedata <- cheese |> select(ID, A_1:C_5, phylum) |> rename(type=phylum) |> pre_process() pathchee <- gAnnotation(annotations,\"phylum\",\"class\") # data cheesedata[112:115,] #> # A tibble: 4 × 11 #> ID A_1 A_2 A_3 A_4 A_5 C_1 C_3 C_4 C_5 type #> #> 1 112 0.943 -0.471 -0.471 -0.471 -0.471 2.36 -0.471 -0.471 -0.471 Other #> 2 113 0.786 -0.124 0.126 -0.623 -0.637 2.33 -0.667 -0.593 -0.593 Other #> 3 114 1.22 -1.09 -0.430 -0.829 -0.170 1.43 -1.30 0.455 0.715 Other #> 4 115 2.67 -0.333 -0.333 -0.333 -0.333 -0.333 -0.333 -0.333 -0.333 Other # annotation pathchee[112:115,] #> # A tibble: 4 × 9 #> ID GO_ID KEGG_ID domain Pathway taxonomic.scope order family genus #> #> 1 145 - ko:K00004 Bacte… Pseudo… Unknown NA NA NA #> 2 147 - ko:K00004 Bacte… Actino… Actinomycetes NA NA NA #> 3 148 - ko:K00004 Bacte… Bacill… Bacilli Baci… Bacil… Lysi… #> 4 149 - ko:K00004,ko:K0… Bacte… Actino… Actinomycetes NA NA NA"},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"cluster-input","dir":"Articles","previous_headings":"Data","what":"Cluster Input","title":"Case Study: Cheese Communities","text":"section, generate clusters first dataset using gClusters function. function takes cheese dataset (cheesedata) input generates clusters based specified parameters. , set number clusters 10 (ncluster = 10) specify maximum number clusters consider determining optimal number clusters (elbow.max=15).","code":"cluschee <- gClusters(cheesedata,ncluster = 10,elbow.max=15)"},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"network-input","dir":"Articles","previous_headings":"Data","what":"network input","title":"Case Study: Cheese Communities","text":"generating clusters major patterns, proceed obtain network results clusters. Taking cluster centroids nodes, prediction process edges divided individual regression tasks, cluster centroid independentally predicted expression cluster centroids, using random forests. pick top 3 related predictors cluster centroid save network output future use. achieved using gNetwork() function. gain insight network results, can visualize details using gNetwork_view() function, shown .","code":"networkchee <- gNetwork(cluschee,ntop = 3) gNetwork_view(networkchee)"},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"run-dashboard","dir":"Articles","previous_headings":"","what":"Run Dashboard","title":"Case Study: Cheese Communities","text":"clusters network results obtained, can proceed run dashboard. involves declaring annotations executing dashboard using gDashboard() function. , pass cheese dataset (cheesedata), cluster results (cluschee), network results (networkchee), specify column names types annotation identifiers.","code":"gDashboard(cheesedata, cluschee, pathchee, networkchee, id_colname = c(\"GO_ID\",\"KEGG_ID\"), id_type = c(\"GO\",\"KEGG\"))"},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"results","dir":"Articles","previous_headings":"","what":"Results","title":"Case Study: Cheese Communities","text":"example, filter Actinomycetota (Actinobacteria) functional group, see edges connecting group 10 group 3- clusters features associated Actinomycetoa Cheese sample (Fig 1.3.). Fig 1: Dashboard showing Actinomycetota filtered network () enrichment pattern Cheese Sample-(B) Cheese Sample-C (C); Cluster pattern Group 9, also enriched Type IV secretion genes (D). Looking pattern traces groups, (Fig 1.3.B), peak samples A4 (week 9) A5 (week 13), mirrors 16S rRNA results Saak et al. Since two clusters edges connecting groups, suggests Actinomycetoa features found groups follow distinct longitudinal succession patterns independent. looking Actinomycetoa within Cheese Sample C see different pattern. Groups 2 5, features associated Actinomycetoa, highly connected groups (Fig 1.3.). results, can hypothesize though Actinomycetoa features abundant later time points cheese samples, dynamics differentially influenced. authors found Type VI secretion enriched Pseudomonadota bacteria (specifically, Psychrobacter), hypothesized enrichment due importance physical species interactions occur habitat. Using MolPad, searched secretion systems associated genes, understand dynamics within community. Focusing KEGG annotated Type IV secretion genes, found Group 9 contained 12/13 genes. Within group, features cluster ones peak Cheese sample C5 (week 13, Fig 1.3.D). also taxonomically diverse sample. , can hypothesize increased taxonomic diversity also associated increases genes related competitive species interactions.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"installation","dir":"Articles","previous_headings":"","what":"Installation","title":"Get started","text":"can either install MolPad devtools, clone repository local computer (example, onto ./Github) installing: , load package:","code":"# Install the package in R: install.packages(\"devtools\") library(devtools) install_github(\"KaiyanM/MolPad\") # Download through Github and install the package in R: setwd(\"./GitHub\") install(\"MolPad\") library(MolPad)"},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"before-input","dir":"Articles","previous_headings":"","what":"Before Input","title":"Get started","text":"recommend carefully reviewing data considering applying normalization (standardization, quantile normalization, log transformation, …) imputation (KNN, PMM, Mean/Median/Mode, …) address issues related library size missing data beforehead, otherwise pre_processing() function default converting NAs 0s.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"tips-for-normalization-and-imputation","dir":"Articles","previous_headings":"Before Input","what":"Tips for normalization and imputation","title":"Get started","text":"choose normalization method, first consider data distribution. Use Z-score normalization data normally distributed, log transformation robust scaling ’s skewed. analysis requires data within specific range, like [0, 1], opt Min-Max normalization. datasets outliers, robust scaling rank normalization better choices. selecting imputation method, assess amount pattern missing data. small amount missing data, simple methods like mean, median, mode imputation effective. missing data related variables, consider sophisticated approaches like Predictive Mean Matching k-NN imputation. computational resources concern, simpler imputation methods might practical, resources allow, advanced methods can improve data quality. ’s hard decide, can always experiment different methods use cross-validation compare effectiveness. Let choice guided domain knowledge specific requirements analysis increase quality following steps.","code":""},{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"multi-omics-yes-or-no","dir":"Articles","previous_headings":"Data Format","what":"Multi-omics: Yes or No","title":"Get started","text":"visualization pipeline starts pre-processing module includes built-functions reshape data inputs standard format streamline data preparation. Depending nature datasets, two primary conditions consider: research object includes multi-omics measurements, assume provide list dataframes, collected different omics type. example, might datasets peptides, metabolites, lipids. combine together standard long dataset row represents feature, simply run pre_process() function, conduct data preparation yield standard input format demonstrated . Datasets multi-omics, can still utilize dashboard long ensuring data inputs reformatted standard longitudinal format. situation, can manually assign type column category label describe major groups data, input data pre_processing() module data preparation . case study, utilized “Kingdom” type label column cheese data. datasets multi-omics, can simply apply dashboard reformatting data standard longitudinal format. case, manually assign category label type column represent major groups. can still input data pre_processing() module cleaning preparation. case study, used “Kingdom” type label cheese datasets.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"data-format-and-pre-process","dir":"Articles","previous_headings":"Data Format","what":"Data Format and Pre-process","title":"Get started","text":"figure, , B, C can represent datasets different sources different aspects measurements. two examples input data might look like transform required format pre_process() modules.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"if-your-input-is-a-list-of-datasets","dir":"Articles","previous_headings":"Data Format > Data Format and Pre-process","what":"If your input is a list of datasets:","title":"Get started","text":"list datasets, following format: ID column identical column names. pre_process():","code":"str(fungal_dlist) #> List of 3 #> $ :'data.frame': 149786 obs. of 12 variables: #> ..$ ID : chr [1:149786] \"grp1_1\" \"grp1_2\" \"grp1_6\" \"grp1_7\" ... #> ..$ D0_01 : num [1:149786] -0.33 -0.33 1.591 1.106 0.659 ... #> ..$ D1_02 : num [1:149786] 0.628 0.628 3.087 3.618 2.843 ... #> ..$ D2_03 : num [1:149786] -1.223 -1.223 0.846 1.178 1.435 ... #> ..$ D3_04 : num [1:149786] -2.411 -1.808 1.239 1.753 0.258 ... #> ..$ D4_05 : num [1:149786] -1.41 -1.18 2.05 1.64 1.21 ... #> ..$ D5_06 : num [1:149786] -2.245 -2.309 0.83 1.64 0.395 ... #> ..$ D6_07 : num [1:149786] -0.784 -1.125 2.758 2.888 2.516 ... #> ..$ D7_08 : num [1:149786] -2.61197 -2.68724 0.42236 0.92846 0.00892 ... #> ..$ D8_09 : num [1:149786] -0.951 -1.3 2.417 2.454 1.418 ... #> ..$ D9_10 : num [1:149786] -1.91 -2.61 1.33 1.8 2.25 ... #> ..$ D10_11: num [1:149786] -2.871 -3.417 1.101 0.619 1.275 ... #> $ :'data.frame': 150 obs. of 12 variables: #> ..$ ID : chr [1:150] \"grp2_1\" \"grp2_2\" \"grp2_3\" \"grp2_4\" ... #> ..$ D0_01 : num [1:150] 0.947 1.012 0.156 0.31 0.138 ... #> ..$ D1_02 : num [1:150] -1.616 -0.917 -1.581 -2.717 -2.578 ... #> ..$ D2_03 : num [1:150] -2.4 -1.67 -2.31 -3.02 -3.66 ... #> ..$ D3_04 : num [1:150] -0.5734 0.0336 -0.351 -1.3052 -1.737 ... #> ..$ D4_05 : num [1:150] 0.662 0.854 1.002 0.12 -0.371 ... #> ..$ D5_06 : num [1:150] -1.09 -1.28 -1.04 -1.82 -3.15 ... #> ..$ D6_07 : num [1:150] -0.212 0.718 0.189 -0.627 -1.081 ... #> ..$ D7_08 : num [1:150] 0.969 2.117 1.462 0.339 -0.038 ... #> ..$ D8_09 : num [1:150] -0.712 0.43 -0.281 -1.297 -1.822 ... #> ..$ D9_10 : num [1:150] -0.649 0.45 -0.124 -1.77 -1.962 ... #> ..$ D10_11: num [1:150] -1.05 -0.85 -1.58 -1.91 -4.05 ... #> $ :'data.frame': 369 obs. of 12 variables: #> ..$ ID : chr [1:369] \"grp3_1\" \"grp3_2\" \"grp3_3\" \"grp3_4\" ... #> ..$ D0_01 : num [1:369] -0.918 -2.112 -1.372 -0.4 -1.75 ... #> ..$ D1_02 : num [1:369] -1.219 -3.502 -1.504 -0.615 -2.11 ... #> ..$ D2_03 : num [1:369] -0.542 -2.324 -0.592 0.161 -1.342 ... #> ..$ D3_04 : num [1:369] -0.101 -1.495 0.1 0.603 -1.058 ... #> ..$ D4_05 : num [1:369] 1.715 1.557 1.89 2.296 0.653 ... #> ..$ D5_06 : num [1:369] -1.452 -2.37 -1.182 -0.846 -2.531 ... #> ..$ D6_07 : num [1:369] 0.0879 -0.9982 0.4382 0.6215 -0.9464 ... #> ..$ D7_08 : num [1:369] 0.0502 0.4668 0.5378 0.8353 -0.9213 ... #> ..$ D8_09 : num [1:369] -0.0268 -0.4226 0.4528 0.6328 -1.0998 ... #> ..$ D9_10 : num [1:369] -0.5807 -1.7755 -0.1956 0.0866 -1.5876 ... #> ..$ D10_11: num [1:369] 0.118 -0.16 0.687 0.572 -1.037 ... #> ID D0_01 D1_02 D2_03 D3_04 D4_05 D5_06 #> 1 grp1_1 -0.3297741 0.6284682 -1.2232968 -2.411339 -1.413062 -2.2445487 #> 2 grp1_2 -0.3297741 0.6284682 -1.2232968 -1.807533 -1.183498 -2.3088022 #> 3 grp1_6 1.5908054 3.0871284 0.8460835 1.239411 2.054109 0.8300876 #> 4 grp1_7 1.1061795 3.6176250 1.1781012 1.753078 1.643661 1.6400102 #> D6_07 D7_08 D8_09 D9_10 D10_11 #> 1 -0.7839412 -2.6119663 -0.9511467 -1.914273 -2.8711810 #> 2 -1.1251434 -2.6872417 -1.2996614 -2.610647 -3.4165861 #> 3 2.7576288 0.4223618 2.4165602 1.333387 1.1012825 #> 4 2.8877343 0.9284640 2.4538561 1.799019 0.6187648 #> ID D0_01 D1_02 D2_03 D3_04 D4_05 D5_06 #> 1 grp2_1 0.9467431 -1.6162900 -2.400394 -0.57337845 0.6616711 -1.090914 #> 2 grp2_2 1.0120048 -0.9165994 -1.674330 0.03361378 0.8540950 -1.283964 #> 3 grp2_3 0.1561394 -1.5805490 -2.307281 -0.35095093 1.0018741 -1.035030 #> 4 grp2_4 0.3104578 -2.7173471 -3.022886 -1.30521496 0.1198075 -1.816677 #> D6_07 D7_08 D8_09 D9_10 D10_11 #> 1 -0.2116778 0.9688582 -0.7123773 -0.6489204 -1.0504845 #> 2 0.7181970 2.1174460 0.4302981 0.4504066 -0.8503481 #> 3 0.1885422 1.4619677 -0.2805926 -0.1235090 -1.5757556 #> 4 -0.6271954 0.3393109 -1.2967938 -1.7697248 -1.9073101 #> ID D0_01 D1_02 D2_03 D3_04 D4_05 D5_06 #> 1 grp3_1 -0.9178218 -1.2193128 -0.5415780 -0.1007493 1.715495 -1.4515238 #> 2 grp3_2 -2.1120636 -3.5015326 -2.3236475 -1.4949366 1.557463 -2.3698578 #> 3 grp3_3 -1.3724446 -1.5043101 -0.5922380 0.1004978 1.889573 -1.1815469 #> 4 grp3_4 -0.4002445 -0.6145252 0.1613003 0.6031744 2.295751 -0.8464012 #> D6_07 D7_08 D8_09 D9_10 D10_11 #> 1 0.08792489 0.05020749 -0.02681783 -0.58065562 0.1177374 #> 2 -0.99822332 0.46681389 -0.42255125 -1.77554842 -0.1603138 #> 3 0.43822354 0.53780990 0.45276561 -0.19558682 0.6870272 #> 4 0.62145080 0.83528878 0.63275758 0.08663959 0.5720591 data <- pre_process(fungal_dlist, typenameList = c('DataSource_A','DataSource_B','DataSource_C'), replaceNA = TRUE, scale = TRUE, autoColName = \"Section_\") #> [1] \"Reformat a list of datasets:\" head(data,10) #> ID D0_01 D1_02 D2_03 D3_04 D4_05 D5_06 #> 1 grp1_1 1.06146675 1.956656 0.2267383 -0.88312967 0.04945980 -0.7273146 #> 2 grp1_2 1.08465667 1.916983 0.3085454 -0.19892034 0.34311497 -0.6343214 #> 3 grp1_6 -0.01899274 1.717871 -0.8834318 -0.42687491 0.51878884 -0.9019992 #> 4 grp1_7 -0.75869657 2.051470 -0.6782202 -0.03485388 -0.15728499 -0.1613700 #> 5 grp1_8 -0.68132827 1.648062 0.1465161 -1.10875822 -0.09101615 -0.9629427 #> 6 grp1_11 0.27709905 1.981042 -0.5786717 0.02888008 0.54647685 -0.7013406 #> 7 grp1_12 0.90487968 2.180079 -0.6439891 -0.14841544 -0.69040101 -0.3834938 #> 8 grp1_13 0.46335031 1.695812 -1.0938459 -0.76868293 0.74415950 -0.6861036 #> 9 grp1_14 0.72014702 1.708604 -0.9344900 -0.35796963 0.63730277 -0.9872778 #> 10 grp1_15 0.54526798 1.784716 -0.8846753 -0.49538649 0.62916563 -0.9742293 #> D6_07 D7_08 D8_09 D9_10 D10_11 type #> 1 0.6371841 -1.0705560 0.4809808 -0.41877137 -1.31271417 DataSource_A #> 2 0.3938012 -0.9630327 0.2422154 -0.89650312 -1.59653914 DataSource_A #> 3 1.3354026 -1.3752687 0.9395058 -0.31779210 -0.58720856 DataSource_A #> 4 1.2347631 -0.9575502 0.7492778 0.01655139 -1.30408627 DataSource_A #> 5 1.2995756 -1.3741657 0.1285139 1.01932257 -0.02377948 DataSource_A #> 6 1.1406352 -1.6339536 0.2041455 -0.45793129 -0.80638109 DataSource_A #> 7 0.4794959 -1.6340692 0.5360766 -0.31430867 -0.28585374 DataSource_A #> 8 1.2235141 -1.2241931 0.6790383 -0.28487806 -0.74817082 DataSource_A #> 9 1.1466328 -1.4265286 0.5357111 -0.51799361 -0.52413804 DataSource_A #> 10 1.1813273 -1.2944121 0.6417621 -0.46906150 -0.66447461 DataSource_A"},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"if-your-input-is-a-single-dataset","dir":"Articles","previous_headings":"Data Format > Data Format and Pre-process","what":"If your input is a single dataset:","title":"Get started","text":"dataset already includes labeled ‘type’ column: pre_process():","code":"#> ID Day_1 Day_2 Day_3 Day_4 Day_5 Day_6 #> 1 1 NA NA -0.82919412 0.08237778 1.30438699 0.4875914 #> 2 2 NA NA -0.41606256 1.42523475 -0.44792066 1.1425154 #> 3 3 1.0354708 NA 0.78841026 -1.49585251 -0.41361867 -0.3271995 #> 4 4 -0.3005323 0.6998150 -1.86018783 1.14167433 -1.04204735 -2.4930409 #> 5 5 -0.1307903 0.7839983 0.25834857 0.62421147 -0.07202124 -0.9214203 #> 6 6 -0.9910321 1.8059465 -1.25395397 -0.45260649 -0.57303842 0.9990113 #> 7 7 -0.9160734 1.4475673 -3.10452531 -0.06560360 -1.27374744 -0.5703149 #> 8 8 -0.7381729 -0.4844302 1.10281675 0.12049188 0.20892638 -0.3115689 #> 9 9 -0.2581716 -0.5906591 0.09794986 2.44249606 1.13135657 0.4886958 #> 10 10 1.8831483 -1.2490571 0.55981559 -2.31731640 0.97323395 -0.7994677 #> Day_7 Day_8 type #> 1 -1.4632070 0.05832042 peptide #> 2 -0.4292379 -0.79047610 peptide #> 3 -0.3508094 -0.34995898 peptide #> 4 -0.3011731 0.67869535 peptide #> 5 1.1761504 -0.86303628 peptide #> 6 -0.8993647 -0.44801227 peptide #> 7 1.1593812 -0.90149427 peptide #> 8 0.5463595 -1.51687039 lipid #> 9 -0.5016034 -0.08241911 lipid #> 10 -0.9442708 NA metabolite x1 <- pre_process(x, replaceNA = TRUE,scale = TRUE) head(x1,10) #> ID Day_1 Day_2 Day_3 Day_4 Day_5 Day_6 #> 1 1 0.05451890 0.05451890 -0.9508450 0.1543986 1.6360347 0.64570353 #> 2 2 -0.07554744 -0.07554744 -0.5950354 1.7039742 -0.6348129 1.35097605 #> 3 3 1.49874204 0.17759695 1.1835204 -1.7309438 -0.3501343 -0.23987305 #> 4 4 0.10376561 0.87801613 -1.1033792 1.2200072 -0.4701534 -1.59319589 #> 5 5 -0.31530188 0.89803359 0.2008348 0.6860993 -0.2373532 -1.36395879 #> 6 6 -0.71746086 1.90776216 -0.9642373 -0.2120986 -0.3251351 1.15037885 #> 7 7 -0.27002806 1.37506283 -1.7931878 0.3218978 -0.5189688 -0.02938062 #> 8 8 -0.74469269 -0.43190500 1.5246885 0.3137802 0.4227931 -0.21881953 #> 9 9 -0.58919072 -0.91616395 -0.2389755 2.0666865 0.7772922 0.14528989 #> 10 10 1.56649401 -0.74805373 0.5886154 -1.5374455 0.8941115 -0.41582898 #> Day_7 Day_8 type #> 1 -1.7195596 0.1252300 peptide #> 2 -0.6114859 -1.0625212 peptide #> 3 -0.2699967 -0.2689116 peptide #> 4 0.1032697 0.8616699 peptide #> 5 1.4181669 -1.2865208 peptide #> 6 -0.6314225 -0.2077865 peptide #> 7 1.1744856 -0.2598810 peptide #> 8 0.8387456 -1.7045902 lipid #> 9 -0.8285852 -0.4163531 lipid #> 10 -0.5228315 0.1749388 metabolite"},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"choose-your-annotation","dir":"Articles","previous_headings":"","what":"Choose Your Annotation","title":"Get started","text":"addition specifying data type mentioned , methods support three levels information: functional annotation, taxonomy annotation, feature annotation. annotations matched ID columns annotation data, serving another crucial input generating dashboard. facilitate automatic feature link generation using KeggID GOID, users set corresponding column names beforehand. example annotation formats align data inputs. data two layers labels (like cheese dataset), can specify two layers want focus network using gAnnotation() function.","code":"#> ID taxonomic.scope ko_term Pathway #> 1 grp1_24 Bacteria K02706 00195_Photosynthesis #> 2 grp1_25 Bacteria K02706 00195_Photosynthesis #> 3 grp1_26 Bacteria K02706 00195_Photosynthesis #> 4 grp1_1075 Bacteria K02112 00195_Photosynthesis #> 5 grp1_1076 Bacteria K02112 00195_Photosynthesis #> 6 grp1_1358 Bacteria K02112 00195_Photosynthesis Annotation_path_taxon <- gAnnotation(annotations,\"phylum\",\"class\")"},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"network-generation","dir":"Articles","previous_headings":"","what":"Network Generation","title":"Get started","text":"generate network, first scale cluster trajectories across input molecular features. Trajectories clustered using K-means, built-elbow method used select optimal K. build network clustered trajectories adapting GENIE3 algorithm. Specifically, network learned series regressions, cluster centroid predicted expression patterns cluster centroids using random forests. choose random forests potential model interacting features non-linearity without strong assumptions. determine feature clusters predictive given trajectory, Mean Decrease Accuracy subset top predictors whose expression directly influences expression target cluster taken indication putative link.","code":"cluster_result <- data |> gClusters(ncluster = 5,elbow.max=15) network_result <- cluster_result |> gNetwork(ntop = 3) gNetwork_view(network_result) #> Warning: Removed 1 rows containing missing values (`geom_segment()`). #> Warning: Removed 1 rows containing missing values (`geom_point()`)."},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"dashboard-navigation","dir":"Articles","previous_headings":"","what":"Dashboard Navigation","title":"Get started","text":"workflow inputs related gDashboard function. key function processes data runs dashboard, requiring input previous steps. ’ve launched Shiny dashboard, can zoom make adjustments explore interesting findings within data. effectively navigate dashboard generated MolPad, ’ll follow three main steps:","code":"gDashboard(data, cluster_result, Annotation_path_taxon, network_result, id_colname = \"ko_term\" , id_type = \"KEGG\")"},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"choose-a-primary-functional-annotation-and-adjust-edge-density","dir":"Articles","previous_headings":"Dashboard Navigation","what":"1. Choose a Primary Functional Annotation and Adjust Edge Density","title":"Get started","text":"Start selecting primary functional annotation available options. , fine-tune edge density adjusting threshold value importance score. Nodes turn bright green indicate clusters containing features related chosen functional annotation.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"explore-the-network","dir":"Articles","previous_headings":"Dashboard Navigation","what":"2. Explore the Network","title":"Get started","text":"Brushing network unveils patterns taxonomic composition typical trajectories. can also zoom specific taxonomic annotations applying filters.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"investigate-feature-details-and-related-function-annotations","dir":"Articles","previous_headings":"Dashboard Navigation","what":"3. Investigate Feature Details and Related Function Annotations","title":"Get started","text":"Delve feature table examine specifics features within selected clusters. Explore additional related function annotations using drop-options. Click provided links access online information items interest. interface encourages iterative exploration, enabling conduct multiple steps answer specific questions, comparing pattern distributions two functions identifying functionally important community members metabolizing feature interest.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"related-visualization-outputs","dir":"Articles","previous_headings":"","what":"Related Visualization Outputs","title":"Get started","text":"following visualizations integrated dashboard, ’s need run function plot individually. ’ve linked stacked bar plot ribbon plot network facilitate focus context explorations. setup helps detecting similar longitudinal patterns understanding relationships.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"network-plot","dir":"Articles","previous_headings":"Related Visualization Outputs","what":"Network Plot","title":"Get started","text":"network plot powerful visualization tool displays relationships different groups features within data. MolPad, network plot helps identify clusters features share similar patterns, revealing underlying connections might immediately obvious. visualizing connections, users can gain clearer understanding structure within data, making easier pinpoint significant associations trends.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"stack-bar-plot","dir":"Articles","previous_headings":"Related Visualization Outputs","what":"Stack Bar Plot","title":"Get started","text":"stacked bar plot provides detailed view composition cluster group dataset. stacking different categories top within single bar, plot allows quick comparison relative proportions across multiple groups. particularly useful microbiome experiments understanding distribution taxa across different conditions time points crucial. stacked bar plot makes easy see distributions change experimental conditions, facilitating deeper insights.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"ribbon-plot","dir":"Articles","previous_headings":"Related Visualization Outputs","what":"Ribbon Plot","title":"Get started","text":"ribbon plot designed visualize changes time, making ideal tool tracking longitudinal data. MolPad, ribbon plot illustrates abundance expression levels features vary across different time points conditions. smooth, flowing design ribbons helps emphasize trends patterns, enabling users quickly identify periods significant change stability. plot particularly advantageous comparing multiple groups, clearly shows overlapping trends divergences, providing comprehensive view temporal dynamics data.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/whymolpad.html","id":"why-molpad","dir":"Articles","previous_headings":"","what":"Why MolPad?","title":"Why MolPad?","text":"increasing multi-omics data longitudinal designs integrated microbiome experiments, growing need present network, especially complex variations across biological modalities. Network perspective helps detect underlying co-occurrence among microbiome samples, allowing high-level insights global structure. Yet comes experimental data records time series 100,000 features, network collapse entangled clumps therefore unable read.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/whymolpad.html","id":"improvements","dir":"Articles","previous_headings":"","what":"Improvements","title":"Why MolPad?","text":"aim network interpretation, MolPad shows improvements 3 important aspects: Capture groups features share longitudinal patterns functional properties within network. Annotate network complex experimental datasets, way dig specific components notable features. Streamlines exploration modularized functions interactive dashboard. package especially facilitates exploration longitudinal molecular co-expression microbiome experiments using focus-plus-context approach, empowering researchers uncover global patterns insights longitudinal microbiomics data. Unlike existing tools, MolPad stands rich network interactivity support contextualization. also equipped beginner-friendly interface self-contained data processing functions, making accessible users limited programming experience.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/whymolpad.html","id":"key-features","dir":"Articles","previous_headings":"","what":"Key Features","title":"Why MolPad?","text":"Trajectory clustering organizing data clusters, helps identify patterns, relationships, underlying structures within dataset. essence, clustering detects groups features observations behave similarly, allowing deeper insights effective decision-making. Network extracting build network clustered trajectories adapting GENIE3 algorithm. Specifically, network learned series regressions, cluster centroid predicted expression patterns cluster centroids using random forests. choose random forests potential model interacting features non-linearity without strong assumptions. Interactive Dashboard package provides interactive dashboard designed help users explore data uncover relationships within complex networks. utilizing focus-plus-context visualization, dashboard allows users zoom specific details keeping broader dataset view. approach enables deeper understanding data, making easier identify patterns make informed decisions.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/whymolpad.html","id":"what-does-it-look-like","dir":"Articles","previous_headings":"","what":"What does it look like?","title":"Why MolPad?","text":"Now, let’s see can get dashboard. ’ll start overview demonstrate discover patterns within data.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/whymolpad.html","id":"molpad-dashboard-overview","dir":"Articles","previous_headings":"What does it look like?","what":"MolPad Dashboard Overview","title":"Why MolPad?","text":"overview MolPad Dashboard. explore dashboard effectively, can start following sequence -B-C-D. approach guide cluster-level network, taxonomic-level bar plot, type-level line plot, feature-level table. , can modify detailed settings identify investigate special patterns within data.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/whymolpad.html","id":"discovering-related-patterns","dir":"Articles","previous_headings":"What does it look like?","what":"Discovering related patterns","title":"Why MolPad?","text":"short example discovering related patterns using network plot: shade edges represents proximity nodes. brushed area, Groups 1-7-8 (circled solid black lines) Groups 1-2 (circled blue dashed lines) strongly linked. Groups 1, 7, 8, patterns form W-shape noticeable peak time section. Although Group 1 shows higher volatility compared Group 2, groups display highly overlapping increasing trend. detailed instructions, please visit “Get Started” page relevant function reference page.","code":""},{"path":"https://kaiyanm.github.io/MolPad/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Kaiyan Ma. Author, maintainer.","code":""},{"path":"https://kaiyanm.github.io/MolPad/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Ma K (2024). MolPad: MolPad: R-Shiny Package Cluster Co-Expression Analysis Longitudinal Microbiomics. R package version 0.1.0, https://kaiyanm.github.io/MolPad/.","code":"@Manual{, title = {MolPad: MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics}, author = {Kaiyan Ma}, year = {2024}, note = {R package version 0.1.0}, url = {https://kaiyanm.github.io/MolPad/}, }"},{"path":"https://kaiyanm.github.io/MolPad/index.html","id":"molpad-","dir":"","previous_headings":"","what":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","title":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","text":"R-Shiny Package Cluster Co-Expression Analysis Longitudinal Microbiomics","code":""},{"path":"https://kaiyanm.github.io/MolPad/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","text":"MolPad offers visualization dashboard tool designed enhance understanding molecular co-expression works context microbiome data. approach involves using cluster network provide initial overview relationships across multiple omics, added functionality interactively zoom specific areas interest. facilitate analysis, ’ve developed focus-plus-context strategy connects online curated annotations. Additionally, package simplifies entire pipeline creating dashboard. user-friendly design makes accessible even people limited R programming experience. Check cheese-data demo .","code":""},{"path":"https://kaiyanm.github.io/MolPad/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","text":"can either install devtools, {r, eval = FALSE} # Install package R: install.packages(\"devtools\") library(devtools) install_github(\"KaiyanM/MolPad\") clone repository local computer (example, onto ./Github) installing: {r, eval = FALSE} # Download install package R: setwd(\"./GitHub\") install(\"MolPad\") , load package: {r,eval=FALSE} library(MolPad)","code":""},{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/index.html","id":"molpad-could-help-you-with","dir":"","previous_headings":"Usage","what":"MolPad could help you with:","title":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","text":"Clustering data k-means building group network. Find significant trend patterns datasets. Target interaction groups, taxons, pathways. Visualize distribution features specific pathways group network. Search particular features user-defined labels. Check detailed information feature automatically generated hyperlinks. better overall understanding datasets.","code":""},{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/index.html","id":"getting-help","dir":"","previous_headings":"","what":"Getting Help","title":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","text":"need assistance MolPad, two primary ways seek help: Ask us anything related MolPad! add question, create issue repository. Stack Overflow another excellent resource answering common issues R. Remember ’s particularly effective can provide reproducible example shows specific problem ’re .","code":""},{"path":"https://kaiyanm.github.io/MolPad/index.html","id":"contribution","dir":"","previous_headings":"","what":"Contribution","title":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","text":"contribute project, use following workflow: fork repository –> create local copy –> submit pull request.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":null,"dir":"Reference","previous_headings":"","what":"Cheese data — cheese","title":"Cheese data — cheese","text":"context cheese production, regular application brine solution maturation technique promotes uniformity microbial populations cheese's surface facilitates interactions among microorganisms. investigation involved analysis longitudinal dataset encompassing three washed-rind cheese communities sampled ripening process.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Cheese data — cheese","text":"two datasets: cheese annotations","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Cheese data — cheese","text":"Reference: doi:10.1128/msystems.00701-22","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Cheese data — cheese","text":"study uncovered remarkably consistent microbial progression within cheese. bacterial community, Firmicutes dominate outset, Proteobacteria swiftly assuming dominance end ripening period. Additionally, Cheese Cheese C consistently demonstrate establishment Actinobacteria Bacteroidetes, distinct manner. corroborate findings using MolPad dashboard, conducted analysis two cheeses (C) three production batches, spanning weeks 2 13.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":"cheese-data","dir":"Reference","previous_headings":"","what":"cheese data","title":"Cheese data — cheese","text":"data frame 106239 rows 18 variables: ID sample ID A_1~A_5 Time series data measured cheese . C_1~C_5 Time series data measured cheese C. domain category feature phylum category feature class category feature order category feature family category feature genus category feature","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":"annotation-data","dir":"Reference","previous_headings":"","what":"annotation data","title":"Cheese data — cheese","text":"data frame 86156 rows 9 variables: ID sample ID GO_ID GO IDs, represents link gene product type molecular function KEGG_ID KEGG IDs, linking genomic information higher order functional information. domain category feature phylum category feature class category feature order category feature family category feature genus category feature","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cheese data — cheese","text":"","code":"data(cheese) head(annotations) #> # A tibble: 6 × 9 #> ID GO_ID KEGG_ID domain phylum class order family genus #> #> 1 9 - - Bacteria Pseudomonadota Alphaproteobac… Caul… Caulo… Caul… #> 2 10 - - Bacteria Pseudomonadota Alphaproteobac… Caul… Caulo… Caul… #> 3 11 - - Bacteria Pseudomonadota Alphaproteobac… Hyph… Barto… Bart… #> 4 12 - - Bacteria Pseudomonadota Alphaproteobac… Hyph… Bruce… Bruc… #> 5 13 - - Bacteria Pseudomonadota Alphaproteobac… Hyph… Nitro… Brad… #> 6 14 - - Bacteria Pseudomonadota Alphaproteobac… Hyph… Phyll… Meso… head(cheese) #> # A tibble: 6 × 18 #> ID A_1 A_2 A_3 A_4 A_5 C_1 C_3 C_4 C_5 GO_ID KEGG_ID #> #> 1 1 38 23 27 5 11 13 1 3 0 NA NA #> 2 2 23 6 4 0 9 21 1 0 2 NA NA #> 3 3 24 5 37 10 19 3 0 1 4 NA NA #> 4 4 3 2 4 2 16 56 1 7 17 NA NA #> 5 5 58 14 45 13 32 82 3 8 3 NA NA #> 6 6 12 9 14 4 13 2 0 1 4 NA NA #> # ℹ 6 more variables: domain , phylum , class , order , #> # family , genus "},{"path":"https://kaiyanm.github.io/MolPad/reference/color_palettes__.html","id":null,"dir":"Reference","previous_headings":"","what":"Color palettes — color_palettes__","title":"Color palettes — color_palettes__","text":"internal function built-color palettes.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/color_palettes__.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Color palettes — color_palettes__","text":"","code":"color_palettes__(name)"},{"path":"https://kaiyanm.github.io/MolPad/reference/color_palettes__.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Color palettes — color_palettes__","text":"name string two options: \"graytone\" \"darkwarm\".","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/color_palettes__.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Color palettes — color_palettes__","text":"","code":"color_palettes__(\"graytone\") #> [1] \"#c3c5c7\" \"#30beba\" \"#998a73\" \"#807566\" \"#a5b1c9\" \"#5d5232\" \"#9a2b41\" #> [8] \"#d6b7a2\" \"#882db4\" \"#b47a53\" color_palettes__(\"darkwarm\") #> [1] \"#251305\" \"#C70A80\" \"#FBCB0A\" \"#ff1122\" \"#7D7463\" \"#CECE5A\" \"#FF9B9B\" #> [8] \"#A459D1\" \"#00235B\" \"#7E1717\""},{"path":"https://kaiyanm.github.io/MolPad/reference/convert_range.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert range — convert_range","title":"Convert range — convert_range","text":"internal function range conversion. element x, returns distance minimal values divided range.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/convert_range.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert range — convert_range","text":"","code":"convert_range(x)"},{"path":"https://kaiyanm.github.io/MolPad/reference/convert_range.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert range — convert_range","text":"x vector numbers.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/convert_range.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert range — convert_range","text":"vector calculated (x - min(x)) / (max(x) - min(x))","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/convert_range.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert range — convert_range","text":"","code":"convert_range(5:10) #> [1] 0.0 0.2 0.4 0.6 0.8 1.0"},{"path":"https://kaiyanm.github.io/MolPad/reference/extend.color__.html","id":null,"dir":"Reference","previous_headings":"","what":"Extend color palettes — extend.color__","title":"Extend color palettes — extend.color__","text":"internal function designed pair vectors color palettes automatically generate colors longer vectors find match.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/extend.color__.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extend color palettes — extend.color__","text":"","code":"extend.color__(n, colors, extendby = 1, alpha = 1, ...)"},{"path":"https://kaiyanm.github.io/MolPad/reference/extend.color__.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extend color palettes — extend.color__","text":"n number. length feature vector want match colors. colors vector colors (finite). extendby number select 1, 2, 3, 4 5, representing distinct auto-fill schemes. alpha number select range 0,1.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gAnnotation.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate processed annotation — gAnnotation","title":"Generate processed annotation — gAnnotation","text":"gAnnotation() provides standard input format dashboard, allowing users select two columns primary factors wish visualize describe data.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gAnnotation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate processed annotation — gAnnotation","text":"","code":"gAnnotation(data, first_order, second_order)"},{"path":"https://kaiyanm.github.io/MolPad/reference/gAnnotation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate processed annotation — gAnnotation","text":"data data.frame containing annotations used describing features measured time point. also required include ID least two categorical variables. first_order string. name one column categorical variable data. second_order string. name another column categorical variable data.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gAnnotation.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate processed annotation — gAnnotation","text":"Guidelines Selecting Annotations: first-order annotation recommended functional, pathway functional system. parameter primarily serve purpose filtering one network time displaying dashboard. second-order annotation utilized illustrate composition first-order annotation using bar plot. Therefore, better set taxon, class label, etc.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gAnnotation.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate processed annotation — gAnnotation","text":"","code":"data(test_data) test_annotations_processed <- gAnnotation(test_annotations,system,class) head(test_annotations_processed) #> ID #> 1 1 #> 2 2 #> 3 3 #> 4 4 #> 5 5 #> 6 6 #> GO_ID #> 1 #> 2 #> 3 GO:0003674,GO:0003824,GO:0005575,GO:0006629,GO:0006644,GO:0006650,GO:0006655,GO:0006793,GO:0006796,GO:0008150,GO:0008152,GO:0008610,GO:0008654,GO:0008808,GO:0009058,GO:0009987,GO:0016020,GO:0016740,GO:0016772,GO:0016780,GO:0019637,GO:0030572,GO:0032048,GO:0032049,GO:0044237,GO:0044238,GO:0044249,GO:0044255,GO:0045017,GO:0046471,GO:0046474,GO:0046486,GO:0071704,GO:0090407,GO:1901576 #> 4 GO:0003674,GO:0003676,GO:0003723,GO:0003729,GO:0003824,GO:0004654,GO:0005488,GO:0005575,GO:0005622,GO:0005623,GO:0005737,GO:0006139,GO:0006401,GO:0006402,GO:0006725,GO:0006807,GO:0008150,GO:0008152,GO:0009056,GO:0009057,GO:0009892,GO:0009987,GO:0010468,GO:0010605,GO:0010629,GO:0016070,GO:0016071,GO:0016740,GO:0016772,GO:0016779,GO:0019222,GO:0019439,GO:0034641,GO:0034655,GO:0043170,GO:0044237,GO:0044238,GO:0044248,GO:0044260,GO:0044265,GO:0044270,GO:0044424,GO:0044464,GO:0046483,GO:0046700,GO:0048519,GO:0050789,GO:0060255,GO:0065007,GO:0071704,GO:0090304,GO:0097159,GO:1901360,GO:1901361,GO:1901363,GO:1901575 #> 5 #> 6 #> KEGG_ID Pathway taxonomic.scope #> 1 K07124 Integumentary System hormonal proteins #> 2 Skeletal System structural proteins #> 3 K06131 Muscular System enzymes #> 4 K00962 Nervous System contractile proteins #> 5 K02083 Endocrine System contractile proteins #> 6 Cardiovascular System structural proteins"},{"path":"https://kaiyanm.github.io/MolPad/reference/gClusters.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate clusters — gClusters","title":"Generate clusters — gClusters","text":"gClusters() returns clusters generated k-means yield elbow plot way finding optimal parameter.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gClusters.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate clusters — gClusters","text":"","code":"gClusters(data, ncluster = 20, elbow.max = 50, ...)"},{"path":"https://kaiyanm.github.io/MolPad/reference/gClusters.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate clusters — gClusters","text":"data scaled data.frame contain variables ID, value time_1, ..., value time_k, type extracting patterns across time. See also pre_process(). ncluster number clusters. related complexity information network: choosing ncluster, suggest thinking many nodes show visualization representative want clustered pattern. elbow.max number maximum value x-axis elbow method plot. larger expected ncluster smaller sample size. iter.max number maximum iterations allowed k-means. See also stats::kmeans. nstart number random attempts generating initial configurations. k-means algorithm choose best one among attempts. larger data, 'nstart' can set lower just set 1. See also stats::kmeans.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gClusters.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate clusters — gClusters","text":"function return list 2 elements: k-means cluster result elbow method plot.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gClusters.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate clusters — gClusters","text":"determine optimal number clusters (ncluster), advised closely examine elbow plot identify point graph substantial change 'elbow' occurs. often indicative suitable cluster count. cases dataset extensive intricate, might consider increasing value elbow.max ensure comprehensive exploration potential cluster counts. can help achieving accurate meaningful results, especially working larger complex datasets. function can executed data parameter outset. However, achieve best clustering results, adjustments recommended. initial run, users expected adjust function's parameters based clustering outcomes elbow plot analysis.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gClusters.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate clusters — gClusters","text":"","code":"data(test_data) reslist <- gClusters(test_data_processed) # k-means result reslist[[1]] #> K-means clustering with 20 clusters of sizes 6, 3, 6, 5, 5, 8, 6, 5, 5, 6, 4, 11, 3, 3, 3, 2, 4, 2, 2, 11 #> #> Cluster means: #> T1 T2 T3 T4 T5 T6 #> 1 2.0919348 -0.33320285 -0.50786563 -0.5579659 -0.5579659 1.5455977 #> 2 0.8277105 1.32036794 -0.09261332 1.2325986 0.4199176 -0.9576634 #> 3 0.5077879 -0.24564283 0.53503708 0.2363183 1.0793829 1.3583848 #> 4 0.2914366 -0.41308350 -0.76541563 -0.9724353 -0.9724353 1.6040887 #> 5 1.2081037 -0.66790409 -0.66790409 -0.9275526 -0.4204221 1.2719890 #> 6 0.1155971 -0.19000668 -0.25629794 -0.1487697 -0.4383348 2.5830148 #> 7 1.2574004 -0.64914997 -0.87502695 0.9353966 0.4760273 1.2681050 #> 8 0.9389212 -0.86278238 0.43887930 1.0009533 1.3733646 -1.0423264 #> 9 -0.8135539 -0.84696982 -0.32573017 1.1218534 2.0955812 -0.5620731 #> 10 -0.3802219 -0.36840199 0.02254271 1.6459653 1.9633618 -0.3246455 #> 11 -0.1225506 -0.44139873 -0.19548572 1.0299038 2.3840629 -0.2147528 #> 12 2.5784883 -0.02446156 -0.60355041 -0.2547486 -0.5120230 -0.3947043 #> 13 0.9922755 0.32399254 -0.94565021 -0.7125240 -0.5104935 -0.9456502 #> 14 0.2699801 -0.08094677 -0.28926312 -0.7813127 -0.6065051 -0.1494523 #> 15 1.7947637 -0.47746004 -0.52783931 -0.7635958 -0.7635958 -0.6438957 #> 16 0.1789247 1.26157166 0.82115649 0.3899368 1.2707671 -0.2614905 #> 17 1.4517382 0.26236463 1.31600581 -0.2531012 0.2004547 -1.1238942 #> 18 -0.2784769 -0.82258408 -0.82258408 -0.8225841 1.2338761 -0.8225841 #> 19 2.1235865 1.05868538 -0.39467611 0.1315587 -0.1315587 0.3946761 #> 20 -0.2262821 -0.73107613 -0.66098092 -0.4323193 -0.4035074 -0.3836848 #> T7 T8 T9 T10 #> 1 -0.425513174 -0.557965933 -0.30571480 -0.391338269 #> 2 -1.160107459 -0.295057401 -0.64757654 -0.647576540 #> 3 -1.006282312 -0.766188425 -0.64803067 -1.050766722 #> 4 0.301678216 -0.371684852 0.66435231 0.633498705 #> 5 1.209152596 -0.095960244 -0.32347149 -0.586030729 #> 6 -0.006327004 -0.327677972 -0.66559889 -0.665598891 #> 7 -0.354972016 -0.601904415 -0.82778139 -0.628094550 #> 8 -1.159101258 -0.345888528 -0.27147562 -0.070544200 #> 9 -0.651292750 -0.498839228 0.42446013 0.056564270 #> 10 -0.647007074 -0.667438225 -0.63229313 -0.611861976 #> 11 -0.400726188 -0.616324716 -0.71136401 -0.711364008 #> 12 -0.526022073 -0.185303954 -0.06989626 -0.007778173 #> 13 -0.945650212 0.121962031 1.19430596 1.427432164 #> 14 -0.763902351 -0.412975440 0.39220455 2.422173070 #> 15 -0.260645101 0.389799714 -0.08327821 1.335746686 #> 16 -0.912917717 -0.481698036 -1.13312530 -1.133125299 #> 17 -0.987867401 -0.340013670 -0.33085684 -0.194830041 #> 18 -0.822584085 1.233876127 1.23387613 0.689768939 #> 19 -0.795567972 -0.795567972 -0.79556797 -0.795567972 #> 20 -0.612869917 -0.006483243 1.70560793 1.751595921 #> #> Clustering vector: #> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 #> 20 1 18 16 3 20 5 19 10 9 8 14 15 12 2 9 6 20 17 12 #> 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 #> 7 20 6 1 12 9 7 12 6 11 5 11 16 18 10 3 8 13 20 2 #> 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 #> 6 1 20 20 7 20 6 3 1 6 15 5 4 13 6 5 4 10 12 10 #> 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 #> 8 3 7 13 3 1 12 7 17 17 14 9 6 19 14 10 2 11 8 7 #> 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 #> 8 20 15 4 4 1 17 12 4 12 10 5 11 12 20 3 9 12 20 12 #> #> Within cluster sum of squares by cluster: #> [1] 3.0260293 5.0357352 13.2343206 13.1118010 10.3695240 8.0412334 #> [7] 11.7251902 8.3255313 3.4617088 2.4693324 1.6353685 13.0645655 #> [13] 2.9416715 3.1842269 5.0413438 2.4669004 9.9418120 0.9921913 #> [19] 0.9833964 12.4042363 #> (between_SS / total_SS = 83.1 %) #> #> Available components: #> #> [1] \"cluster\" \"centers\" \"totss\" \"withinss\" \"tot.withinss\" #> [6] \"betweenss\" \"size\" \"iter\" \"ifault\" # elbow plot reslist[[2]]"},{"path":"https://kaiyanm.github.io/MolPad/reference/gDashboard.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate shiny dashboard — gDashboard","title":"Generate shiny dashboard — gDashboard","text":"outputs g-functions, ready create custom Molpad dashboard. sure specify web ID columns corresponding column names.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gDashboard.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate shiny dashboard — gDashboard","text":"","code":"gDashboard( data, cluster, annotation, networkres, dashboardtitle = \"MolPad Dashboard\", id_colname = NULL, id_type = NULL )"},{"path":"https://kaiyanm.github.io/MolPad/reference/gDashboard.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate shiny dashboard — gDashboard","text":"data output pre_process():scaled data.frame contain variables ID, value time_1, ..., value time_k, type. cluster output gClusters(): list contains result k-means Cluster means, vectors sum squares. annotation output gAnnotation(): data.frame containing annotations describing features. Variables must include ID, Pathway, taxonomic.scope. Note NA permitted 3 variables. networkres output gNetwork(): data.frame 4 variables weight,IncNodePurity,var_names,. dashboardtitle string customized dashboard name. id_colname single string string vector. column names annotation dataset vector contains external database IDs. id_type single string string vector. corresponding database names columns, must chosen \"KEGG\" \"GO\".","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gDashboard.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate shiny dashboard — gDashboard","text":"Please ensure columns containing external database IDs adhere following standards: KEGG ID: Begin 'K' followed 5 digits, example, K05685 K06671. GO ID: Begin 'GO:' followed 7 digits, GO:0003674.\"","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gDashboard.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate shiny dashboard — gDashboard","text":"","code":"if (FALSE) { data(test_data) gDashboard(test_data_processed,test_cluster,test_annotations_processed,test_network,id_colname = c(\"GO_ID\",\"KEGG_ID\"),id_type = c(\"GO\",\"KEGG\")) }"},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate prediction network — gNetwork","title":"Generate prediction network — gNetwork","text":"gNetwork() generates prediction network functional annotation. every feature, features considered independent variables, top predictors selected based %IncMSE.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate prediction network — gNetwork","text":"","code":"gNetwork(clusters, ntop = 10, method = \"randomforest\")"},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate prediction network — gNetwork","text":"clusters list two outputs gClusters(). first element k-means result (See also stats::kmeans), element plot automatically omitted ease directly passing results function. ntop number pick set top n predictors(default: top 10) feature important ones. used construct network among clustered patterns. seed number random seed.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate prediction network — gNetwork","text":"assess relationships clusters using Mean Squared Error (MSE) changes resulting random shuffling. gNetwork()'s output includes edge weights node pairs, essential inputs dashboard.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate prediction network — gNetwork","text":"","code":"data(test_data) networkres <- gNetwork(test_cluster, ntop = 3) head(networkres) #> weight IncNodePurity var_names from #> 1 1.4215243 1.1776012 Group_5 Group_1 #> 2 0.7911266 0.7011049 Group_3 Group_1 #> 3 -1.9891284 0.5811186 Group_2 Group_1 #> 4 5.8098014 1.1107592 Group_4 Group_2 #> 5 5.4000389 0.8744358 Group_5 Group_2 #> 6 1.2492288 1.1816976 Group_1 Group_2 gNetwork_view(networkres)"},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork_view.html","id":null,"dir":"Reference","previous_headings":"","what":"View the feature importance plot — gNetwork_view","title":"View the feature importance plot — gNetwork_view","text":"gNetwork_view() Generate feature importance plot random forest result. plot visually highlights importance individual cluster within dataset, helping identify key factors predictive modeling.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork_view.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"View the feature importance plot — gNetwork_view","text":"","code":"gNetwork_view(data)"},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork_view.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"View the feature importance plot — gNetwork_view","text":"data dataframe; output gNetwork(), including 4 variables: weight, IncNodePurity, var_names . weight edge value, IncNodePurity(%IncMSE) measure predictions result var_names permuted. dependent variable round random forest.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork_view.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"View the feature importance plot — gNetwork_view","text":"","code":"data(test_data) gNetwork_view(test_network) #> Warning: Removed 1 rows containing missing values (`geom_segment()`). #> Warning: Removed 1 rows containing missing values (`geom_point()`)."},{"path":"https://kaiyanm.github.io/MolPad/reference/make_line_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Make line plot — make_line_plot","title":"Make line plot — make_line_plot","text":"Generate line ribbon plot dashboard.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_line_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make line plot — make_line_plot","text":"","code":"make_line_plot(dfgroup_long, selected_groups, selected_taxa)"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_line_plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Make line plot — make_line_plot","text":"function makes ribbon plot every brush action Shiny show min, max, mean value clustered pattern group across time. ribbon plot grouped colored type variable input datasets. See also ggplot2::geom_ribbon.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_line_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Make line plot — make_line_plot","text":"","code":"data(test_data) make_line_plot(test_maindata, \"Group_5\", c(\"hormonal proteins\",\"structural proteins\",\"enzymes\",\"storage proteins\",\"antibodies\",\"transport proteins\"))"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_stackbar_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Make stackbar plot — make_stackbar_plot","title":"Make stackbar plot — make_stackbar_plot","text":"Generate stackbar plot dashboard.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_stackbar_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make stackbar plot — make_stackbar_plot","text":"","code":"make_stackbar_plot(dfgroup_long, selected_groups, selected_taxa)"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_stackbar_plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Make stackbar plot — make_stackbar_plot","text":"function makes bar plot every brush action Shiny show components clustered pattern group. bar plot colored taxonomic.scope variable processed annotation dataset generated gAnnotation(). See also ggplot2::geom_bar.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_stackbar_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Make stackbar plot — make_stackbar_plot","text":"","code":"data(test_data) make_stackbar_plot(test_maindata, \"Group_5\", c(\"hormonal proteins\",\"structural proteins\",\"enzymes\",\"storage proteins\",\"antibodies\",\"transport proteins\"))"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_graph.html","id":null,"dir":"Reference","previous_headings":"","what":"Make graph plot — make_the_graph","title":"Make graph plot — make_the_graph","text":"Generate graph dashboard.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_graph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make graph plot — make_the_graph","text":"","code":"make_the_graph(ptw, network_output, min_weight, s_ptw, graph_layout)"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_graph.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Make graph plot — make_the_graph","text":"function makes network plot every Pathway selection Shiny show relationship among clustered pattern groups. network plot built dataset generated gNetwork() can adjusted layout minimum weights. See also ggraph::ggraph.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_graph.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Make graph plot — make_the_graph","text":"","code":"data(test_data) make_the_graph(test_graphptw, test_network, 0.03, \"Muscular System\",\"kk\")"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_table.html","id":null,"dir":"Reference","previous_headings":"","what":"Make table from brushed region — make_the_table","title":"Make table from brushed region — make_the_table","text":"Generate table selected groups dashboard.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make table from brushed region — make_the_table","text":"","code":"make_the_table(p)"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Make table from brushed region — make_the_table","text":"p ggplot output, see also ggplot2::ggplot_build.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_table.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Make table from brushed region — make_the_table","text":"function simply aims collect position information brushed area network plot returns annotation table corresponding features. takes plot object produce object information table.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/mass_produce_lm__.html","id":null,"dir":"Reference","previous_headings":"","what":"Mass produce linear model — mass_produce_lm__","title":"Mass produce linear model — mass_produce_lm__","text":"Take column dependent vairable time, produce n linear models n columns dataset.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/mass_produce_lm__.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mass produce linear model — mass_produce_lm__","text":"","code":"mass_produce_lm__(data)"},{"path":"https://kaiyanm.github.io/MolPad/reference/mass_produce_lm__.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mass produce linear model — mass_produce_lm__","text":"data dataframe.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/mass_produce_lm__.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Mass produce linear model — mass_produce_lm__","text":"internal function designed automatically generate list functions regression. column considered response variable columns. resulting functions can utilized inputs random forest regression gNetwork().","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/match.color__.html","id":null,"dir":"Reference","previous_headings":"","what":"Match color — match.color__","title":"Match color — match.color__","text":"Match vector finite list colors.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/match.color__.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Match color — match.color__","text":"","code":"match.color__(valist, mycolors, extendby = 5)"},{"path":"https://kaiyanm.github.io/MolPad/reference/match.color__.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Match color — match.color__","text":"valist vector want specify colors element. mycolors vector colors. extendby number select 1, 2, 3, 4, 5, representing distinct auto-fill schemes.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/match.color__.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Match color — match.color__","text":"","code":"my_vector <- paste0(\"N_\",1:10) match.color__(my_vector,c(\"red\",\"yellow\",\"blue\")) #> N_1 N_2 N_3 N_4 N_5 #> \"red\" \"yellow\" \"blue\" \"white\" \"antiquewhite3\" #> N_6 N_7 N_8 N_9 N_10 #> \"aquamarine3\" \"azure3\" \"bisque2\" \"blue\" \"blueviolet\""},{"path":"https://kaiyanm.github.io/MolPad/reference/match_database.html","id":null,"dir":"Reference","previous_headings":"","what":"Match database — match_database","title":"Match database — match_database","text":"matches selected columns online database names.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/match_database.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Match database — match_database","text":"","code":"match_database(data, id_colname, id_type)"},{"path":"https://kaiyanm.github.io/MolPad/reference/match_database.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Match database — match_database","text":"match_database() internal function matches selected columns dataset corresponding databases. Currently MolPad support two types annotation database: GO KEGG. want custom URL platform, download modify paste_URL() function.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/match_database.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Match database — match_database","text":"","code":"data(test_data) head(test_annotations_processed) #> ID #> 1 1 #> 2 2 #> 3 3 #> 4 4 #> 5 5 #> 6 6 #> GO_ID #> 1 #> 2 #> 3 GO:0003674,GO:0003824,GO:0005575,GO:0006629,GO:0006644,GO:0006650,GO:0006655,GO:0006793,GO:0006796,GO:0008150,GO:0008152,GO:0008610,GO:0008654,GO:0008808,GO:0009058,GO:0009987,GO:0016020,GO:0016740,GO:0016772,GO:0016780,GO:0019637,GO:0030572,GO:0032048,GO:0032049,GO:0044237,GO:0044238,GO:0044249,GO:0044255,GO:0045017,GO:0046471,GO:0046474,GO:0046486,GO:0071704,GO:0090407,GO:1901576 #> 4 GO:0003674,GO:0003676,GO:0003723,GO:0003729,GO:0003824,GO:0004654,GO:0005488,GO:0005575,GO:0005622,GO:0005623,GO:0005737,GO:0006139,GO:0006401,GO:0006402,GO:0006725,GO:0006807,GO:0008150,GO:0008152,GO:0009056,GO:0009057,GO:0009892,GO:0009987,GO:0010468,GO:0010605,GO:0010629,GO:0016070,GO:0016071,GO:0016740,GO:0016772,GO:0016779,GO:0019222,GO:0019439,GO:0034641,GO:0034655,GO:0043170,GO:0044237,GO:0044238,GO:0044248,GO:0044260,GO:0044265,GO:0044270,GO:0044424,GO:0044464,GO:0046483,GO:0046700,GO:0048519,GO:0050789,GO:0060255,GO:0065007,GO:0071704,GO:0090304,GO:0097159,GO:1901360,GO:1901361,GO:1901363,GO:1901575 #> 5 #> 6 #> KEGG_ID Pathway taxonomic.scope #> 1 K07124 Integumentary System hormonal proteins #> 2 Skeletal System structural proteins #> 3 K06131 Muscular System enzymes #> 4 K00962 Nervous System contractile proteins #> 5 K02083 Endocrine System contractile proteins #> 6 Cardiovascular System structural proteins head(match_database(test_annotations_processed,id_colname = c(\"GO_ID\",\"KEGG_ID\"),id_type = c(\"GO\",\"KEGG\"))) #> ID #> 1 1 #> 2 2 #> 3 3 #> 4 4 #> 5 5 #> 6 6 #> GO_ID #> 1 #> 2 #> 3 GO:0003674<\/a>
GO:0003824<\/a>
GO:0005575<\/a>
GO:0006629<\/a>
GO:0006644<\/a>
GO:0006650<\/a>
GO:0006655<\/a>
GO:0006793<\/a>
GO:0006796<\/a>
GO:0008150<\/a>
GO:0008152<\/a>
GO:0008610<\/a>
GO:0008654<\/a>
GO:0008808<\/a>
GO:0009058<\/a>
GO:0009987<\/a>
GO:0016020<\/a>
GO:0016740<\/a>
GO:0016772<\/a>
GO:0016780<\/a>
GO:0019637<\/a>
GO:0030572<\/a>
GO:0032048<\/a>
GO:0032049<\/a>
GO:0044237<\/a>
GO:0044238<\/a>
GO:0044249<\/a>
GO:0044255<\/a>
GO:0045017<\/a>
GO:0046471<\/a>
GO:0046474<\/a>
GO:0046486<\/a>
GO:0071704<\/a>
GO:0090407<\/a>
GO:1901576<\/a> #> 4 GO:0003674<\/a>
GO:0003676<\/a>
GO:0003723<\/a>
GO:0003729<\/a>
GO:0003824<\/a>
GO:0004654<\/a>
GO:0005488<\/a>
GO:0005575<\/a>
GO:0005622<\/a>
GO:0005623<\/a>
GO:0005737<\/a>
GO:0006139<\/a>
GO:0006401<\/a>
GO:0006402<\/a>
GO:0006725<\/a>
GO:0006807<\/a>
GO:0008150<\/a>
GO:0008152<\/a>
GO:0009056<\/a>
GO:0009057<\/a>
GO:0009892<\/a>
GO:0009987<\/a>
GO:0010468<\/a>
GO:0010605<\/a>
GO:0010629<\/a>
GO:0016070<\/a>
GO:0016071<\/a>
GO:0016740<\/a>
GO:0016772<\/a>
GO:0016779<\/a>
GO:0019222<\/a>
GO:0019439<\/a>
GO:0034641<\/a>
GO:0034655<\/a>
GO:0043170<\/a>
GO:0044237<\/a>
GO:0044238<\/a>
GO:0044248<\/a>
GO:0044260<\/a>
GO:0044265<\/a>
GO:0044270<\/a>
GO:0044424<\/a>
GO:0044464<\/a>
GO:0046483<\/a>
GO:0046700<\/a>
GO:0048519<\/a>
GO:0050789<\/a>
GO:0060255<\/a>
GO:0065007<\/a>
GO:0071704<\/a>
GO:0090304<\/a>
GO:0097159<\/a>
GO:1901360<\/a>
GO:1901361<\/a>
GO:1901363<\/a>
GO:1901575<\/a> #> 5 #> 6 #> KEGG_ID #> 1 K07124<\/a> #> 2 #> 3 K06131<\/a> #> 4 K00962<\/a> #> 5 K02083<\/a> #> 6 #> Pathway taxonomic.scope #> 1 Integumentary System hormonal proteins #> 2 Skeletal System structural proteins #> 3 Muscular System enzymes #> 4 Nervous System contractile proteins #> 5 Endocrine System contractile proteins #> 6 Cardiovascular System structural proteins"},{"path":"https://kaiyanm.github.io/MolPad/reference/paste_URL.html","id":null,"dir":"Reference","previous_headings":"","what":"Paste URL — paste_URL","title":"Paste URL — paste_URL","text":"Retrieve database IDs associate respective URLs.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/paste_URL.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Paste URL — paste_URL","text":"","code":"paste_URL(x, id_type)"},{"path":"https://kaiyanm.github.io/MolPad/reference/paste_URL.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Paste URL — paste_URL","text":"","code":"data(test_data) paste_URL(test_annotations$GO_ID[1:4], id_type = \"GO\") #> [1] NA #> [2] NA #> [3] \"GO:0003674<\/a>
GO:0003824<\/a>
GO:0005575<\/a>
GO:0006629<\/a>
GO:0006644<\/a>
GO:0006650<\/a>
GO:0006655<\/a>
GO:0006793<\/a>
GO:0006796<\/a>
GO:0008150<\/a>
GO:0008152<\/a>
GO:0008610<\/a>
GO:0008654<\/a>
GO:0008808<\/a>
GO:0009058<\/a>
GO:0009987<\/a>
GO:0016020<\/a>
GO:0016740<\/a>
GO:0016772<\/a>
GO:0016780<\/a>
GO:0019637<\/a>
GO:0030572<\/a>
GO:0032048<\/a>
GO:0032049<\/a>
GO:0044237<\/a>
GO:0044238<\/a>
GO:0044249<\/a>
GO:0044255<\/a>
GO:0045017<\/a>
GO:0046471<\/a>
GO:0046474<\/a>
GO:0046486<\/a>
GO:0071704<\/a>
GO:0090407<\/a>
GO:1901576<\/a>\" #> [4] \"GO:0003674<\/a>
GO:0003676<\/a>
GO:0003723<\/a>
GO:0003729<\/a>
GO:0003824<\/a>
GO:0004654<\/a>
GO:0005488<\/a>
GO:0005575<\/a>
GO:0005622<\/a>
GO:0005623<\/a>
GO:0005737<\/a>
GO:0006139<\/a>
GO:0006401<\/a>
GO:0006402<\/a>
GO:0006725<\/a>
GO:0006807<\/a>
GO:0008150<\/a>
GO:0008152<\/a>
GO:0009056<\/a>
GO:0009057<\/a>
GO:0009892<\/a>
GO:0009987<\/a>
GO:0010468<\/a>
GO:0010605<\/a>
GO:0010629<\/a>
GO:0016070<\/a>
GO:0016071<\/a>
GO:0016740<\/a>
GO:0016772<\/a>
GO:0016779<\/a>
GO:0019222<\/a>
GO:0019439<\/a>
GO:0034641<\/a>
GO:0034655<\/a>
GO:0043170<\/a>
GO:0044237<\/a>
GO:0044238<\/a>
GO:0044248<\/a>
GO:0044260<\/a>
GO:0044265<\/a>
GO:0044270<\/a>
GO:0044424<\/a>
GO:0044464<\/a>
GO:0046483<\/a>
GO:0046700<\/a>
GO:0048519<\/a>
GO:0050789<\/a>
GO:0060255<\/a>
GO:0065007<\/a>
GO:0071704<\/a>
GO:0090304<\/a>
GO:0097159<\/a>
GO:1901360<\/a>
GO:1901361<\/a>
GO:1901363<\/a>
GO:1901575<\/a>\""},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":null,"dir":"Reference","previous_headings":"","what":"Pre-processing datasets — pre_process","title":"Pre-processing datasets — pre_process","text":"pre_process() function aids processing data inputs automatically establishes standardized format future use. allows two types data input: list datasets different sources long dataset containing specified last column type.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pre-processing datasets — pre_process","text":"","code":"pre_process( data, typenameList = NULL, replaceNA = TRUE, scale = TRUE, autoColName = \"Sec_\" )"},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pre-processing datasets — pre_process","text":"data data.frame describe feature one row. data contain variables ID value time_1, ..., value time_k, type extracting patterns across time. Note initial last column must exactly ID type. multiple data.frame format needs analyzed, also put list data.frame argument. case, variable type required generated next argument typenameList. typenameList vector strings. parameter used clarify source names data.frame, applicable input data list data.frame. default, set \"Dataset_1\", \"Dataset_2\", ..., etc. scale Logical; scale TRUE (default), standardize data.frame row base::scale. converts original value z-score. See also scale_by_row__(). autoColName string; autoColName -NULL (default), automatically set uniform column names data.frames. parameter applicable input data list data.frame. replaceNa Logical; replaceNa TRUE (default), replace NA 0.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pre-processing datasets — pre_process","text":"function returns long data.frame columns ID, value time_1, ..., value time_k, type.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Pre-processing datasets — pre_process","text":"consider two distinct scenarios application: one scenario, individuals collect several datasets various aspects instruments objects. example, might separately detecting lipids, metabolites, peptides specific soil sample. scenario, data uniform quality, can categorized larger groups exhibit significant differences. cases, pre_process() function serves valuable versatile tool. Yet, function optional generating dashboard. Users can perform processing long format matches required output. However, mindful number samples (timepoints) must greater 5 avoid potential errors subsequent prediction section.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pre-processing datasets — pre_process","text":"","code":"data(test_data) head(test_data, 10) #> ID T1 T2 T3 T4 T5 T6 T7 T8 T9 T10 type #> 1 1 1 0 0 1 1 0 0 1 6 6 type_A #> 2 2 6 0 0 0 0 3 1 0 2 1 type_A #> 3 3 1 0 0 0 2 0 0 2 2 1 type_A #> 4 4 4 5 3 3 7 2 1 1 0 0 type_A #> 5 5 4 3 NA 2 5 5 0 0 0 0 type_A #> 6 6 4 1 0 1 3 1 3 5 11 14 type_A #> 7 7 1 0 0 0 1 3 3 1 1 1 type_A #> 8 8 4 2 1 1 1 1 0 0 0 0 type_A #> 9 9 1 1 1 19 22 1 2 1 1 2 type_A #> 10 10 1 1 3 5 8 2 2 2 5 2 type_A a <- pre_process(test_data) head(a, 10) #> ID T1 T2 T3 T4 T5 T6 #> 1 1 -0.25354628 -0.6761234 -0.67612340 -0.25354628 -0.25354628 -0.6761234 #> 2 2 2.41458180 -0.6678631 -0.66786305 -0.66786305 -0.66786305 0.8733594 #> 3 3 0.21764288 -0.8705715 -0.87057150 -0.87057150 1.30585725 -0.8705715 #> 4 4 0.61658123 1.0569964 0.17616607 0.17616607 1.93782672 -0.2642491 #> 5 5 0.96186009 0.5038315 -0.87025436 0.04580286 1.41988870 1.4198887 #> 6 6 -0.06459959 -0.7105955 -0.92592741 -0.71059546 -0.27993154 -0.7105955 #> 7 7 -0.09086738 -0.9995412 -0.99954118 -0.99954118 -0.09086738 1.7264802 #> 8 8 2.40535118 0.8017837 0.00000000 0.00000000 0.00000000 0.0000000 #> 9 9 -0.50260633 -0.5026063 -0.50260633 1.70395805 2.07171878 -0.5026063 #> 10 10 -0.94019379 -0.9401938 -0.04477113 0.85065153 2.19378551 -0.4924825 #> T7 T8 T9 T10 type #> 1 -0.6761234 -0.25354628 1.85933936 1.85933936 type_A #> 2 -0.1541222 -0.66786305 0.35961857 -0.15412224 type_A #> 3 -0.8705715 1.30585725 1.30585725 0.21764288 type_A #> 4 -0.7046643 -0.70466426 -1.14507943 -1.14507943 type_A #> 5 -0.8702544 -0.87025436 -0.87025436 -0.87025436 type_A #> 6 -0.2799315 0.15073237 1.44272411 2.08871998 type_A #> 7 1.7264802 -0.09086738 -0.09086738 -0.09086738 type_A #> 8 -0.8017837 -0.80178373 -0.80178373 -0.80178373 type_A #> 9 -0.3800194 -0.50260633 -0.50260633 -0.38001942 type_A #> 10 -0.4924825 -0.49248246 0.85065153 -0.49248246 type_A"},{"path":"https://kaiyanm.github.io/MolPad/reference/reshape_for_make_functions.html","id":null,"dir":"Reference","previous_headings":"","what":"Reshape for 'make' functions — reshape_for_make_functions","title":"Reshape for 'make' functions — reshape_for_make_functions","text":"internal function produces three primary datasets dashboard intended \"make\" functions.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/reshape_for_make_functions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reshape for 'make' functions — reshape_for_make_functions","text":"","code":"reshape_for_make_functions(data, cluster, annotation, id_colname, id_type)"},{"path":"https://kaiyanm.github.io/MolPad/reference/reshape_for_make_functions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reshape for 'make' functions — reshape_for_make_functions","text":"data output pre_process() cluster output gClusters() annotation output gPathway() id_colname columns contain database IDs. id_type corresponding database names columns.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/reshape_for_make_functions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reshape for 'make' functions — reshape_for_make_functions","text":"","code":"data(test_data) l <- reshape_for_make_functions(test_data_processed, test_cluster, test_annotations_processed, id_colname = c(\"GO_ID\",\"KEGG_ID\"),id_type = c(\"GO\",\"KEGG\")) head(l[[1]]) #> ID cluster #> 1 1 Group_2 #> 2 2 Group_5 #> 3 3 Group_2 #> 4 4 Group_4 #> 5 5 Group_4 #> 6 6 Group_2 #> GO_ID #> 1 #> 2 #> 3 GO:0003674,GO:0003824,GO:0005575,GO:0006629,GO:0006644,GO:0006650,GO:0006655,GO:0006793,GO:0006796,GO:0008150,GO:0008152,GO:0008610,GO:0008654,GO:0008808,GO:0009058,GO:0009987,GO:0016020,GO:0016740,GO:0016772,GO:0016780,GO:0019637,GO:0030572,GO:0032048,GO:0032049,GO:0044237,GO:0044238,GO:0044249,GO:0044255,GO:0045017,GO:0046471,GO:0046474,GO:0046486,GO:0071704,GO:0090407,GO:1901576 #> 4 GO:0003674,GO:0003676,GO:0003723,GO:0003729,GO:0003824,GO:0004654,GO:0005488,GO:0005575,GO:0005622,GO:0005623,GO:0005737,GO:0006139,GO:0006401,GO:0006402,GO:0006725,GO:0006807,GO:0008150,GO:0008152,GO:0009056,GO:0009057,GO:0009892,GO:0009987,GO:0010468,GO:0010605,GO:0010629,GO:0016070,GO:0016071,GO:0016740,GO:0016772,GO:0016779,GO:0019222,GO:0019439,GO:0034641,GO:0034655,GO:0043170,GO:0044237,GO:0044238,GO:0044248,GO:0044260,GO:0044265,GO:0044270,GO:0044424,GO:0044464,GO:0046483,GO:0046700,GO:0048519,GO:0050789,GO:0060255,GO:0065007,GO:0071704,GO:0090304,GO:0097159,GO:1901360,GO:1901361,GO:1901363,GO:1901575 #> 5 #> 6 #> KEGG_ID Pathway taxonomic.scope #> 1 K07124 Integumentary System hormonal proteins #> 2 Skeletal System structural proteins #> 3 K06131 Muscular System enzymes #> 4 K00962 Nervous System contractile proteins #> 5 K02083 Endocrine System contractile proteins #> 6 Cardiovascular System structural proteins head(l[[2]]) #> # A tibble: 6 × 6 #> ID type cluster day value taxonomic.scope #> #> 1 1 type_A Group_2 T1 -0.254 hormonal proteins #> 2 1 type_A Group_2 T2 -0.676 hormonal proteins #> 3 1 type_A Group_2 T3 -0.676 hormonal proteins #> 4 1 type_A Group_2 T4 -0.254 hormonal proteins #> 5 1 type_A Group_2 T5 -0.254 hormonal proteins #> 6 1 type_A Group_2 T6 -0.676 hormonal proteins head(l[[3]]) #> ID cluster #> 1 1 Group_2 #> 2 2 Group_5 #> 3 3 Group_2 #> 4 4 Group_4 #> 5 5 Group_4 #> 6 6 Group_2 #> GO_ID #> 1 #> 2 #> 3 GO:0003674<\/a>
GO:0003824<\/a>
GO:0005575<\/a>
GO:0006629<\/a>
GO:0006644<\/a>
GO:0006650<\/a>
GO:0006655<\/a>
GO:0006793<\/a>
GO:0006796<\/a>
GO:0008150<\/a>
GO:0008152<\/a>
GO:0008610<\/a>
GO:0008654<\/a>
GO:0008808<\/a>
GO:0009058<\/a>
GO:0009987<\/a>
GO:0016020<\/a>
GO:0016740<\/a>
GO:0016772<\/a>
GO:0016780<\/a>
GO:0019637<\/a>
GO:0030572<\/a>
GO:0032048<\/a>
GO:0032049<\/a>
GO:0044237<\/a>
GO:0044238<\/a>
GO:0044249<\/a>
GO:0044255<\/a>
GO:0045017<\/a>
GO:0046471<\/a>
GO:0046474<\/a>
GO:0046486<\/a>
GO:0071704<\/a>
GO:0090407<\/a>
GO:1901576<\/a> #> 4 GO:0003674<\/a>
GO:0003676<\/a>
GO:0003723<\/a>
GO:0003729<\/a>
GO:0003824<\/a>
GO:0004654<\/a>
GO:0005488<\/a>
GO:0005575<\/a>
GO:0005622<\/a>
GO:0005623<\/a>
GO:0005737<\/a>
GO:0006139<\/a>
GO:0006401<\/a>
GO:0006402<\/a>
GO:0006725<\/a>
GO:0006807<\/a>
GO:0008150<\/a>
GO:0008152<\/a>
GO:0009056<\/a>
GO:0009057<\/a>
GO:0009892<\/a>
GO:0009987<\/a>
GO:0010468<\/a>
GO:0010605<\/a>
GO:0010629<\/a>
GO:0016070<\/a>
GO:0016071<\/a>
GO:0016740<\/a>
GO:0016772<\/a>
GO:0016779<\/a>
GO:0019222<\/a>
GO:0019439<\/a>
GO:0034641<\/a>
GO:0034655<\/a>
GO:0043170<\/a>
GO:0044237<\/a>
GO:0044238<\/a>
GO:0044248<\/a>
GO:0044260<\/a>
GO:0044265<\/a>
GO:0044270<\/a>
GO:0044424<\/a>
GO:0044464<\/a>
GO:0046483<\/a>
GO:0046700<\/a>
GO:0048519<\/a>
GO:0050789<\/a>
GO:0060255<\/a>
GO:0065007<\/a>
GO:0071704<\/a>
GO:0090304<\/a>
GO:0097159<\/a>
GO:1901360<\/a>
GO:1901361<\/a>
GO:1901363<\/a>
GO:1901575<\/a> #> 5 #> 6 #> KEGG_ID #> 1 K07124<\/a> #> 2 #> 3 K06131<\/a> #> 4 K00962<\/a> #> 5 K02083<\/a> #> 6 #> Pathway taxonomic.scope #> 1 Integumentary System hormonal proteins #> 2 Skeletal System structural proteins #> 3 Muscular System enzymes #> 4 Nervous System contractile proteins #> 5 Endocrine System contractile proteins #> 6 Cardiovascular System structural proteins"},{"path":"https://kaiyanm.github.io/MolPad/reference/scale_by_row__.html","id":null,"dir":"Reference","previous_headings":"","what":"Scale by row — scale_by_row__","title":"Scale by row — scale_by_row__","text":"Scales values sample, row independently processed.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/scale_by_row__.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Scale by row — scale_by_row__","text":"","code":"scale_by_row__(data)"},{"path":"https://kaiyanm.github.io/MolPad/reference/scale_by_row__.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Scale by row — scale_by_row__","text":"input expected data frame first column ID following columns containing observations different time points. ID column remains unaltered, columns double (dbl) format scaled.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/scale_by_row__.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Scale by row — scale_by_row__","text":"","code":"data(test_data) scale_by_row__(test_data[1:5,1:10]) #> ID T1 T2 T3 T4 T5 T6 #> 1 1 -0.05847053 -0.5847053 -0.58470535 -0.05847053 -0.05847053 -0.5847053 #> 2 2 2.26366583 -0.6467617 -0.64676167 -0.64676167 -0.64676167 0.8084521 #> 3 3 0.22866478 -0.8003267 -0.80032673 -0.80032673 1.25765629 -0.8003267 #> 4 4 0.50395263 0.9575100 0.05039526 0.05039526 1.86462473 -0.4031621 #> 5 5 0.73869087 0.2841119 NA -0.17046712 1.19326987 1.1932699 #> T7 T8 T9 #> 1 -0.5847053 -0.05847053 2.5727035 #> 2 -0.1616904 -0.64676167 0.3233808 #> 3 -0.8003267 1.25765629 1.2576563 #> 4 -0.8567195 -0.85671947 -1.3102768 #> 5 -1.0796251 -1.07962512 -1.0796251"},{"path":"https://kaiyanm.github.io/MolPad/reference/test_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Test data — test_data","title":"Test data — test_data","text":"synthetically generated dataset created basic testing purposes.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/test_data.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Test data — test_data","text":"two datasets: data annotations","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/test_data.html","id":"data","dir":"Reference","previous_headings":"","what":"data","title":"Test data — test_data","text":"data frame 100 rows 12 variables: ID row ID T1~T10 count value 10 timepoints type type ~D","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/test_data.html","id":"annotations","dir":"Reference","previous_headings":"","what":"annotations","title":"Test data — test_data","text":"data frame 100 rows 5 variables: ID row ID GO_ID go ID KEGG_ID kegg ID system primary lable: 'Integumentary System', 'Skeletal System', 'Muscular System', 'Nervous System', 'Endocrine System', 'Cardiovascular System', 'Lymphatic System', 'Respiratory System', 'Digestive System', 'Urinary System' class secondary label: 'antibodies', 'contractile proteins', 'enzymes', 'hormonal proteins', 'structural proteins', 'storage proteins', 'transport proteins'","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/test_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Test data — test_data","text":"","code":"data(test_data)"},{"path":"https://kaiyanm.github.io/MolPad/reference/transpose_dataframe__.html","id":null,"dir":"Reference","previous_headings":"","what":"Transpose dataframe — transpose_dataframe__","title":"Transpose dataframe — transpose_dataframe__","text":"function transposes provided data frame, using values first column new column names.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/transpose_dataframe__.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transpose dataframe — transpose_dataframe__","text":"","code":"transpose_dataframe__(data)"},{"path":"https://kaiyanm.github.io/MolPad/reference/transpose_dataframe__.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transpose dataframe — transpose_dataframe__","text":"expected input data frame first column serves Time, subsequent columns contain observations various features.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/transpose_dataframe__.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transpose dataframe — transpose_dataframe__","text":"","code":"a <- data.frame(\"Day\"=c(\"Day1\",\"Day2\",\"Day3\"),\"feature_1\" =c(1,2,3),\"feature_2\" =c(0,4,1),\"feature_3\" =c(1,1,0)) a #> Day feature_1 feature_2 feature_3 #> 1 Day1 1 0 1 #> 2 Day2 2 4 1 #> 3 Day3 3 1 0 transpose_dataframe__(a) #> Day1 Day2 Day3 #> 1 1 2 3 #> 2 0 4 1 #> 3 1 1 0"}]
diff --git a/man/figures/.DS_Store b/man/figures/.DS_Store
index bbe3881..862bd8d 100644
Binary files a/man/figures/.DS_Store and b/man/figures/.DS_Store differ
diff --git a/man/figures/cheesecase.png b/man/figures/cheesecase.png
deleted file mode 100644
index d91f265..0000000
Binary files a/man/figures/cheesecase.png and /dev/null differ
diff --git a/man/figures/cheesecase1.png b/man/figures/cheesecase1.png
new file mode 100644
index 0000000..6d4d8aa
Binary files /dev/null and b/man/figures/cheesecase1.png differ
diff --git a/vignettes/cheese.Rmd b/vignettes/cheese.Rmd
index a26acd4..18967b8 100644
--- a/vignettes/cheese.Rmd
+++ b/vignettes/cheese.Rmd
@@ -98,3 +98,18 @@ gDashboard(cheesedata,
id_colname = c("GO_ID","KEGG_ID"),
id_type = c("GO","KEGG"))
```
+
+# Results
+
+For example, when we filter for Actinomycetota (Actinobacteria) as the functional group, we see that there are no edges connecting to group 10 and group 3- the clusters that have the most features associated with Actinomycetoa for Cheese sample A (Fig 1.3.A).
+
+
+
+ { width=100% }
+ *Fig 1: Dashboard showing Actinomycetota filtered network (A) with enrichment pattern for Cheese Sample-A (B) and Cheese Sample-C (C); Cluster pattern for Group 9, which also is enriched for Type IV secretion genes (D).*
+
+
+
+Looking at the pattern traces of these groups, (Fig 1.3.B), there is a peak in samples A4 (week 9) and A5 (week 13), which mirrors the 16S rRNA results of Saak et al. Since these two clusters do not have edges connecting them to other groups, this suggests that the Actinomycetoa features found in these groups follow distinct longitudinal succession patterns that are independent. When looking at Actinomycetoa within Cheese Sample C we see a different pattern. Groups 2 and 5, have the most features associated with Actinomycetoa, but they are highly connected to the other groups (Fig 1.3.A). From these results, we can hypothesize that though Actinomycetoa features are more abundant in later time points for both cheese samples, their dynamics are differentially influenced.
+
+The authors found that Type VI secretion was enriched in Pseudomonadota bacteria (specifically, Psychrobacter), and hypothesized this enrichment was due to the importance of physical species interactions that occur with this habitat. Using MolPad, we searched for other secretion systems associated genes, to understand their dynamics within the community. Focusing on KEGG annotated Type IV secretion genes, we found that Group 9 contained 12/13 of these genes. Within this group, features that cluster are ones that peak in Cheese sample C5 (week 13, Fig 1.3.D). This is also the most taxonomically diverse sample. From this, we can hypothesize that increased taxonomic diversity is also associated with increases in genes that are related to competitive species interactions.
\ No newline at end of file
diff --git a/vignettes/getstarted.Rmd b/vignettes/getstarted.Rmd
index b9ec236..5724477 100644
--- a/vignettes/getstarted.Rmd
+++ b/vignettes/getstarted.Rmd
@@ -45,6 +45,8 @@ library(MolPad)
load("/Users/hazelma/Documents/GitHub/MolPad_Data/multiomics_simulate.RData")
```
+
+
# Before Input
We recommend carefully reviewing your data and considering applying normalization (standardization, quantile normalization, log transformation, ...) and imputation (KNN, PMM, Mean/Median/Mode, ...) to address issues related to library size and missing data beforehead, otherwise the `pre_processing()` function will default to converting NAs to 0s.
@@ -57,6 +59,7 @@ We recommend carefully reviewing your data and considering applying normalizatio
* If it's hard to decide, you can always experiment with different methods and use cross-validation to compare their effectiveness. Let your choice be guided by domain knowledge and the specific requirements of your analysis to increase the quality of the following steps.
+
# Data Format
@@ -72,14 +75,17 @@ If your datasets are not multi-omics, you can simply apply it to our dashboard b
### Data Format and Pre-process
+
{ width=70% }
-
+
In the figure, A, B, and C can represent datasets from different sources or different aspects of measurements. Below are two examples of what the input data might look like and how to transform it into the required format through `pre_process()` modules.
+
+
#### If your input is a list of datasets:
Here is a list of datasets, each following the same format: they all have an ID column and identical column names.
@@ -105,6 +111,8 @@ data <- pre_process(fungal_dlist, typenameList = c('DataSource_A','DataSource_B'
head(data,10)
```
+
+
#### If your input is a single dataset:
Here is a dataset that already includes a labeled 'type' column:
@@ -121,6 +129,7 @@ x1 <- pre_process(x, replaceNA = TRUE,scale = TRUE)
head(x1,10)
```
+
# Choose Your Annotation
@@ -137,15 +146,18 @@ If your data has more than two layers of labels (like the cheese dataset), you c
Annotation_path_taxon <- gAnnotation(annotations,"phylum","class")
```
+
+
# Network Generation
To generate the network, we first scale and cluster the trajectories across the input molecular features. Trajectories are clustered using K-means, and a built-in elbow method is used to select the optimal K. We build network between clustered trajectories by adapting the GENIE3 algorithm. Specifically, the network is learned through a series of regressions, where each cluster centroid is predicted from the expression patterns of all the other cluster centroids using random forests. We choose random forests because of their potential to model interacting features and non-linearity without strong assumptions.
+
{ width=50% }
-
+
To determine which feature clusters are predictive of a given trajectory, the Mean Decrease Accuracy of a subset of top predictors whose expression directly influences the expression of the target cluster is taken as an indication of a putative link.
@@ -159,6 +171,8 @@ network_result <- cluster_result |>
gNetwork_view(network_result)
```
+
+
# Dashboard Navigation
@@ -173,46 +187,68 @@ gDashboard(data,
id_type = "KEGG")
```
+
{ width=70% }
+
Once you've launched the Shiny dashboard, you can zoom in or make adjustments to explore interesting findings within your data. To effectively navigate the dashboard generated by MolPad, you'll follow three main steps:
-### Choose a Primary Functional Annotation and Adjust Edge Density
+### 1. Choose a Primary Functional Annotation and Adjust Edge Density
Start by selecting a primary functional annotation from the available options. Then, fine-tune the edge density by adjusting the threshold value on the importance score. Nodes that turn bright green indicate clusters containing the most features related to the chosen functional annotation.
-### Explore the Network
+### 2. Explore the Network
Brushing on the network unveils patterns of taxonomic composition and typical trajectories. You can also zoom into specific taxonomic annotations by applying filters.
-### Investigate Feature Details and Related Function Annotations
+### 3. Investigate Feature Details and Related Function Annotations
Delve into the feature table to examine the specifics of the features within the selected clusters. Explore additional related function annotations using the drop-down options. Click on the provided links to access online information about the items of interest. The interface encourages iterative exploration, enabling you to conduct multiple steps to answer specific questions, such as comparing pattern distributions between two functions or identifying functionally important community members metabolizing a feature of interest.
+
+
+
{ width=100% }
+
# Related Visualization Outputs
The following visualizations are integrated into the dashboard, so there's no need to run function for each plot individually. We've linked a stacked bar plot and a ribbon plot with the network to facilitate focus and context explorations. This setup helps in detecting similar longitudinal patterns and understanding their relationships.
+
+
+
### Network Plot
+
+The network plot is a powerful visualization tool that displays the relationships between different groups or features within your data. In MolPad, the network plot helps to identify clusters of features that share similar patterns, revealing underlying connections that might not be immediately obvious. By visualizing these connections, users can gain a clearer understanding of the structure within their data, making it easier to pinpoint significant associations and trends.
+
```{r,echo=FALSE}
data("test_data")
make_the_graph(test_graphptw, test_network, 0.03, "Muscular System","nicely")
```
+
+
### Stack Bar Plot
+
+The stacked bar plot provides a detailed view of the composition of each cluster or group in your dataset. By stacking different categories on top of each other within a single bar, this plot allows for a quick comparison of relative proportions across multiple groups. This is particularly useful in microbiome experiments where understanding the distribution of taxa across different conditions or time points is crucial. The stacked bar plot makes it easy to see how these distributions change between experimental conditions, facilitating deeper insights.
+
```{r,echo=FALSE}
make_stackbar_plot(test_maindata, c("Group_1","Group_2","Group_3","Group_4"), c("hormonal proteins","structural proteins","enzymes","storage proteins","antibodies","transport proteins"))
```
+
+
+
### Ribbon Plot
+The ribbon plot is designed to visualize changes over time, making it an ideal tool for tracking longitudinal data. In MolPad, the ribbon plot illustrates how the abundance or expression levels of features vary across different time points or conditions. The smooth, flowing design of the ribbons helps to emphasize trends and patterns, enabling users to quickly identify periods of significant change or stability. This plot is particularly advantageous when comparing multiple groups, as it clearly shows overlapping trends and divergences, providing a comprehensive view of temporal dynamics in the data.
+
```{r,echo=FALSE}
make_line_plot(test_maindata, c("Group_1","Group_2","Group_3","Group_4"), c("hormonal proteins","structural proteins","enzymes","storage proteins","antibodies","transport proteins"))
diff --git a/vignettes/whymolpad.Rmd b/vignettes/whymolpad.Rmd
index c308dba..38cb484 100644
--- a/vignettes/whymolpad.Rmd
+++ b/vignettes/whymolpad.Rmd
@@ -24,11 +24,17 @@ library(dplyr)
# Why MolPad?
With the increasing multi-omics data and longitudinal designs integrated into microbiome experiments, there is a growing need to present the network, especially with complex variations across biological modalities. Network perspective helps detect the underlying co-occurrence among microbiome samples, allowing for high-level insights into the global structure. Yet when it comes to experimental data that records time series for 100,000 features, the network will collapse into some entangled clumps and therefore unable to read.
+
+
{ width=50% }
+
+
+# Improvements
+
For the aim of network interpretation, MolPad shows improvements in 3 important aspects:
* Capture groups of features that share longitudinal patterns and functional properties within the network.
@@ -39,6 +45,7 @@ This package especially facilitates the exploration of longitudinal molecular co
Unlike existing tools, MolPad stands out for its rich network interactivity and support for contextualization. It is also equipped with a beginner-friendly interface and self-contained data processing functions, making it accessible to users with limited programming experience.
+
# Key Features
@@ -54,25 +61,31 @@ We build network between clustered trajectories by adapting the GENIE3 algorithm
This package provides an interactive dashboard designed to help users explore data and uncover relationships within complex networks. By utilizing focus-plus-context visualization, the dashboard allows users to zoom in on specific details while keeping the broader dataset in view. This approach enables a deeper understanding of the data, making it easier to identify patterns and make informed decisions.
+
+
# What does it look like?
+Now, let’s see what you can get from the dashboard. We’ll start with an overview and then demonstrate how to discover patterns within your data.
+
+
### MolPad Dashboard Overview
+
{ width=100% }
-
+
Above is the overview of the MolPad Dashboard. To explore the dashboard effectively, you can start by following the sequence A-B-C-D. This approach will guide you through the cluster-level network, taxonomic-level bar plot, type-level line plot, and feature-level table. From there, you can modify detailed settings to identify and investigate special patterns within your data.
-
### Discovering related patterns
{ width=100% }
+
Here is a short example of discovering related patterns using the network plot: The shade of the edges represents the proximity of nodes. In the brushed area, Groups 1-7-8 (circled by solid black lines) and Groups 1-2 (circled by blue dashed lines) are strongly linked. For Groups 1, 7, and 8, the patterns form a W-shape with a noticeable peak at the same time section. Although Group 1 shows higher volatility compared to Group 2, both groups display a highly overlapping increasing trend.
Key Features
What does it look like?
+Now, let’s see what you can get from the dashboard. We’ll start with
+an overview and then demonstrate how to discover patterns within your
+data.
MolPad Dashboard Overview
+
Above is the overview of the MolPad Dashboard. To explore the
dashboard effectively, you can start by following the sequence A-B-C-D.
This approach will guide you through the cluster-level network,
@@ -170,6 +181,7 @@
Discovering related patterns
+
Here is a short example of discovering related patterns using the
network plot: The shade of the edges represents the proximity of nodes.
In the brushed area, Groups 1-7-8 (circled by solid black lines) and
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
index 24820a2..106fea2 100644
--- a/docs/pkgdown.yml
+++ b/docs/pkgdown.yml
@@ -6,7 +6,7 @@ articles:
cheese: cheese.html
getstarted: getstarted.html
whymolpad: whymolpad.html
-last_built: 2024-08-15T06:34Z
+last_built: 2024-08-15T07:35Z
urls:
reference: https://kaiyanm.github.io/MolPad/reference
article: https://kaiyanm.github.io/MolPad/articles
diff --git a/docs/reference/figures/cheesecase.png b/docs/reference/figures/cheesecase.png
deleted file mode 100644
index d91f265..0000000
Binary files a/docs/reference/figures/cheesecase.png and /dev/null differ
diff --git a/docs/reference/figures/cheesecase1.png b/docs/reference/figures/cheesecase1.png
new file mode 100644
index 0000000..6d4d8aa
Binary files /dev/null and b/docs/reference/figures/cheesecase1.png differ
diff --git a/docs/reference/gClusters-1.png b/docs/reference/gClusters-1.png
index 993d2a4..463b550 100644
Binary files a/docs/reference/gClusters-1.png and b/docs/reference/gClusters-1.png differ
diff --git a/docs/reference/gClusters.html b/docs/reference/gClusters.html
index 28642a8..566cc47 100644
--- a/docs/reference/gClusters.html
+++ b/docs/reference/gClusters.html
@@ -102,70 +102,70 @@
Examplesreslist <- gClusters(test_data_processed)
# k-means result
reslist[[1]]
-#> K-means clustering with 20 clusters of sizes 7, 6, 7, 10, 8, 2, 3, 5, 4, 6, 10, 3, 3, 4, 4, 6, 4, 3, 2, 3
+#> K-means clustering with 20 clusters of sizes 6, 3, 6, 5, 5, 8, 6, 5, 5, 6, 4, 11, 3, 3, 3, 2, 4, 2, 2, 11
#>
#> Cluster means:
-#> T1 T2 T3 T4 T5 T6
-#> 1 2.22789830 0.02950883 -0.75836275 -0.2619747 -0.5953255 -0.70974025
-#> 2 2.77349939 -0.21724464 -0.35972402 -0.4172516 -0.5000122 -0.11678355
-#> 3 0.93534606 -0.39771771 0.31238452 1.1531782 1.1995301 -1.05203742
-#> 4 -0.27715339 -0.39760069 -0.06466866 1.3995407 2.1316423 -0.28068841
-#> 5 0.11559709 -0.19000668 -0.25629794 -0.1487697 -0.4383348 2.58301481
-#> 6 -0.27847690 -0.82258408 -0.82258408 -0.8225841 1.2338761 -0.82258408
-#> 7 0.26998015 -0.08094677 -0.28926312 -0.7813127 -0.6065051 -0.14945230
-#> 8 -0.81355388 -0.84696982 -0.32573017 1.1218534 2.0955812 -0.56207309
-#> 9 1.45173820 0.26236463 1.31600581 -0.2531012 0.2004547 -1.12389420
-#> 10 0.34700978 0.13673201 0.82464908 0.2571569 1.2656031 0.63541302
-#> 11 -0.19020083 -0.69654961 -0.66836949 -0.4657663 -0.3851487 -0.55904213
-#> 12 1.62582942 1.51619556 -0.35316244 0.2978109 -0.1777508 0.02299732
-#> 13 -0.09187847 -0.87257293 -0.87257293 -0.8725729 -0.5696817 2.05668333
-#> 14 0.85679806 -0.37993358 -0.46099690 0.3934155 0.5857582 1.85027957
-#> 15 1.12205113 0.09185659 -0.89815993 -0.7233153 -0.5717924 -0.86037548
-#> 16 2.09193480 -0.33320285 -0.50786563 -0.5579659 -0.5579659 1.54559773
-#> 17 1.56599593 -0.49119311 -0.83000857 -0.6160199 -0.2092753 1.19151568
-#> 18 0.89463785 0.01698143 -0.57023879 -1.1815834 -1.1815834 1.18824796
-#> 19 -0.13543375 -0.90710305 -0.66248003 -0.4178570 -0.6624800 1.37010464
-#> 20 1.31631350 -0.76059916 -0.76059916 1.5193444 0.6006058 0.84385791
-#> T7 T8 T9 T10
-#> 1 -0.567081109 0.132060988 0.03929621 0.46372005
-#> 2 -0.357532815 -0.298895212 -0.29119673 -0.21485863
-#> 3 -1.222209771 -0.334925678 -0.36853535 -0.22501291
-#> 4 -0.548494719 -0.646992821 -0.66392148 -0.65166279
-#> 5 -0.006327004 -0.327677972 -0.66559889 -0.66559889
-#> 6 -0.822584085 1.233876127 1.23387613 0.68976894
-#> 7 -0.763902351 -0.412975440 0.39220455 2.42217307
-#> 8 -0.651292750 -0.498839228 0.42446013 0.05656427
-#> 9 -0.987867401 -0.340013670 -0.33085684 -0.19483004
-#> 10 -1.004339966 -0.620506186 -0.71949086 -1.12222690
-#> 11 -0.566522777 0.002653353 1.73917982 1.78976662
-#> 12 -0.770498738 -0.620423681 -0.77049874 -0.77049874
-#> 13 0.882337606 0.086536293 0.12686085 0.12686085
-#> 14 -0.526130916 -0.896529515 -0.89652951 -0.52613092
-#> 15 -0.822591022 0.091471523 1.00908283 1.56177203
-#> 16 -0.425513174 -0.557965933 -0.30571480 -0.39133827
-#> 17 0.774154665 0.196302112 -0.62663627 -0.95483532
-#> 18 0.894637851 -0.836550610 0.41343688 0.36201421
-#> 19 -0.907103054 -0.417856999 1.37010464 1.37010464
-#> 20 -0.666108041 -0.666108041 -0.66610804 -0.76059916
+#> T1 T2 T3 T4 T5 T6
+#> 1 2.0919348 -0.33320285 -0.50786563 -0.5579659 -0.5579659 1.5455977
+#> 2 0.8277105 1.32036794 -0.09261332 1.2325986 0.4199176 -0.9576634
+#> 3 0.5077879 -0.24564283 0.53503708 0.2363183 1.0793829 1.3583848
+#> 4 0.2914366 -0.41308350 -0.76541563 -0.9724353 -0.9724353 1.6040887
+#> 5 1.2081037 -0.66790409 -0.66790409 -0.9275526 -0.4204221 1.2719890
+#> 6 0.1155971 -0.19000668 -0.25629794 -0.1487697 -0.4383348 2.5830148
+#> 7 1.2574004 -0.64914997 -0.87502695 0.9353966 0.4760273 1.2681050
+#> 8 0.9389212 -0.86278238 0.43887930 1.0009533 1.3733646 -1.0423264
+#> 9 -0.8135539 -0.84696982 -0.32573017 1.1218534 2.0955812 -0.5620731
+#> 10 -0.3802219 -0.36840199 0.02254271 1.6459653 1.9633618 -0.3246455
+#> 11 -0.1225506 -0.44139873 -0.19548572 1.0299038 2.3840629 -0.2147528
+#> 12 2.5784883 -0.02446156 -0.60355041 -0.2547486 -0.5120230 -0.3947043
+#> 13 0.9922755 0.32399254 -0.94565021 -0.7125240 -0.5104935 -0.9456502
+#> 14 0.2699801 -0.08094677 -0.28926312 -0.7813127 -0.6065051 -0.1494523
+#> 15 1.7947637 -0.47746004 -0.52783931 -0.7635958 -0.7635958 -0.6438957
+#> 16 0.1789247 1.26157166 0.82115649 0.3899368 1.2707671 -0.2614905
+#> 17 1.4517382 0.26236463 1.31600581 -0.2531012 0.2004547 -1.1238942
+#> 18 -0.2784769 -0.82258408 -0.82258408 -0.8225841 1.2338761 -0.8225841
+#> 19 2.1235865 1.05868538 -0.39467611 0.1315587 -0.1315587 0.3946761
+#> 20 -0.2262821 -0.73107613 -0.66098092 -0.4323193 -0.4035074 -0.3836848
+#> T7 T8 T9 T10
+#> 1 -0.425513174 -0.557965933 -0.30571480 -0.391338269
+#> 2 -1.160107459 -0.295057401 -0.64757654 -0.647576540
+#> 3 -1.006282312 -0.766188425 -0.64803067 -1.050766722
+#> 4 0.301678216 -0.371684852 0.66435231 0.633498705
+#> 5 1.209152596 -0.095960244 -0.32347149 -0.586030729
+#> 6 -0.006327004 -0.327677972 -0.66559889 -0.665598891
+#> 7 -0.354972016 -0.601904415 -0.82778139 -0.628094550
+#> 8 -1.159101258 -0.345888528 -0.27147562 -0.070544200
+#> 9 -0.651292750 -0.498839228 0.42446013 0.056564270
+#> 10 -0.647007074 -0.667438225 -0.63229313 -0.611861976
+#> 11 -0.400726188 -0.616324716 -0.71136401 -0.711364008
+#> 12 -0.526022073 -0.185303954 -0.06989626 -0.007778173
+#> 13 -0.945650212 0.121962031 1.19430596 1.427432164
+#> 14 -0.763902351 -0.412975440 0.39220455 2.422173070
+#> 15 -0.260645101 0.389799714 -0.08327821 1.335746686
+#> 16 -0.912917717 -0.481698036 -1.13312530 -1.133125299
+#> 17 -0.987867401 -0.340013670 -0.33085684 -0.194830041
+#> 18 -0.822584085 1.233876127 1.23387613 0.689768939
+#> 19 -0.795567972 -0.795567972 -0.79556797 -0.795567972
+#> 20 -0.612869917 -0.006483243 1.70560793 1.751595921
#>
#> Clustering vector:
#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
-#> 11 16 6 10 10 11 13 12 4 8 3 7 15 2 3 8 5 11 9 1
+#> 20 1 18 16 3 20 5 19 10 9 8 14 15 12 2 9 6 20 17 12
#> 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40
-#> 17 11 5 16 2 8 20 1 5 4 17 4 10 6 4 10 3 15 11 12
+#> 7 20 6 1 12 9 7 12 6 11 5 11 16 18 10 3 8 13 20 2
#> 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
-#> 5 16 11 11 14 11 5 14 16 5 1 17 18 15 5 17 13 4 1 4
+#> 6 1 20 20 7 20 6 3 1 6 15 5 4 13 6 5 4 10 12 10
#> 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80
-#> 3 14 20 15 10 16 2 14 9 9 7 8 5 12 7 4 3 4 3 20
+#> 8 3 7 13 3 1 12 7 17 17 14 9 6 19 14 10 2 11 8 7
#> 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100
-#> 3 19 1 13 19 16 9 2 18 2 4 18 4 1 11 10 8 2 11 1
+#> 8 20 15 4 4 1 17 12 4 12 10 5 11 12 20 3 9 12 20 12
#>
#> Within cluster sum of squares by cluster:
-#> [1] 13.8467580 2.0713631 15.4102772 5.9461649 8.0412334 0.9921913
-#> [7] 3.1842269 3.4617088 9.9418120 17.6305488 8.0515257 4.9390800
-#> [13] 4.0041862 7.3057948 4.7723673 3.0260293 7.3290085 5.6033175
-#> [19] 0.9549143 2.4583646
-#> (between_SS / total_SS = 83.4 %)
+#> [1] 3.0260293 5.0357352 13.2343206 13.1118010 10.3695240 8.0412334
+#> [7] 11.7251902 8.3255313 3.4617088 2.4693324 1.6353685 13.0645655
+#> [13] 2.9416715 3.1842269 5.0413438 2.4669004 9.9418120 0.9921913
+#> [19] 0.9833964 12.4042363
+#> (between_SS / total_SS = 83.1 %)
#>
#> Available components:
#>
diff --git a/docs/reference/gNetwork-1.png b/docs/reference/gNetwork-1.png
index efb4385..14d64f3 100644
Binary files a/docs/reference/gNetwork-1.png and b/docs/reference/gNetwork-1.png differ
diff --git a/docs/reference/gNetwork.html b/docs/reference/gNetwork.html
index dc8f058..f0279e4 100644
--- a/docs/reference/gNetwork.html
+++ b/docs/reference/gNetwork.html
@@ -83,12 +83,12 @@ Examplesnetworkres <- gNetwork(test_cluster, ntop = 3)
head(networkres)
#> weight IncNodePurity var_names from
-#> 1 1.6429045 0.7371260 Group_3 Group_1
-#> 2 0.4588961 0.9310697 Group_5 Group_1
-#> 3 -0.7833169 0.6761286 Group_2 Group_1
-#> 4 5.0076415 0.8821582 Group_5 Group_2
-#> 5 4.1618215 1.1976058 Group_4 Group_2
-#> 6 2.5714583 1.2820391 Group_1 Group_2
+#> 1 1.4215243 1.1776012 Group_5 Group_1
+#> 2 0.7911266 0.7011049 Group_3 Group_1
+#> 3 -1.9891284 0.5811186 Group_2 Group_1
+#> 4 5.8098014 1.1107592 Group_4 Group_2
+#> 5 5.4000389 0.8744358 Group_5 Group_2
+#> 6 1.2492288 1.1816976 Group_1 Group_2
gNetwork_view(networkres)
diff --git a/docs/reference/make_the_graph-1.png b/docs/reference/make_the_graph-1.png
index 061c537..76d9b9c 100644
Binary files a/docs/reference/make_the_graph-1.png and b/docs/reference/make_the_graph-1.png differ
diff --git a/docs/search.json b/docs/search.json
index 782a610..33c4d18 100644
--- a/docs/search.json
+++ b/docs/search.json
@@ -1 +1 @@
-[{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"CC0 1.0 Universal","title":"CC0 1.0 Universal","text":"CREATIVE COMMONS CORPORATION LAW FIRM PROVIDE LEGAL SERVICES. DISTRIBUTION DOCUMENT CREATE ATTORNEY-CLIENT RELATIONSHIP. CREATIVE COMMONS PROVIDES INFORMATION “-” BASIS. CREATIVE COMMONS MAKES WARRANTIES REGARDING USE DOCUMENT INFORMATION WORKS PROVIDED HEREUNDER, DISCLAIMS LIABILITY DAMAGES RESULTING USE DOCUMENT INFORMATION WORKS PROVIDED HEREUNDER.","code":""},{"path":"https://kaiyanm.github.io/MolPad/LICENSE.html","id":"statement-of-purpose","dir":"","previous_headings":"","what":"Statement of Purpose","title":"CC0 1.0 Universal","text":"laws jurisdictions throughout world automatically confer exclusive Copyright Related Rights (defined ) upon creator subsequent owner(s) (, “owner”) original work authorship /database (, “Work”). 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Affirmer understands acknowledges Creative Commons party document duty obligation respect CC0 use Work.","code":""},{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/articles/FAQ.html","id":"how-can-i-tell-if-my-dataset-is-suitable-for-using-this-dashboard","dir":"Articles","previous_headings":"Common Questions:","what":"How can I tell if my dataset is suitable for using this dashboard?","title":"FAQs","text":"lot longitudinal data least two descriptive columns (like types), match minimal needs starting dashboard. addition, data specific web-ID columns, MolPad make convenient createing links databases automatically.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/FAQ.html","id":"how-can-i-customize-my-dashboard","dir":"Articles","previous_headings":"Common Questions:","what":"How can I customize my dashboard?","title":"FAQs","text":"can edit title within gDashboard() function. additional personalization, adjusting colors sizes figures, can fork repository, create copy, make modifications dashboard.css file.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/FAQ.html","id":"what-should-i-do-if-i-only-have-a-few-time-points5","dir":"Articles","previous_headings":"Common Questions:","what":"What should I do if I only have a few time points(<5)?","title":"FAQs","text":"Firstly, strongly recommend extending length longitudinal samples. can achieve increasing number measurements two days incorporating simulated data. options don’t suit needs, can also consider using “linear” method gNetwork() function.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Case Study: Cheese Communities","text":"vignette provides comprehensive guide using MolPad case study, including data pre-processing, network generation, result analysis.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"washed-rind-cheese-microbial-communities","dir":"Articles","previous_headings":"","what":"Washed-Rind Cheese Microbial Communities","title":"Case Study: Cheese Communities","text":"Cheese making ancient craft involves coagulation milk proteins form curds, separated liquid whey. curds processed, shaped, aged develop desired texture flavor. Various factors, type milk, specific cultures bacteria, aging conditions, contribute unique characteristics cheese. One crucial step process aging practice, regular washing brine solution plays significant role. process producing cheese, regular washing brine solution aging practice can homogenize microbial communities cheese’ surface facilitate intermicrobial interactions. following parts, analyze longitudinal data set three washed-rind cheese communities collected cheese ripening.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"data","dir":"Articles","previous_headings":"","what":"Data","title":"Case Study: Cheese Communities","text":"analysis, use data set contained study Smith et al. (2022) microbial communities cheese. original research investigated successional dynamics occur within cheese rind microbial communities using combination 16S rRNA amplicon, Illumina, PacBio sequencing. functionally taxonomically annotate (using eggNOG (21) MMseqs2 (22)) contigs generated Illumina reads, demonstrate utility MolPad using single-omic. Specifically, focus Cheese Sample Cheese Sample C. detailed information attached data, please check documentation.","code":"data(\"cheese\") str(cheese) #> tibble [106,239 × 18] (S3: tbl_df/tbl/data.frame) #> $ ID : chr [1:106239] \"1\" \"2\" \"3\" \"4\" ... #> $ A_1 : int [1:106239] 38 23 24 3 58 12 1 14 1 3 ... #> $ A_2 : int [1:106239] 23 6 5 2 14 9 1 7 1 1 ... #> $ A_3 : int [1:106239] 27 4 37 4 45 14 0 14 3 5 ... #> $ A_4 : int [1:106239] 5 0 10 2 13 4 1 4 4 0 ... #> $ A_5 : int [1:106239] 11 9 19 16 32 13 0 4 1 1 ... #> $ C_1 : int [1:106239] 13 21 3 56 82 2 4 17 7 2 ... #> $ C_3 : int [1:106239] 1 1 0 1 3 0 0 0 0 1 ... #> $ C_4 : int [1:106239] 3 0 1 7 8 1 0 1 2 2 ... #> $ C_5 : int [1:106239] 0 2 4 17 3 4 0 1 8 6 ... #> $ GO_ID : chr [1:106239] NA NA NA NA ... #> $ KEGG_ID: chr [1:106239] NA NA NA NA ... #> $ domain : chr [1:106239] NA NA NA NA ... #> $ phylum : chr [1:106239] NA NA NA NA ... #> $ class : chr [1:106239] NA NA NA NA ... #> $ order : chr [1:106239] NA NA NA NA ... #> $ family : chr [1:106239] NA NA NA NA ... #> $ genus : chr [1:106239] NA NA NA NA ... str(annotations) #> tibble [86,156 × 9] (S3: tbl_df/tbl/data.frame) #> $ ID : chr [1:86156] \"9\" \"10\" \"11\" \"12\" ... #> $ GO_ID : chr [1:86156] \"-\" \"-\" \"-\" \"-\" ... #> $ KEGG_ID: chr [1:86156] \"-\" \"-\" \"-\" \"-\" ... #> $ domain : chr [1:86156] \"Bacteria\" \"Bacteria\" \"Bacteria\" \"Bacteria\" ... #> $ phylum : chr [1:86156] \"Pseudomonadota\" \"Pseudomonadota\" \"Pseudomonadota\" \"Pseudomonadota\" ... #> $ class : chr [1:86156] \"Alphaproteobacteria\" \"Alphaproteobacteria\" \"Alphaproteobacteria\" \"Alphaproteobacteria\" ... #> $ order : chr [1:86156] \"Caulobacterales\" \"Caulobacterales\" \"Hyphomicrobiales\" \"Hyphomicrobiales\" ... #> $ family : chr [1:86156] \"Caulobacteraceae\" \"Caulobacteraceae\" \"Bartonellaceae\" \"Brucellaceae;-_Brucella/Ochrobactrum group\" ... #> $ genus : chr [1:86156] \"Caulobacter\" \"Caulobacter;-_unclassified Caulobacter\" \"Bartonella\" \"Brucella\" ..."},{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"data-and-annotations","dir":"Articles","previous_headings":"Data > Pre-process","what":"Data and Annotations","title":"Case Study: Cheese Communities","text":"select ‘type’ column phylum describe characteristic cheese data. Also, columns phylum class taken tags elemental composition. section, introduce data preparing steps analysis. annotations dataset contains various columns describe characteristics properties samples. First, select ‘type’ column phylum provide broad categorization microbial communities present cheese surface. categorization helps understanding overall composition diversity microbes high taxonomic level. run pre_process() function clean standardize data. annotate dataset, also use columns phylum class tags elemental composition microbial communities. phylum column represents major taxonomic rank, giving us broad overview microbial distribution. class column provides detailed information, allowing us delve deeper specific types microbes present. pre-processing, two datasets put dashboard look like:","code":"cheesedata <- cheese |> select(ID, A_1:C_5, phylum) |> rename(type=phylum) |> pre_process() pathchee <- gAnnotation(annotations,\"phylum\",\"class\") # data cheesedata[112:115,] #> # A tibble: 4 × 11 #> ID A_1 A_2 A_3 A_4 A_5 C_1 C_3 C_4 C_5 type #> #> 1 112 0.943 -0.471 -0.471 -0.471 -0.471 2.36 -0.471 -0.471 -0.471 Other #> 2 113 0.786 -0.124 0.126 -0.623 -0.637 2.33 -0.667 -0.593 -0.593 Other #> 3 114 1.22 -1.09 -0.430 -0.829 -0.170 1.43 -1.30 0.455 0.715 Other #> 4 115 2.67 -0.333 -0.333 -0.333 -0.333 -0.333 -0.333 -0.333 -0.333 Other # annotation pathchee[112:115,] #> # A tibble: 4 × 9 #> ID GO_ID KEGG_ID domain Pathway taxonomic.scope order family genus #> #> 1 145 - ko:K00004 Bacte… Pseudo… Unknown NA NA NA #> 2 147 - ko:K00004 Bacte… Actino… Actinomycetes NA NA NA #> 3 148 - ko:K00004 Bacte… Bacill… Bacilli Baci… Bacil… Lysi… #> 4 149 - ko:K00004,ko:K0… Bacte… Actino… Actinomycetes NA NA NA"},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"cluster-input","dir":"Articles","previous_headings":"Data","what":"Cluster Input","title":"Case Study: Cheese Communities","text":"section, generate clusters first dataset using gClusters function. function takes cheese dataset (cheesedata) input generates clusters based specified parameters. , set number clusters 10 (ncluster = 10) specify maximum number clusters consider determining optimal number clusters (elbow.max=15).","code":"cluschee <- gClusters(cheesedata,ncluster = 10,elbow.max=15)"},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"network-input","dir":"Articles","previous_headings":"Data","what":"network input","title":"Case Study: Cheese Communities","text":"generating clusters major patterns, proceed obtain network results clusters. Taking cluster centroids nodes, prediction process edges divided individual regression tasks, cluster centroid independentally predicted expression cluster centroids, using random forests. pick top 3 related predictors cluster centroid save network output future use. achieved using gNetwork() function. gain insight network results, can visualize details using gNetwork_view() function, shown .","code":"networkchee <- gNetwork(cluschee,ntop = 3) gNetwork_view(networkchee)"},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"run-dashboard","dir":"Articles","previous_headings":"","what":"Run Dashboard","title":"Case Study: Cheese Communities","text":"clusters network results obtained, can proceed run dashboard. involves declaring annotations executing dashboard using gDashboard() function. , pass cheese dataset (cheesedata), cluster results (cluschee), network results (networkchee), specify column names types annotation identifiers.","code":"gDashboard(cheesedata, cluschee, pathchee, networkchee, id_colname = c(\"GO_ID\",\"KEGG_ID\"), id_type = c(\"GO\",\"KEGG\"))"},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"installation","dir":"Articles","previous_headings":"","what":"Installation","title":"Get started","text":"can either install MolPad devtools, clone repository local computer (example, onto ./Github) installing: , load package:","code":"# Install the package in R: install.packages(\"devtools\") library(devtools) install_github(\"KaiyanM/MolPad\") # Download through Github and install the package in R: setwd(\"./GitHub\") install(\"MolPad\") library(MolPad)"},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"before-input","dir":"Articles","previous_headings":"","what":"Before Input","title":"Get started","text":"recommend carefully reviewing data considering applying normalization (standardization, quantile normalization, log transformation, …) imputation (KNN, PMM, Mean/Median/Mode, …) address issues related library size missing data beforehead, otherwise pre_processing() function default converting NAs 0s.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"tips-for-normalization-and-imputation","dir":"Articles","previous_headings":"Before Input","what":"Tips for normalization and imputation","title":"Get started","text":"choose normalization method, first consider data distribution. Use Z-score normalization data normally distributed, log transformation robust scaling ’s skewed. analysis requires data within specific range, like [0, 1], opt Min-Max normalization. datasets outliers, robust scaling rank normalization better choices. selecting imputation method, assess amount pattern missing data. small amount missing data, simple methods like mean, median, mode imputation effective. missing data related variables, consider sophisticated approaches like Predictive Mean Matching k-NN imputation. computational resources concern, simpler imputation methods might practical, resources allow, advanced methods can improve data quality. ’s hard decide, can always experiment different methods use cross-validation compare effectiveness. Let choice guided domain knowledge specific requirements analysis increase quality following steps.","code":""},{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"multi-omics-yes-or-no","dir":"Articles","previous_headings":"Data Format","what":"Multi-omics: Yes or No","title":"Get started","text":"visualization pipeline starts pre-processing module includes built-functions reshape data inputs standard format streamline data preparation. Depending nature datasets, two primary conditions consider: research object includes multi-omics measurements, assume provide list dataframes, collected different omics type. example, might datasets peptides, metabolites, lipids. combine together standard long dataset row represents feature, simply run pre_process() function, conduct data preparation yield standard input format demonstrated . Datasets multi-omics, can still utilize dashboard long ensuring data inputs reformatted standard longitudinal format. situation, can manually assign type column category label describe major groups data, input data pre_processing() module data preparation . case study, utilized “Kingdom” type label column cheese data. datasets multi-omics, can simply apply dashboard reformatting data standard longitudinal format. case, manually assign category label type column represent major groups. can still input data pre_processing() module cleaning preparation. case study, used “Kingdom” type label cheese datasets.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"data-format-and-pre-process","dir":"Articles","previous_headings":"Data Format","what":"Data Format and Pre-process","title":"Get started","text":"figure, , B, C can represent datasets different sources different aspects measurements. two examples input data might look like transform required format pre_process() modules.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"if-your-input-is-a-list-of-datasets","dir":"Articles","previous_headings":"Data Format > Data Format and Pre-process","what":"If your input is a list of datasets:","title":"Get started","text":"list datasets, following format: ID column identical column names. pre_process():","code":"str(fungal_dlist) #> List of 3 #> $ :'data.frame': 149786 obs. of 12 variables: #> ..$ ID : chr [1:149786] \"grp1_1\" \"grp1_2\" \"grp1_6\" \"grp1_7\" ... #> ..$ D0_01 : num [1:149786] -0.33 -0.33 1.591 1.106 0.659 ... #> ..$ D1_02 : num [1:149786] 0.628 0.628 3.087 3.618 2.843 ... #> ..$ D2_03 : num [1:149786] -1.223 -1.223 0.846 1.178 1.435 ... #> ..$ D3_04 : num [1:149786] -2.411 -1.808 1.239 1.753 0.258 ... #> ..$ D4_05 : num [1:149786] -1.41 -1.18 2.05 1.64 1.21 ... #> ..$ D5_06 : num [1:149786] -2.245 -2.309 0.83 1.64 0.395 ... #> ..$ D6_07 : num [1:149786] -0.784 -1.125 2.758 2.888 2.516 ... #> ..$ D7_08 : num [1:149786] -2.61197 -2.68724 0.42236 0.92846 0.00892 ... #> ..$ D8_09 : num [1:149786] -0.951 -1.3 2.417 2.454 1.418 ... #> ..$ D9_10 : num [1:149786] -1.91 -2.61 1.33 1.8 2.25 ... #> ..$ D10_11: num [1:149786] -2.871 -3.417 1.101 0.619 1.275 ... #> $ :'data.frame': 150 obs. of 12 variables: #> ..$ ID : chr [1:150] \"grp2_1\" \"grp2_2\" \"grp2_3\" \"grp2_4\" ... #> ..$ D0_01 : num [1:150] 0.947 1.012 0.156 0.31 0.138 ... #> ..$ D1_02 : num [1:150] -1.616 -0.917 -1.581 -2.717 -2.578 ... #> ..$ D2_03 : num [1:150] -2.4 -1.67 -2.31 -3.02 -3.66 ... #> ..$ D3_04 : num [1:150] -0.5734 0.0336 -0.351 -1.3052 -1.737 ... #> ..$ D4_05 : num [1:150] 0.662 0.854 1.002 0.12 -0.371 ... #> ..$ D5_06 : num [1:150] -1.09 -1.28 -1.04 -1.82 -3.15 ... #> ..$ D6_07 : num [1:150] -0.212 0.718 0.189 -0.627 -1.081 ... #> ..$ D7_08 : num [1:150] 0.969 2.117 1.462 0.339 -0.038 ... #> ..$ D8_09 : num [1:150] -0.712 0.43 -0.281 -1.297 -1.822 ... #> ..$ D9_10 : num [1:150] -0.649 0.45 -0.124 -1.77 -1.962 ... #> ..$ D10_11: num [1:150] -1.05 -0.85 -1.58 -1.91 -4.05 ... #> $ :'data.frame': 369 obs. of 12 variables: #> ..$ ID : chr [1:369] \"grp3_1\" \"grp3_2\" \"grp3_3\" \"grp3_4\" ... #> ..$ D0_01 : num [1:369] -0.918 -2.112 -1.372 -0.4 -1.75 ... #> ..$ D1_02 : num [1:369] -1.219 -3.502 -1.504 -0.615 -2.11 ... #> ..$ D2_03 : num [1:369] -0.542 -2.324 -0.592 0.161 -1.342 ... #> ..$ D3_04 : num [1:369] -0.101 -1.495 0.1 0.603 -1.058 ... #> ..$ D4_05 : num [1:369] 1.715 1.557 1.89 2.296 0.653 ... #> ..$ D5_06 : num [1:369] -1.452 -2.37 -1.182 -0.846 -2.531 ... #> ..$ D6_07 : num [1:369] 0.0879 -0.9982 0.4382 0.6215 -0.9464 ... #> ..$ D7_08 : num [1:369] 0.0502 0.4668 0.5378 0.8353 -0.9213 ... #> ..$ D8_09 : num [1:369] -0.0268 -0.4226 0.4528 0.6328 -1.0998 ... #> ..$ D9_10 : num [1:369] -0.5807 -1.7755 -0.1956 0.0866 -1.5876 ... #> ..$ D10_11: num [1:369] 0.118 -0.16 0.687 0.572 -1.037 ... #> ID D0_01 D1_02 D2_03 D3_04 D4_05 D5_06 #> 1 grp1_1 -0.3297741 0.6284682 -1.2232968 -2.411339 -1.413062 -2.2445487 #> 2 grp1_2 -0.3297741 0.6284682 -1.2232968 -1.807533 -1.183498 -2.3088022 #> 3 grp1_6 1.5908054 3.0871284 0.8460835 1.239411 2.054109 0.8300876 #> 4 grp1_7 1.1061795 3.6176250 1.1781012 1.753078 1.643661 1.6400102 #> D6_07 D7_08 D8_09 D9_10 D10_11 #> 1 -0.7839412 -2.6119663 -0.9511467 -1.914273 -2.8711810 #> 2 -1.1251434 -2.6872417 -1.2996614 -2.610647 -3.4165861 #> 3 2.7576288 0.4223618 2.4165602 1.333387 1.1012825 #> 4 2.8877343 0.9284640 2.4538561 1.799019 0.6187648 #> ID D0_01 D1_02 D2_03 D3_04 D4_05 D5_06 #> 1 grp2_1 0.9467431 -1.6162900 -2.400394 -0.57337845 0.6616711 -1.090914 #> 2 grp2_2 1.0120048 -0.9165994 -1.674330 0.03361378 0.8540950 -1.283964 #> 3 grp2_3 0.1561394 -1.5805490 -2.307281 -0.35095093 1.0018741 -1.035030 #> 4 grp2_4 0.3104578 -2.7173471 -3.022886 -1.30521496 0.1198075 -1.816677 #> D6_07 D7_08 D8_09 D9_10 D10_11 #> 1 -0.2116778 0.9688582 -0.7123773 -0.6489204 -1.0504845 #> 2 0.7181970 2.1174460 0.4302981 0.4504066 -0.8503481 #> 3 0.1885422 1.4619677 -0.2805926 -0.1235090 -1.5757556 #> 4 -0.6271954 0.3393109 -1.2967938 -1.7697248 -1.9073101 #> ID D0_01 D1_02 D2_03 D3_04 D4_05 D5_06 #> 1 grp3_1 -0.9178218 -1.2193128 -0.5415780 -0.1007493 1.715495 -1.4515238 #> 2 grp3_2 -2.1120636 -3.5015326 -2.3236475 -1.4949366 1.557463 -2.3698578 #> 3 grp3_3 -1.3724446 -1.5043101 -0.5922380 0.1004978 1.889573 -1.1815469 #> 4 grp3_4 -0.4002445 -0.6145252 0.1613003 0.6031744 2.295751 -0.8464012 #> D6_07 D7_08 D8_09 D9_10 D10_11 #> 1 0.08792489 0.05020749 -0.02681783 -0.58065562 0.1177374 #> 2 -0.99822332 0.46681389 -0.42255125 -1.77554842 -0.1603138 #> 3 0.43822354 0.53780990 0.45276561 -0.19558682 0.6870272 #> 4 0.62145080 0.83528878 0.63275758 0.08663959 0.5720591 data <- pre_process(fungal_dlist, typenameList = c('DataSource_A','DataSource_B','DataSource_C'), replaceNA = TRUE, scale = TRUE, autoColName = \"Section_\") #> [1] \"Reformat a list of datasets:\" head(data,10) #> ID D0_01 D1_02 D2_03 D3_04 D4_05 D5_06 #> 1 grp1_1 1.06146675 1.956656 0.2267383 -0.88312967 0.04945980 -0.7273146 #> 2 grp1_2 1.08465667 1.916983 0.3085454 -0.19892034 0.34311497 -0.6343214 #> 3 grp1_6 -0.01899274 1.717871 -0.8834318 -0.42687491 0.51878884 -0.9019992 #> 4 grp1_7 -0.75869657 2.051470 -0.6782202 -0.03485388 -0.15728499 -0.1613700 #> 5 grp1_8 -0.68132827 1.648062 0.1465161 -1.10875822 -0.09101615 -0.9629427 #> 6 grp1_11 0.27709905 1.981042 -0.5786717 0.02888008 0.54647685 -0.7013406 #> 7 grp1_12 0.90487968 2.180079 -0.6439891 -0.14841544 -0.69040101 -0.3834938 #> 8 grp1_13 0.46335031 1.695812 -1.0938459 -0.76868293 0.74415950 -0.6861036 #> 9 grp1_14 0.72014702 1.708604 -0.9344900 -0.35796963 0.63730277 -0.9872778 #> 10 grp1_15 0.54526798 1.784716 -0.8846753 -0.49538649 0.62916563 -0.9742293 #> D6_07 D7_08 D8_09 D9_10 D10_11 type #> 1 0.6371841 -1.0705560 0.4809808 -0.41877137 -1.31271417 DataSource_A #> 2 0.3938012 -0.9630327 0.2422154 -0.89650312 -1.59653914 DataSource_A #> 3 1.3354026 -1.3752687 0.9395058 -0.31779210 -0.58720856 DataSource_A #> 4 1.2347631 -0.9575502 0.7492778 0.01655139 -1.30408627 DataSource_A #> 5 1.2995756 -1.3741657 0.1285139 1.01932257 -0.02377948 DataSource_A #> 6 1.1406352 -1.6339536 0.2041455 -0.45793129 -0.80638109 DataSource_A #> 7 0.4794959 -1.6340692 0.5360766 -0.31430867 -0.28585374 DataSource_A #> 8 1.2235141 -1.2241931 0.6790383 -0.28487806 -0.74817082 DataSource_A #> 9 1.1466328 -1.4265286 0.5357111 -0.51799361 -0.52413804 DataSource_A #> 10 1.1813273 -1.2944121 0.6417621 -0.46906150 -0.66447461 DataSource_A"},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"if-your-input-is-a-single-dataset","dir":"Articles","previous_headings":"Data Format > Data Format and Pre-process","what":"If your input is a single dataset:","title":"Get started","text":"dataset already includes labeled ‘type’ column: pre_process():","code":"#> ID Day_1 Day_2 Day_3 Day_4 Day_5 Day_6 #> 1 1 NA NA 0.104083550 -1.07999900 1.3416214 -1.733564471 #> 2 2 NA NA 0.882578509 0.08440181 0.7165712 0.004811372 #> 3 3 0.40203917 NA 0.675076590 0.97017740 -0.8210872 0.659073443 #> 4 4 1.02859068 0.30178418 0.412617771 0.39306149 -0.3427640 0.837009714 #> 5 5 -2.14036218 -0.06315709 0.656982200 -0.18301850 -1.3967562 -0.123327229 #> 6 6 -2.04354192 1.30960484 1.371349945 -1.67403067 -1.2946150 0.760315504 #> 7 7 0.31164367 2.26615374 0.972843085 -0.03956293 1.0190965 1.180680430 #> 8 8 -0.79692858 -0.86038791 -3.417872877 0.62118867 -2.1983197 -0.104229763 #> 9 9 -0.12033042 1.17585682 -0.001075426 0.01122359 0.3430992 -0.189234532 #> 10 10 0.02592915 0.48551867 0.841254390 -0.46430744 0.6891800 0.751766756 #> Day_7 Day_8 type #> 1 0.4638261 2.1318716 peptide #> 2 -0.2949926 -0.9310538 peptide #> 3 -0.2520195 0.7809977 peptide #> 4 -1.0120371 -0.1162361 peptide #> 5 -2.0085970 -0.8959521 peptide #> 6 0.7874222 -0.5106016 peptide #> 7 -0.7719437 -1.4686258 peptide #> 8 0.3541200 0.8224521 lipid #> 9 0.5674059 -0.9313882 lipid #> 10 -0.4097464 NA metabolite x1 <- pre_process(x, replaceNA = TRUE,scale = TRUE) head(x1,10) #> ID Day_1 Day_2 Day_3 Day_4 Day_5 Day_6 #> 1 1 -0.12499488 -0.1249949 -0.04022866 -1.00455207 0.9676290 -1.53681943 #> 2 2 -0.10246242 -0.1024624 1.46237249 0.04718422 1.1680372 -0.09393173 #> 3 3 0.16412393 -0.4940273 0.61109508 1.09418472 -1.8381738 0.58489741 #> 4 4 1.27432720 0.1728189 0.34079228 0.31115385 -0.8040231 0.98397755 #> 5 5 -1.35954882 0.7001734 1.41425156 0.58132077 -0.6222015 0.64050965 #> 6 6 -1.35505505 1.0595199 1.10398207 -1.08897287 -0.8157586 0.66398092 #> 7 7 -0.10291149 1.5438734 0.45418630 -0.39882285 0.4931574 0.62930098 #> 8 8 -0.06748422 -0.1105542 -1.84632390 0.89499455 -1.0186109 0.40265173 #> 9 9 -0.36947382 1.7376966 -0.17560476 -0.15561064 0.3839091 -0.48148903 #> 10 10 -0.41003145 0.4704748 1.15201231 -1.34925296 0.8606601 0.98056711 #> Day_7 Day_8 type #> 1 0.2527477 1.6112132 peptide #> 2 -0.6254920 -1.7532453 peptide #> 3 -0.9065915 0.7844915 peptide #> 4 -1.8183370 -0.4607098 peptide #> 5 -1.2288927 -0.1256124 peptide #> 6 0.6835002 -0.2511966 peptide #> 7 -1.0158949 -1.6028889 peptide #> 8 0.7137346 1.0315924 lipid #> 9 0.7485573 -1.6879846 lipid #> 10 -1.2447220 -0.4597079 metabolite"},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"choose-your-annotation","dir":"Articles","previous_headings":"","what":"Choose Your Annotation","title":"Get started","text":"addition specifying data type mentioned , methods support three levels information: functional annotation, taxonomy annotation, feature annotation. annotations matched ID columns annotation data, serving another crucial input generating dashboard. facilitate automatic feature link generation using KeggID GOID, users set corresponding column names beforehand. example annotation formats align data inputs. data two layers labels (like cheese dataset), can specify two layers want focus network using gAnnotation() function.","code":"#> ID taxonomic.scope ko_term Pathway #> 1 grp1_24 Bacteria K02706 00195_Photosynthesis #> 2 grp1_25 Bacteria K02706 00195_Photosynthesis #> 3 grp1_26 Bacteria K02706 00195_Photosynthesis #> 4 grp1_1075 Bacteria K02112 00195_Photosynthesis #> 5 grp1_1076 Bacteria K02112 00195_Photosynthesis #> 6 grp1_1358 Bacteria K02112 00195_Photosynthesis Annotation_path_taxon <- gAnnotation(annotations,\"phylum\",\"class\")"},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"network-generation","dir":"Articles","previous_headings":"","what":"Network Generation","title":"Get started","text":"generate network, first scale cluster trajectories across input molecular features. Trajectories clustered using K-means, built-elbow method used select optimal K. build network clustered trajectories adapting GENIE3 algorithm. Specifically, network learned series regressions, cluster centroid predicted expression patterns cluster centroids using random forests. choose random forests potential model interacting features non-linearity without strong assumptions. determine feature clusters predictive given trajectory, Mean Decrease Accuracy subset top predictors whose expression directly influences expression target cluster taken indication putative link.","code":"cluster_result <- data |> gClusters(ncluster = 5,elbow.max=15) network_result <- cluster_result |> gNetwork(ntop = 3) gNetwork_view(network_result)"},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"dashboard-navigation","dir":"Articles","previous_headings":"","what":"Dashboard Navigation","title":"Get started","text":"workflow inputs related gDashboard function. key function processes data runs dashboard, requiring input previous steps. ’ve launched Shiny dashboard, can zoom make adjustments explore interesting findings within data. effectively navigate dashboard generated MolPad, ’ll follow three main steps:","code":"gDashboard(data, cluster_result, Annotation_path_taxon, network_result, id_colname = \"ko_term\" , id_type = \"KEGG\")"},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"choose-a-primary-functional-annotation-and-adjust-edge-density","dir":"Articles","previous_headings":"Dashboard Navigation","what":"Choose a Primary Functional Annotation and Adjust Edge Density","title":"Get started","text":"Start selecting primary functional annotation available options. , fine-tune edge density adjusting threshold value importance score. Nodes turn bright green indicate clusters containing features related chosen functional annotation.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"explore-the-network","dir":"Articles","previous_headings":"Dashboard Navigation","what":"Explore the Network","title":"Get started","text":"Brushing network unveils patterns taxonomic composition typical trajectories. can also zoom specific taxonomic annotations applying filters.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"investigate-feature-details-and-related-function-annotations","dir":"Articles","previous_headings":"Dashboard Navigation","what":"Investigate Feature Details and Related Function Annotations","title":"Get started","text":"Delve feature table examine specifics features within selected clusters. Explore additional related function annotations using drop-options. Click provided links access online information items interest. interface encourages iterative exploration, enabling conduct multiple steps answer specific questions, comparing pattern distributions two functions identifying functionally important community members metabolizing feature interest.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"related-visualization-outputs","dir":"Articles","previous_headings":"","what":"Related Visualization Outputs","title":"Get started","text":"following visualizations integrated dashboard, ’s need run function plot individually. ’ve linked stacked bar plot ribbon plot network facilitate focus context explorations. setup helps detecting similar longitudinal patterns understanding relationships.","code":""},{"path":[]},{"path":[]},{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/articles/whymolpad.html","id":"why-molpad","dir":"Articles","previous_headings":"","what":"Why MolPad?","title":"Why MolPad?","text":"aim network interpretation, MolPad shows improvements 3 important aspects: Capture groups features share longitudinal patterns functional properties within network. Annotate network complex experimental datasets, way dig specific components notable features. Streamlines exploration modularized functions interactive dashboard. package especially facilitates exploration longitudinal molecular co-expression microbiome experiments using focus-plus-context approach, empowering researchers uncover global patterns insights longitudinal microbiomics data. Unlike existing tools, MolPad stands rich network interactivity support contextualization. also equipped beginner-friendly interface self-contained data processing functions, making accessible users limited programming experience.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/whymolpad.html","id":"key-features","dir":"Articles","previous_headings":"","what":"Key Features","title":"Why MolPad?","text":"Trajectory clustering organizing data clusters, helps identify patterns, relationships, underlying structures within dataset. essence, clustering detects groups features observations behave similarly, allowing deeper insights effective decision-making. Network extracting build network clustered trajectories adapting GENIE3 algorithm. Specifically, network learned series regressions, cluster centroid predicted expression patterns cluster centroids using random forests. choose random forests potential model interacting features non-linearity without strong assumptions. Interactive Dashboard package provides interactive dashboard designed help users explore data uncover relationships within complex networks. utilizing focus-plus-context visualization, dashboard allows users zoom specific details keeping broader dataset view. approach enables deeper understanding data, making easier identify patterns make informed decisions.","code":""},{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/articles/whymolpad.html","id":"molpad-dashboard-overview","dir":"Articles","previous_headings":"What does it look like?","what":"MolPad Dashboard Overview","title":"Why MolPad?","text":"overview MolPad Dashboard. explore dashboard effectively, can start following sequence -B-C-D. approach guide cluster-level network, taxonomic-level bar plot, type-level line plot, feature-level table. , can modify detailed settings identify investigate special patterns within data.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/whymolpad.html","id":"discovering-related-patterns","dir":"Articles","previous_headings":"What does it look like?","what":"Discovering related patterns","title":"Why MolPad?","text":"short example discovering related patterns using network plot: shade edges represents proximity nodes. brushed area, Groups 1-7-8 (circled solid black lines) Groups 1-2 (circled blue dashed lines) strongly linked. Groups 1, 7, 8, patterns form W-shape noticeable peak time section. Although Group 1 shows higher volatility compared Group 2, groups display highly overlapping increasing trend. detailed instructions, please visit “Get Started” page relevant function reference page.","code":""},{"path":"https://kaiyanm.github.io/MolPad/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Kaiyan Ma. Author, maintainer.","code":""},{"path":"https://kaiyanm.github.io/MolPad/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Ma K (2024). MolPad: MolPad: R-Shiny Package Cluster Co-Expression Analysis Longitudinal Microbiomics. R package version 0.1.0, https://kaiyanm.github.io/MolPad/.","code":"@Manual{, title = {MolPad: MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics}, author = {Kaiyan Ma}, year = {2024}, note = {R package version 0.1.0}, url = {https://kaiyanm.github.io/MolPad/}, }"},{"path":"https://kaiyanm.github.io/MolPad/index.html","id":"molpad-","dir":"","previous_headings":"","what":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","title":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","text":"R-Shiny Package Cluster Co-Expression Analysis Longitudinal Microbiomics","code":""},{"path":"https://kaiyanm.github.io/MolPad/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","text":"MolPad offers visualization dashboard tool designed enhance understanding molecular co-expression works context microbiome data. approach involves using cluster network provide initial overview relationships across multiple omics, added functionality interactively zoom specific areas interest. facilitate analysis, ’ve developed focus-plus-context strategy connects online curated annotations. Additionally, package simplifies entire pipeline creating dashboard. user-friendly design makes accessible even people limited R programming experience. Check cheese-data demo .","code":""},{"path":"https://kaiyanm.github.io/MolPad/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","text":"can either install devtools, {r, eval = FALSE} # Install package R: install.packages(\"devtools\") library(devtools) install_github(\"KaiyanM/MolPad\") clone repository local computer (example, onto ./Github) installing: {r, eval = FALSE} # Download install package R: setwd(\"./GitHub\") install(\"MolPad\") , load package: {r,eval=FALSE} library(MolPad)","code":""},{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/index.html","id":"molpad-could-help-you-with","dir":"","previous_headings":"Usage","what":"MolPad could help you with:","title":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","text":"Clustering data k-means building group network. Find significant trend patterns datasets. Target interaction groups, taxons, pathways. Visualize distribution features specific pathways group network. Search particular features user-defined labels. Check detailed information feature automatically generated hyperlinks. better overall understanding datasets.","code":""},{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/index.html","id":"getting-help","dir":"","previous_headings":"","what":"Getting Help","title":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","text":"need assistance MolPad, two primary ways seek help: Ask us anything related MolPad! add question, create issue repository. Stack Overflow another excellent resource answering common issues R. Remember ’s particularly effective can provide reproducible example shows specific problem ’re .","code":""},{"path":"https://kaiyanm.github.io/MolPad/index.html","id":"contribution","dir":"","previous_headings":"","what":"Contribution","title":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","text":"contribute project, use following workflow: fork repository –> create local copy –> submit pull request.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":null,"dir":"Reference","previous_headings":"","what":"Cheese data — cheese","title":"Cheese data — cheese","text":"context cheese production, regular application brine solution maturation technique promotes uniformity microbial populations cheese's surface facilitates interactions among microorganisms. investigation involved analysis longitudinal dataset encompassing three washed-rind cheese communities sampled ripening process.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Cheese data — cheese","text":"two datasets: cheese annotations","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Cheese data — cheese","text":"Reference: doi:10.1128/msystems.00701-22","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Cheese data — cheese","text":"study uncovered remarkably consistent microbial progression within cheese. bacterial community, Firmicutes dominate outset, Proteobacteria swiftly assuming dominance end ripening period. Additionally, Cheese Cheese C consistently demonstrate establishment Actinobacteria Bacteroidetes, distinct manner. corroborate findings using MolPad dashboard, conducted analysis two cheeses (C) three production batches, spanning weeks 2 13.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":"cheese-data","dir":"Reference","previous_headings":"","what":"cheese data","title":"Cheese data — cheese","text":"data frame 106239 rows 18 variables: ID sample ID A_1~A_5 Time series data measured cheese . C_1~C_5 Time series data measured cheese C. domain category feature phylum category feature class category feature order category feature family category feature genus category feature","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":"annotation-data","dir":"Reference","previous_headings":"","what":"annotation data","title":"Cheese data — cheese","text":"data frame 86156 rows 9 variables: ID sample ID GO_ID GO IDs, represents link gene product type molecular function KEGG_ID KEGG IDs, linking genomic information higher order functional information. domain category feature phylum category feature class category feature order category feature family category feature genus category feature","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cheese data — cheese","text":"","code":"data(cheese) head(annotations) #> # A tibble: 6 × 9 #> ID GO_ID KEGG_ID domain phylum class order family genus #> #> 1 9 - - Bacteria Pseudomonadota Alphaproteobac… Caul… Caulo… Caul… #> 2 10 - - Bacteria Pseudomonadota Alphaproteobac… Caul… Caulo… Caul… #> 3 11 - - Bacteria Pseudomonadota Alphaproteobac… Hyph… Barto… Bart… #> 4 12 - - Bacteria Pseudomonadota Alphaproteobac… Hyph… Bruce… Bruc… #> 5 13 - - Bacteria Pseudomonadota Alphaproteobac… Hyph… Nitro… Brad… #> 6 14 - - Bacteria Pseudomonadota Alphaproteobac… Hyph… Phyll… Meso… head(cheese) #> # A tibble: 6 × 18 #> ID A_1 A_2 A_3 A_4 A_5 C_1 C_3 C_4 C_5 GO_ID KEGG_ID #> #> 1 1 38 23 27 5 11 13 1 3 0 NA NA #> 2 2 23 6 4 0 9 21 1 0 2 NA NA #> 3 3 24 5 37 10 19 3 0 1 4 NA NA #> 4 4 3 2 4 2 16 56 1 7 17 NA NA #> 5 5 58 14 45 13 32 82 3 8 3 NA NA #> 6 6 12 9 14 4 13 2 0 1 4 NA NA #> # ℹ 6 more variables: domain , phylum , class , order , #> # family , genus "},{"path":"https://kaiyanm.github.io/MolPad/reference/color_palettes__.html","id":null,"dir":"Reference","previous_headings":"","what":"Color palettes — color_palettes__","title":"Color palettes — color_palettes__","text":"internal function built-color palettes.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/color_palettes__.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Color palettes — color_palettes__","text":"","code":"color_palettes__(name)"},{"path":"https://kaiyanm.github.io/MolPad/reference/color_palettes__.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Color palettes — color_palettes__","text":"name string two options: \"graytone\" \"darkwarm\".","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/color_palettes__.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Color palettes — color_palettes__","text":"","code":"color_palettes__(\"graytone\") #> [1] \"#c3c5c7\" \"#30beba\" \"#998a73\" \"#807566\" \"#a5b1c9\" \"#5d5232\" \"#9a2b41\" #> [8] \"#d6b7a2\" \"#882db4\" \"#b47a53\" color_palettes__(\"darkwarm\") #> [1] \"#251305\" \"#C70A80\" \"#FBCB0A\" \"#ff1122\" \"#7D7463\" \"#CECE5A\" \"#FF9B9B\" #> [8] \"#A459D1\" \"#00235B\" \"#7E1717\""},{"path":"https://kaiyanm.github.io/MolPad/reference/convert_range.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert range — convert_range","title":"Convert range — convert_range","text":"internal function range conversion. element x, returns distance minimal values divided range.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/convert_range.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert range — convert_range","text":"","code":"convert_range(x)"},{"path":"https://kaiyanm.github.io/MolPad/reference/convert_range.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert range — convert_range","text":"x vector numbers.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/convert_range.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert range — convert_range","text":"vector calculated (x - min(x)) / (max(x) - min(x))","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/convert_range.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert range — convert_range","text":"","code":"convert_range(5:10) #> [1] 0.0 0.2 0.4 0.6 0.8 1.0"},{"path":"https://kaiyanm.github.io/MolPad/reference/extend.color__.html","id":null,"dir":"Reference","previous_headings":"","what":"Extend color palettes — extend.color__","title":"Extend color palettes — extend.color__","text":"internal function designed pair vectors color palettes automatically generate colors longer vectors find match.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/extend.color__.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extend color palettes — extend.color__","text":"","code":"extend.color__(n, colors, extendby = 1, alpha = 1, ...)"},{"path":"https://kaiyanm.github.io/MolPad/reference/extend.color__.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extend color palettes — extend.color__","text":"n number. length feature vector want match colors. colors vector colors (finite). extendby number select 1, 2, 3, 4 5, representing distinct auto-fill schemes. alpha number select range 0,1.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gAnnotation.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate processed annotation — gAnnotation","title":"Generate processed annotation — gAnnotation","text":"gAnnotation() provides standard input format dashboard, allowing users select two columns primary factors wish visualize describe data.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gAnnotation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate processed annotation — gAnnotation","text":"","code":"gAnnotation(data, first_order, second_order)"},{"path":"https://kaiyanm.github.io/MolPad/reference/gAnnotation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate processed annotation — gAnnotation","text":"data data.frame containing annotations used describing features measured time point. also required include ID least two categorical variables. first_order string. name one column categorical variable data. second_order string. name another column categorical variable data.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gAnnotation.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate processed annotation — gAnnotation","text":"Guidelines Selecting Annotations: first-order annotation recommended functional, pathway functional system. parameter primarily serve purpose filtering one network time displaying dashboard. second-order annotation utilized illustrate composition first-order annotation using bar plot. Therefore, better set taxon, class label, etc.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gAnnotation.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate processed annotation — gAnnotation","text":"","code":"data(test_data) test_annotations_processed <- gAnnotation(test_annotations,system,class) head(test_annotations_processed) #> ID #> 1 1 #> 2 2 #> 3 3 #> 4 4 #> 5 5 #> 6 6 #> GO_ID #> 1 #> 2 #> 3 GO:0003674,GO:0003824,GO:0005575,GO:0006629,GO:0006644,GO:0006650,GO:0006655,GO:0006793,GO:0006796,GO:0008150,GO:0008152,GO:0008610,GO:0008654,GO:0008808,GO:0009058,GO:0009987,GO:0016020,GO:0016740,GO:0016772,GO:0016780,GO:0019637,GO:0030572,GO:0032048,GO:0032049,GO:0044237,GO:0044238,GO:0044249,GO:0044255,GO:0045017,GO:0046471,GO:0046474,GO:0046486,GO:0071704,GO:0090407,GO:1901576 #> 4 GO:0003674,GO:0003676,GO:0003723,GO:0003729,GO:0003824,GO:0004654,GO:0005488,GO:0005575,GO:0005622,GO:0005623,GO:0005737,GO:0006139,GO:0006401,GO:0006402,GO:0006725,GO:0006807,GO:0008150,GO:0008152,GO:0009056,GO:0009057,GO:0009892,GO:0009987,GO:0010468,GO:0010605,GO:0010629,GO:0016070,GO:0016071,GO:0016740,GO:0016772,GO:0016779,GO:0019222,GO:0019439,GO:0034641,GO:0034655,GO:0043170,GO:0044237,GO:0044238,GO:0044248,GO:0044260,GO:0044265,GO:0044270,GO:0044424,GO:0044464,GO:0046483,GO:0046700,GO:0048519,GO:0050789,GO:0060255,GO:0065007,GO:0071704,GO:0090304,GO:0097159,GO:1901360,GO:1901361,GO:1901363,GO:1901575 #> 5 #> 6 #> KEGG_ID Pathway taxonomic.scope #> 1 K07124 Integumentary System hormonal proteins #> 2 Skeletal System structural proteins #> 3 K06131 Muscular System enzymes #> 4 K00962 Nervous System contractile proteins #> 5 K02083 Endocrine System contractile proteins #> 6 Cardiovascular System structural proteins"},{"path":"https://kaiyanm.github.io/MolPad/reference/gClusters.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate clusters — gClusters","title":"Generate clusters — gClusters","text":"gClusters() returns clusters generated k-means yield elbow plot way finding optimal parameter.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gClusters.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate clusters — gClusters","text":"","code":"gClusters(data, ncluster = 20, elbow.max = 50, ...)"},{"path":"https://kaiyanm.github.io/MolPad/reference/gClusters.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate clusters — gClusters","text":"data scaled data.frame contain variables ID, value time_1, ..., value time_k, type extracting patterns across time. See also pre_process(). ncluster number clusters. related complexity information network: choosing ncluster, suggest thinking many nodes show visualization representative want clustered pattern. elbow.max number maximum value x-axis elbow method plot. larger expected ncluster smaller sample size. iter.max number maximum iterations allowed k-means. See also stats::kmeans. nstart number random attempts generating initial configurations. k-means algorithm choose best one among attempts. larger data, 'nstart' can set lower just set 1. See also stats::kmeans.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gClusters.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate clusters — gClusters","text":"function return list 2 elements: k-means cluster result elbow method plot.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gClusters.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate clusters — gClusters","text":"determine optimal number clusters (ncluster), advised closely examine elbow plot identify point graph substantial change 'elbow' occurs. often indicative suitable cluster count. cases dataset extensive intricate, might consider increasing value elbow.max ensure comprehensive exploration potential cluster counts. can help achieving accurate meaningful results, especially working larger complex datasets. function can executed data parameter outset. However, achieve best clustering results, adjustments recommended. initial run, users expected adjust function's parameters based clustering outcomes elbow plot analysis.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gClusters.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate clusters — gClusters","text":"","code":"data(test_data) reslist <- gClusters(test_data_processed) # k-means result reslist[[1]] #> K-means clustering with 20 clusters of sizes 7, 6, 7, 10, 8, 2, 3, 5, 4, 6, 10, 3, 3, 4, 4, 6, 4, 3, 2, 3 #> #> Cluster means: #> T1 T2 T3 T4 T5 T6 #> 1 2.22789830 0.02950883 -0.75836275 -0.2619747 -0.5953255 -0.70974025 #> 2 2.77349939 -0.21724464 -0.35972402 -0.4172516 -0.5000122 -0.11678355 #> 3 0.93534606 -0.39771771 0.31238452 1.1531782 1.1995301 -1.05203742 #> 4 -0.27715339 -0.39760069 -0.06466866 1.3995407 2.1316423 -0.28068841 #> 5 0.11559709 -0.19000668 -0.25629794 -0.1487697 -0.4383348 2.58301481 #> 6 -0.27847690 -0.82258408 -0.82258408 -0.8225841 1.2338761 -0.82258408 #> 7 0.26998015 -0.08094677 -0.28926312 -0.7813127 -0.6065051 -0.14945230 #> 8 -0.81355388 -0.84696982 -0.32573017 1.1218534 2.0955812 -0.56207309 #> 9 1.45173820 0.26236463 1.31600581 -0.2531012 0.2004547 -1.12389420 #> 10 0.34700978 0.13673201 0.82464908 0.2571569 1.2656031 0.63541302 #> 11 -0.19020083 -0.69654961 -0.66836949 -0.4657663 -0.3851487 -0.55904213 #> 12 1.62582942 1.51619556 -0.35316244 0.2978109 -0.1777508 0.02299732 #> 13 -0.09187847 -0.87257293 -0.87257293 -0.8725729 -0.5696817 2.05668333 #> 14 0.85679806 -0.37993358 -0.46099690 0.3934155 0.5857582 1.85027957 #> 15 1.12205113 0.09185659 -0.89815993 -0.7233153 -0.5717924 -0.86037548 #> 16 2.09193480 -0.33320285 -0.50786563 -0.5579659 -0.5579659 1.54559773 #> 17 1.56599593 -0.49119311 -0.83000857 -0.6160199 -0.2092753 1.19151568 #> 18 0.89463785 0.01698143 -0.57023879 -1.1815834 -1.1815834 1.18824796 #> 19 -0.13543375 -0.90710305 -0.66248003 -0.4178570 -0.6624800 1.37010464 #> 20 1.31631350 -0.76059916 -0.76059916 1.5193444 0.6006058 0.84385791 #> T7 T8 T9 T10 #> 1 -0.567081109 0.132060988 0.03929621 0.46372005 #> 2 -0.357532815 -0.298895212 -0.29119673 -0.21485863 #> 3 -1.222209771 -0.334925678 -0.36853535 -0.22501291 #> 4 -0.548494719 -0.646992821 -0.66392148 -0.65166279 #> 5 -0.006327004 -0.327677972 -0.66559889 -0.66559889 #> 6 -0.822584085 1.233876127 1.23387613 0.68976894 #> 7 -0.763902351 -0.412975440 0.39220455 2.42217307 #> 8 -0.651292750 -0.498839228 0.42446013 0.05656427 #> 9 -0.987867401 -0.340013670 -0.33085684 -0.19483004 #> 10 -1.004339966 -0.620506186 -0.71949086 -1.12222690 #> 11 -0.566522777 0.002653353 1.73917982 1.78976662 #> 12 -0.770498738 -0.620423681 -0.77049874 -0.77049874 #> 13 0.882337606 0.086536293 0.12686085 0.12686085 #> 14 -0.526130916 -0.896529515 -0.89652951 -0.52613092 #> 15 -0.822591022 0.091471523 1.00908283 1.56177203 #> 16 -0.425513174 -0.557965933 -0.30571480 -0.39133827 #> 17 0.774154665 0.196302112 -0.62663627 -0.95483532 #> 18 0.894637851 -0.836550610 0.41343688 0.36201421 #> 19 -0.907103054 -0.417856999 1.37010464 1.37010464 #> 20 -0.666108041 -0.666108041 -0.66610804 -0.76059916 #> #> Clustering vector: #> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 #> 11 16 6 10 10 11 13 12 4 8 3 7 15 2 3 8 5 11 9 1 #> 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 #> 17 11 5 16 2 8 20 1 5 4 17 4 10 6 4 10 3 15 11 12 #> 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 #> 5 16 11 11 14 11 5 14 16 5 1 17 18 15 5 17 13 4 1 4 #> 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 #> 3 14 20 15 10 16 2 14 9 9 7 8 5 12 7 4 3 4 3 20 #> 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 #> 3 19 1 13 19 16 9 2 18 2 4 18 4 1 11 10 8 2 11 1 #> #> Within cluster sum of squares by cluster: #> [1] 13.8467580 2.0713631 15.4102772 5.9461649 8.0412334 0.9921913 #> [7] 3.1842269 3.4617088 9.9418120 17.6305488 8.0515257 4.9390800 #> [13] 4.0041862 7.3057948 4.7723673 3.0260293 7.3290085 5.6033175 #> [19] 0.9549143 2.4583646 #> (between_SS / total_SS = 83.4 %) #> #> Available components: #> #> [1] \"cluster\" \"centers\" \"totss\" \"withinss\" \"tot.withinss\" #> [6] \"betweenss\" \"size\" \"iter\" \"ifault\" # elbow plot reslist[[2]]"},{"path":"https://kaiyanm.github.io/MolPad/reference/gDashboard.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate shiny dashboard — gDashboard","title":"Generate shiny dashboard — gDashboard","text":"outputs g-functions, ready create custom Molpad dashboard. sure specify web ID columns corresponding column names.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gDashboard.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate shiny dashboard — gDashboard","text":"","code":"gDashboard( data, cluster, annotation, networkres, dashboardtitle = \"MolPad Dashboard\", id_colname = NULL, id_type = NULL )"},{"path":"https://kaiyanm.github.io/MolPad/reference/gDashboard.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate shiny dashboard — gDashboard","text":"data output pre_process():scaled data.frame contain variables ID, value time_1, ..., value time_k, type. cluster output gClusters(): list contains result k-means Cluster means, vectors sum squares. annotation output gAnnotation(): data.frame containing annotations describing features. Variables must include ID, Pathway, taxonomic.scope. Note NA permitted 3 variables. networkres output gNetwork(): data.frame 4 variables weight,IncNodePurity,var_names,. dashboardtitle string customized dashboard name. id_colname single string string vector. column names annotation dataset vector contains external database IDs. id_type single string string vector. corresponding database names columns, must chosen \"KEGG\" \"GO\".","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gDashboard.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate shiny dashboard — gDashboard","text":"Please ensure columns containing external database IDs adhere following standards: KEGG ID: Begin 'K' followed 5 digits, example, K05685 K06671. GO ID: Begin 'GO:' followed 7 digits, GO:0003674.\"","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gDashboard.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate shiny dashboard — gDashboard","text":"","code":"if (FALSE) { data(test_data) gDashboard(test_data_processed,test_cluster,test_annotations_processed,test_network,id_colname = c(\"GO_ID\",\"KEGG_ID\"),id_type = c(\"GO\",\"KEGG\")) }"},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate prediction network — gNetwork","title":"Generate prediction network — gNetwork","text":"gNetwork() generates prediction network functional annotation. every feature, features considered independent variables, top predictors selected based %IncMSE.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate prediction network — gNetwork","text":"","code":"gNetwork(clusters, ntop = 10, method = \"randomforest\")"},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate prediction network — gNetwork","text":"clusters list two outputs gClusters(). first element k-means result (See also stats::kmeans), element plot automatically omitted ease directly passing results function. ntop number pick set top n predictors(default: top 10) feature important ones. used construct network among clustered patterns. seed number random seed.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate prediction network — gNetwork","text":"assess relationships clusters using Mean Squared Error (MSE) changes resulting random shuffling. gNetwork()'s output includes edge weights node pairs, essential inputs dashboard.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate prediction network — gNetwork","text":"","code":"data(test_data) networkres <- gNetwork(test_cluster, ntop = 3) head(networkres) #> weight IncNodePurity var_names from #> 1 1.6429045 0.7371260 Group_3 Group_1 #> 2 0.4588961 0.9310697 Group_5 Group_1 #> 3 -0.7833169 0.6761286 Group_2 Group_1 #> 4 5.0076415 0.8821582 Group_5 Group_2 #> 5 4.1618215 1.1976058 Group_4 Group_2 #> 6 2.5714583 1.2820391 Group_1 Group_2 gNetwork_view(networkres)"},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork_view.html","id":null,"dir":"Reference","previous_headings":"","what":"View the feature importance plot — gNetwork_view","title":"View the feature importance plot — gNetwork_view","text":"gNetwork_view() Generate feature importance plot random forest result. plot visually highlights importance individual cluster within dataset, helping identify key factors predictive modeling.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork_view.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"View the feature importance plot — gNetwork_view","text":"","code":"gNetwork_view(data)"},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork_view.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"View the feature importance plot — gNetwork_view","text":"data dataframe; output gNetwork(), including 4 variables: weight, IncNodePurity, var_names . weight edge value, IncNodePurity(%IncMSE) measure predictions result var_names permuted. dependent variable round random forest.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork_view.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"View the feature importance plot — gNetwork_view","text":"","code":"data(test_data) gNetwork_view(test_network) #> Warning: Removed 1 rows containing missing values (`geom_segment()`). #> Warning: Removed 1 rows containing missing values (`geom_point()`)."},{"path":"https://kaiyanm.github.io/MolPad/reference/make_line_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Make line plot — make_line_plot","title":"Make line plot — make_line_plot","text":"Generate line ribbon plot dashboard.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_line_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make line plot — make_line_plot","text":"","code":"make_line_plot(dfgroup_long, selected_groups, selected_taxa)"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_line_plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Make line plot — make_line_plot","text":"function makes ribbon plot every brush action Shiny show min, max, mean value clustered pattern group across time. ribbon plot grouped colored type variable input datasets. See also ggplot2::geom_ribbon.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_line_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Make line plot — make_line_plot","text":"","code":"data(test_data) make_line_plot(test_maindata, \"Group_5\", c(\"hormonal proteins\",\"structural proteins\",\"enzymes\",\"storage proteins\",\"antibodies\",\"transport proteins\"))"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_stackbar_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Make stackbar plot — make_stackbar_plot","title":"Make stackbar plot — make_stackbar_plot","text":"Generate stackbar plot dashboard.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_stackbar_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make stackbar plot — make_stackbar_plot","text":"","code":"make_stackbar_plot(dfgroup_long, selected_groups, selected_taxa)"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_stackbar_plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Make stackbar plot — make_stackbar_plot","text":"function makes bar plot every brush action Shiny show components clustered pattern group. bar plot colored taxonomic.scope variable processed annotation dataset generated gAnnotation(). See also ggplot2::geom_bar.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_stackbar_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Make stackbar plot — make_stackbar_plot","text":"","code":"data(test_data) make_stackbar_plot(test_maindata, \"Group_5\", c(\"hormonal proteins\",\"structural proteins\",\"enzymes\",\"storage proteins\",\"antibodies\",\"transport proteins\"))"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_graph.html","id":null,"dir":"Reference","previous_headings":"","what":"Make graph plot — make_the_graph","title":"Make graph plot — make_the_graph","text":"Generate graph dashboard.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_graph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make graph plot — make_the_graph","text":"","code":"make_the_graph(ptw, network_output, min_weight, s_ptw, graph_layout)"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_graph.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Make graph plot — make_the_graph","text":"function makes network plot every Pathway selection Shiny show relationship among clustered pattern groups. network plot built dataset generated gNetwork() can adjusted layout minimum weights. See also ggraph::ggraph.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_graph.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Make graph plot — make_the_graph","text":"","code":"data(test_data) make_the_graph(test_graphptw, test_network, 0.03, \"Muscular System\",\"kk\")"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_table.html","id":null,"dir":"Reference","previous_headings":"","what":"Make table from brushed region — make_the_table","title":"Make table from brushed region — make_the_table","text":"Generate table selected groups dashboard.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make table from brushed region — make_the_table","text":"","code":"make_the_table(p)"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Make table from brushed region — make_the_table","text":"p ggplot output, see also ggplot2::ggplot_build.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_table.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Make table from brushed region — make_the_table","text":"function simply aims collect position information brushed area network plot returns annotation table corresponding features. takes plot object produce object information table.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/mass_produce_lm__.html","id":null,"dir":"Reference","previous_headings":"","what":"Mass produce linear model — mass_produce_lm__","title":"Mass produce linear model — mass_produce_lm__","text":"Take column dependent vairable time, produce n linear models n columns dataset.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/mass_produce_lm__.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mass produce linear model — mass_produce_lm__","text":"","code":"mass_produce_lm__(data)"},{"path":"https://kaiyanm.github.io/MolPad/reference/mass_produce_lm__.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mass produce linear model — mass_produce_lm__","text":"data dataframe.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/mass_produce_lm__.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Mass produce linear model — mass_produce_lm__","text":"internal function designed automatically generate list functions regression. column considered response variable columns. resulting functions can utilized inputs random forest regression gNetwork().","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/match.color__.html","id":null,"dir":"Reference","previous_headings":"","what":"Match color — match.color__","title":"Match color — match.color__","text":"Match vector finite list colors.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/match.color__.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Match color — match.color__","text":"","code":"match.color__(valist, mycolors, extendby = 5)"},{"path":"https://kaiyanm.github.io/MolPad/reference/match.color__.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Match color — match.color__","text":"valist vector want specify colors element. mycolors vector colors. extendby number select 1, 2, 3, 4, 5, representing distinct auto-fill schemes.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/match.color__.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Match color — match.color__","text":"","code":"my_vector <- paste0(\"N_\",1:10) match.color__(my_vector,c(\"red\",\"yellow\",\"blue\")) #> N_1 N_2 N_3 N_4 N_5 #> \"red\" \"yellow\" \"blue\" \"white\" \"antiquewhite3\" #> N_6 N_7 N_8 N_9 N_10 #> \"aquamarine3\" \"azure3\" \"bisque2\" \"blue\" \"blueviolet\""},{"path":"https://kaiyanm.github.io/MolPad/reference/match_database.html","id":null,"dir":"Reference","previous_headings":"","what":"Match database — match_database","title":"Match database — match_database","text":"matches selected columns online database names.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/match_database.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Match database — match_database","text":"","code":"match_database(data, id_colname, id_type)"},{"path":"https://kaiyanm.github.io/MolPad/reference/match_database.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Match database — match_database","text":"match_database() internal function matches selected columns dataset corresponding databases. Currently MolPad support two types annotation database: GO KEGG. want custom URL platform, download modify paste_URL() function.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/match_database.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Match database — match_database","text":"","code":"data(test_data) head(test_annotations_processed) #> ID #> 1 1 #> 2 2 #> 3 3 #> 4 4 #> 5 5 #> 6 6 #> GO_ID #> 1 #> 2 #> 3 GO:0003674,GO:0003824,GO:0005575,GO:0006629,GO:0006644,GO:0006650,GO:0006655,GO:0006793,GO:0006796,GO:0008150,GO:0008152,GO:0008610,GO:0008654,GO:0008808,GO:0009058,GO:0009987,GO:0016020,GO:0016740,GO:0016772,GO:0016780,GO:0019637,GO:0030572,GO:0032048,GO:0032049,GO:0044237,GO:0044238,GO:0044249,GO:0044255,GO:0045017,GO:0046471,GO:0046474,GO:0046486,GO:0071704,GO:0090407,GO:1901576 #> 4 GO:0003674,GO:0003676,GO:0003723,GO:0003729,GO:0003824,GO:0004654,GO:0005488,GO:0005575,GO:0005622,GO:0005623,GO:0005737,GO:0006139,GO:0006401,GO:0006402,GO:0006725,GO:0006807,GO:0008150,GO:0008152,GO:0009056,GO:0009057,GO:0009892,GO:0009987,GO:0010468,GO:0010605,GO:0010629,GO:0016070,GO:0016071,GO:0016740,GO:0016772,GO:0016779,GO:0019222,GO:0019439,GO:0034641,GO:0034655,GO:0043170,GO:0044237,GO:0044238,GO:0044248,GO:0044260,GO:0044265,GO:0044270,GO:0044424,GO:0044464,GO:0046483,GO:0046700,GO:0048519,GO:0050789,GO:0060255,GO:0065007,GO:0071704,GO:0090304,GO:0097159,GO:1901360,GO:1901361,GO:1901363,GO:1901575 #> 5 #> 6 #> KEGG_ID Pathway taxonomic.scope #> 1 K07124 Integumentary System hormonal proteins #> 2 Skeletal System structural proteins #> 3 K06131 Muscular System enzymes #> 4 K00962 Nervous System contractile proteins #> 5 K02083 Endocrine System contractile proteins #> 6 Cardiovascular System structural proteins head(match_database(test_annotations_processed,id_colname = c(\"GO_ID\",\"KEGG_ID\"),id_type = c(\"GO\",\"KEGG\"))) #> ID #> 1 1 #> 2 2 #> 3 3 #> 4 4 #> 5 5 #> 6 6 #> GO_ID #> 1 #> 2 #> 3 GO:0003674<\/a>
GO:0003824<\/a>
GO:0005575<\/a>
GO:0006629<\/a>
GO:0006644<\/a>
GO:0006650<\/a>
GO:0006655<\/a>
GO:0006793<\/a>
GO:0006796<\/a>
GO:0008150<\/a>
GO:0008152<\/a>
GO:0008610<\/a>
GO:0008654<\/a>
GO:0008808<\/a>
GO:0009058<\/a>
GO:0009987<\/a>
GO:0016020<\/a>
GO:0016740<\/a>
GO:0016772<\/a>
GO:0016780<\/a>
GO:0019637<\/a>
GO:0030572<\/a>
GO:0032048<\/a>
GO:0032049<\/a>
GO:0044237<\/a>
GO:0044238<\/a>
GO:0044249<\/a>
GO:0044255<\/a>
GO:0045017<\/a>
GO:0046471<\/a>
GO:0046474<\/a>
GO:0046486<\/a>
GO:0071704<\/a>
GO:0090407<\/a>
GO:1901576<\/a> #> 4 GO:0003674<\/a>
GO:0003676<\/a>
GO:0003723<\/a>
GO:0003729<\/a>
GO:0003824<\/a>
GO:0004654<\/a>
GO:0005488<\/a>
GO:0005575<\/a>
GO:0005622<\/a>
GO:0005623<\/a>
GO:0005737<\/a>
GO:0006139<\/a>
GO:0006401<\/a>
GO:0006402<\/a>
GO:0006725<\/a>
GO:0006807<\/a>
GO:0008150<\/a>
GO:0008152<\/a>
GO:0009056<\/a>
GO:0009057<\/a>
GO:0009892<\/a>
GO:0009987<\/a>
GO:0010468<\/a>
GO:0010605<\/a>
GO:0010629<\/a>
GO:0016070<\/a>
GO:0016071<\/a>
GO:0016740<\/a>
GO:0016772<\/a>
GO:0016779<\/a>
GO:0019222<\/a>
GO:0019439<\/a>
GO:0034641<\/a>
GO:0034655<\/a>
GO:0043170<\/a>
GO:0044237<\/a>
GO:0044238<\/a>
GO:0044248<\/a>
GO:0044260<\/a>
GO:0044265<\/a>
GO:0044270<\/a>
GO:0044424<\/a>
GO:0044464<\/a>
GO:0046483<\/a>
GO:0046700<\/a>
GO:0048519<\/a>
GO:0050789<\/a>
GO:0060255<\/a>
GO:0065007<\/a>
GO:0071704<\/a>
GO:0090304<\/a>
GO:0097159<\/a>
GO:1901360<\/a>
GO:1901361<\/a>
GO:1901363<\/a>
GO:1901575<\/a> #> 5 #> 6 #> KEGG_ID #> 1 K07124<\/a> #> 2 #> 3 K06131<\/a> #> 4 K00962<\/a> #> 5 K02083<\/a> #> 6 #> Pathway taxonomic.scope #> 1 Integumentary System hormonal proteins #> 2 Skeletal System structural proteins #> 3 Muscular System enzymes #> 4 Nervous System contractile proteins #> 5 Endocrine System contractile proteins #> 6 Cardiovascular System structural proteins"},{"path":"https://kaiyanm.github.io/MolPad/reference/paste_URL.html","id":null,"dir":"Reference","previous_headings":"","what":"Paste URL — paste_URL","title":"Paste URL — paste_URL","text":"Retrieve database IDs associate respective URLs.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/paste_URL.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Paste URL — paste_URL","text":"","code":"paste_URL(x, id_type)"},{"path":"https://kaiyanm.github.io/MolPad/reference/paste_URL.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Paste URL — paste_URL","text":"","code":"data(test_data) paste_URL(test_annotations$GO_ID[1:4], id_type = \"GO\") #> [1] NA #> [2] NA #> [3] \"GO:0003674<\/a>
GO:0003824<\/a>
GO:0005575<\/a>
GO:0006629<\/a>
GO:0006644<\/a>
GO:0006650<\/a>
GO:0006655<\/a>
GO:0006793<\/a>
GO:0006796<\/a>
GO:0008150<\/a>
GO:0008152<\/a>
GO:0008610<\/a>
GO:0008654<\/a>
GO:0008808<\/a>
GO:0009058<\/a>
GO:0009987<\/a>
GO:0016020<\/a>
GO:0016740<\/a>
GO:0016772<\/a>
GO:0016780<\/a>
GO:0019637<\/a>
GO:0030572<\/a>
GO:0032048<\/a>
GO:0032049<\/a>
GO:0044237<\/a>
GO:0044238<\/a>
GO:0044249<\/a>
GO:0044255<\/a>
GO:0045017<\/a>
GO:0046471<\/a>
GO:0046474<\/a>
GO:0046486<\/a>
GO:0071704<\/a>
GO:0090407<\/a>
GO:1901576<\/a>\" #> [4] \"GO:0003674<\/a>
GO:0003676<\/a>
GO:0003723<\/a>
GO:0003729<\/a>
GO:0003824<\/a>
GO:0004654<\/a>
GO:0005488<\/a>
GO:0005575<\/a>
GO:0005622<\/a>
GO:0005623<\/a>
GO:0005737<\/a>
GO:0006139<\/a>
GO:0006401<\/a>
GO:0006402<\/a>
GO:0006725<\/a>
GO:0006807<\/a>
GO:0008150<\/a>
GO:0008152<\/a>
GO:0009056<\/a>
GO:0009057<\/a>
GO:0009892<\/a>
GO:0009987<\/a>
GO:0010468<\/a>
GO:0010605<\/a>
GO:0010629<\/a>
GO:0016070<\/a>
GO:0016071<\/a>
GO:0016740<\/a>
GO:0016772<\/a>
GO:0016779<\/a>
GO:0019222<\/a>
GO:0019439<\/a>
GO:0034641<\/a>
GO:0034655<\/a>
GO:0043170<\/a>
GO:0044237<\/a>
GO:0044238<\/a>
GO:0044248<\/a>
GO:0044260<\/a>
GO:0044265<\/a>
GO:0044270<\/a>
GO:0044424<\/a>
GO:0044464<\/a>
GO:0046483<\/a>
GO:0046700<\/a>
GO:0048519<\/a>
GO:0050789<\/a>
GO:0060255<\/a>
GO:0065007<\/a>
GO:0071704<\/a>
GO:0090304<\/a>
GO:0097159<\/a>
GO:1901360<\/a>
GO:1901361<\/a>
GO:1901363<\/a>
GO:1901575<\/a>\""},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":null,"dir":"Reference","previous_headings":"","what":"Pre-processing datasets — pre_process","title":"Pre-processing datasets — pre_process","text":"pre_process() function aids processing data inputs automatically establishes standardized format future use. allows two types data input: list datasets different sources long dataset containing specified last column type.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pre-processing datasets — pre_process","text":"","code":"pre_process( data, typenameList = NULL, replaceNA = TRUE, scale = TRUE, autoColName = \"Sec_\" )"},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pre-processing datasets — pre_process","text":"data data.frame describe feature one row. data contain variables ID value time_1, ..., value time_k, type extracting patterns across time. Note initial last column must exactly ID type. multiple data.frame format needs analyzed, also put list data.frame argument. case, variable type required generated next argument typenameList. typenameList vector strings. parameter used clarify source names data.frame, applicable input data list data.frame. default, set \"Dataset_1\", \"Dataset_2\", ..., etc. scale Logical; scale TRUE (default), standardize data.frame row base::scale. converts original value z-score. See also scale_by_row__(). autoColName string; autoColName -NULL (default), automatically set uniform column names data.frames. parameter applicable input data list data.frame. replaceNa Logical; replaceNa TRUE (default), replace NA 0.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pre-processing datasets — pre_process","text":"function returns long data.frame columns ID, value time_1, ..., value time_k, type.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Pre-processing datasets — pre_process","text":"consider two distinct scenarios application: one scenario, individuals collect several datasets various aspects instruments objects. example, might separately detecting lipids, metabolites, peptides specific soil sample. scenario, data uniform quality, can categorized larger groups exhibit significant differences. cases, pre_process() function serves valuable versatile tool. Yet, function optional generating dashboard. Users can perform processing long format matches required output. However, mindful number samples (timepoints) must greater 5 avoid potential errors subsequent prediction section.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pre-processing datasets — pre_process","text":"","code":"data(test_data) head(test_data, 10) #> ID T1 T2 T3 T4 T5 T6 T7 T8 T9 T10 type #> 1 1 1 0 0 1 1 0 0 1 6 6 type_A #> 2 2 6 0 0 0 0 3 1 0 2 1 type_A #> 3 3 1 0 0 0 2 0 0 2 2 1 type_A #> 4 4 4 5 3 3 7 2 1 1 0 0 type_A #> 5 5 4 3 NA 2 5 5 0 0 0 0 type_A #> 6 6 4 1 0 1 3 1 3 5 11 14 type_A #> 7 7 1 0 0 0 1 3 3 1 1 1 type_A #> 8 8 4 2 1 1 1 1 0 0 0 0 type_A #> 9 9 1 1 1 19 22 1 2 1 1 2 type_A #> 10 10 1 1 3 5 8 2 2 2 5 2 type_A a <- pre_process(test_data) head(a, 10) #> ID T1 T2 T3 T4 T5 T6 #> 1 1 -0.25354628 -0.6761234 -0.67612340 -0.25354628 -0.25354628 -0.6761234 #> 2 2 2.41458180 -0.6678631 -0.66786305 -0.66786305 -0.66786305 0.8733594 #> 3 3 0.21764288 -0.8705715 -0.87057150 -0.87057150 1.30585725 -0.8705715 #> 4 4 0.61658123 1.0569964 0.17616607 0.17616607 1.93782672 -0.2642491 #> 5 5 0.96186009 0.5038315 -0.87025436 0.04580286 1.41988870 1.4198887 #> 6 6 -0.06459959 -0.7105955 -0.92592741 -0.71059546 -0.27993154 -0.7105955 #> 7 7 -0.09086738 -0.9995412 -0.99954118 -0.99954118 -0.09086738 1.7264802 #> 8 8 2.40535118 0.8017837 0.00000000 0.00000000 0.00000000 0.0000000 #> 9 9 -0.50260633 -0.5026063 -0.50260633 1.70395805 2.07171878 -0.5026063 #> 10 10 -0.94019379 -0.9401938 -0.04477113 0.85065153 2.19378551 -0.4924825 #> T7 T8 T9 T10 type #> 1 -0.6761234 -0.25354628 1.85933936 1.85933936 type_A #> 2 -0.1541222 -0.66786305 0.35961857 -0.15412224 type_A #> 3 -0.8705715 1.30585725 1.30585725 0.21764288 type_A #> 4 -0.7046643 -0.70466426 -1.14507943 -1.14507943 type_A #> 5 -0.8702544 -0.87025436 -0.87025436 -0.87025436 type_A #> 6 -0.2799315 0.15073237 1.44272411 2.08871998 type_A #> 7 1.7264802 -0.09086738 -0.09086738 -0.09086738 type_A #> 8 -0.8017837 -0.80178373 -0.80178373 -0.80178373 type_A #> 9 -0.3800194 -0.50260633 -0.50260633 -0.38001942 type_A #> 10 -0.4924825 -0.49248246 0.85065153 -0.49248246 type_A"},{"path":"https://kaiyanm.github.io/MolPad/reference/reshape_for_make_functions.html","id":null,"dir":"Reference","previous_headings":"","what":"Reshape for 'make' functions — reshape_for_make_functions","title":"Reshape for 'make' functions — reshape_for_make_functions","text":"internal function produces three primary datasets dashboard intended \"make\" functions.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/reshape_for_make_functions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reshape for 'make' functions — reshape_for_make_functions","text":"","code":"reshape_for_make_functions(data, cluster, annotation, id_colname, id_type)"},{"path":"https://kaiyanm.github.io/MolPad/reference/reshape_for_make_functions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reshape for 'make' functions — reshape_for_make_functions","text":"data output pre_process() cluster output gClusters() annotation output gPathway() id_colname columns contain database IDs. id_type corresponding database names columns.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/reshape_for_make_functions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reshape for 'make' functions — reshape_for_make_functions","text":"","code":"data(test_data) l <- reshape_for_make_functions(test_data_processed, test_cluster, test_annotations_processed, id_colname = c(\"GO_ID\",\"KEGG_ID\"),id_type = c(\"GO\",\"KEGG\")) head(l[[1]]) #> ID cluster #> 1 1 Group_2 #> 2 2 Group_5 #> 3 3 Group_2 #> 4 4 Group_4 #> 5 5 Group_4 #> 6 6 Group_2 #> GO_ID #> 1 #> 2 #> 3 GO:0003674,GO:0003824,GO:0005575,GO:0006629,GO:0006644,GO:0006650,GO:0006655,GO:0006793,GO:0006796,GO:0008150,GO:0008152,GO:0008610,GO:0008654,GO:0008808,GO:0009058,GO:0009987,GO:0016020,GO:0016740,GO:0016772,GO:0016780,GO:0019637,GO:0030572,GO:0032048,GO:0032049,GO:0044237,GO:0044238,GO:0044249,GO:0044255,GO:0045017,GO:0046471,GO:0046474,GO:0046486,GO:0071704,GO:0090407,GO:1901576 #> 4 GO:0003674,GO:0003676,GO:0003723,GO:0003729,GO:0003824,GO:0004654,GO:0005488,GO:0005575,GO:0005622,GO:0005623,GO:0005737,GO:0006139,GO:0006401,GO:0006402,GO:0006725,GO:0006807,GO:0008150,GO:0008152,GO:0009056,GO:0009057,GO:0009892,GO:0009987,GO:0010468,GO:0010605,GO:0010629,GO:0016070,GO:0016071,GO:0016740,GO:0016772,GO:0016779,GO:0019222,GO:0019439,GO:0034641,GO:0034655,GO:0043170,GO:0044237,GO:0044238,GO:0044248,GO:0044260,GO:0044265,GO:0044270,GO:0044424,GO:0044464,GO:0046483,GO:0046700,GO:0048519,GO:0050789,GO:0060255,GO:0065007,GO:0071704,GO:0090304,GO:0097159,GO:1901360,GO:1901361,GO:1901363,GO:1901575 #> 5 #> 6 #> KEGG_ID Pathway taxonomic.scope #> 1 K07124 Integumentary System hormonal proteins #> 2 Skeletal System structural proteins #> 3 K06131 Muscular System enzymes #> 4 K00962 Nervous System contractile proteins #> 5 K02083 Endocrine System contractile proteins #> 6 Cardiovascular System structural proteins head(l[[2]]) #> # A tibble: 6 × 6 #> ID type cluster day value taxonomic.scope #> #> 1 1 type_A Group_2 T1 -0.254 hormonal proteins #> 2 1 type_A Group_2 T2 -0.676 hormonal proteins #> 3 1 type_A Group_2 T3 -0.676 hormonal proteins #> 4 1 type_A Group_2 T4 -0.254 hormonal proteins #> 5 1 type_A Group_2 T5 -0.254 hormonal proteins #> 6 1 type_A Group_2 T6 -0.676 hormonal proteins head(l[[3]]) #> ID cluster #> 1 1 Group_2 #> 2 2 Group_5 #> 3 3 Group_2 #> 4 4 Group_4 #> 5 5 Group_4 #> 6 6 Group_2 #> GO_ID #> 1 #> 2 #> 3 GO:0003674<\/a>
GO:0003824<\/a>
GO:0005575<\/a>
GO:0006629<\/a>
GO:0006644<\/a>
GO:0006650<\/a>
GO:0006655<\/a>
GO:0006793<\/a>
GO:0006796<\/a>
GO:0008150<\/a>
GO:0008152<\/a>
GO:0008610<\/a>
GO:0008654<\/a>
GO:0008808<\/a>
GO:0009058<\/a>
GO:0009987<\/a>
GO:0016020<\/a>
GO:0016740<\/a>
GO:0016772<\/a>
GO:0016780<\/a>
GO:0019637<\/a>
GO:0030572<\/a>
GO:0032048<\/a>
GO:0032049<\/a>
GO:0044237<\/a>
GO:0044238<\/a>
GO:0044249<\/a>
GO:0044255<\/a>
GO:0045017<\/a>
GO:0046471<\/a>
GO:0046474<\/a>
GO:0046486<\/a>
GO:0071704<\/a>
GO:0090407<\/a>
GO:1901576<\/a> #> 4 GO:0003674<\/a>
GO:0003676<\/a>
GO:0003723<\/a>
GO:0003729<\/a>
GO:0003824<\/a>
GO:0004654<\/a>
GO:0005488<\/a>
GO:0005575<\/a>
GO:0005622<\/a>
GO:0005623<\/a>
GO:0005737<\/a>
GO:0006139<\/a>
GO:0006401<\/a>
GO:0006402<\/a>
GO:0006725<\/a>
GO:0006807<\/a>
GO:0008150<\/a>
GO:0008152<\/a>
GO:0009056<\/a>
GO:0009057<\/a>
GO:0009892<\/a>
GO:0009987<\/a>
GO:0010468<\/a>
GO:0010605<\/a>
GO:0010629<\/a>
GO:0016070<\/a>
GO:0016071<\/a>
GO:0016740<\/a>
GO:0016772<\/a>
GO:0016779<\/a>
GO:0019222<\/a>
GO:0019439<\/a>
GO:0034641<\/a>
GO:0034655<\/a>
GO:0043170<\/a>
GO:0044237<\/a>
GO:0044238<\/a>
GO:0044248<\/a>
GO:0044260<\/a>
GO:0044265<\/a>
GO:0044270<\/a>
GO:0044424<\/a>
GO:0044464<\/a>
GO:0046483<\/a>
GO:0046700<\/a>
GO:0048519<\/a>
GO:0050789<\/a>
GO:0060255<\/a>
GO:0065007<\/a>
GO:0071704<\/a>
GO:0090304<\/a>
GO:0097159<\/a>
GO:1901360<\/a>
GO:1901361<\/a>
GO:1901363<\/a>
GO:1901575<\/a> #> 5 #> 6 #> KEGG_ID #> 1 K07124<\/a> #> 2 #> 3 K06131<\/a> #> 4 K00962<\/a> #> 5 K02083<\/a> #> 6 #> Pathway taxonomic.scope #> 1 Integumentary System hormonal proteins #> 2 Skeletal System structural proteins #> 3 Muscular System enzymes #> 4 Nervous System contractile proteins #> 5 Endocrine System contractile proteins #> 6 Cardiovascular System structural proteins"},{"path":"https://kaiyanm.github.io/MolPad/reference/scale_by_row__.html","id":null,"dir":"Reference","previous_headings":"","what":"Scale by row — scale_by_row__","title":"Scale by row — scale_by_row__","text":"Scales values sample, row independently processed.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/scale_by_row__.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Scale by row — scale_by_row__","text":"","code":"scale_by_row__(data)"},{"path":"https://kaiyanm.github.io/MolPad/reference/scale_by_row__.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Scale by row — scale_by_row__","text":"input expected data frame first column ID following columns containing observations different time points. ID column remains unaltered, columns double (dbl) format scaled.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/scale_by_row__.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Scale by row — scale_by_row__","text":"","code":"data(test_data) scale_by_row__(test_data[1:5,1:10]) #> ID T1 T2 T3 T4 T5 T6 #> 1 1 -0.05847053 -0.5847053 -0.58470535 -0.05847053 -0.05847053 -0.5847053 #> 2 2 2.26366583 -0.6467617 -0.64676167 -0.64676167 -0.64676167 0.8084521 #> 3 3 0.22866478 -0.8003267 -0.80032673 -0.80032673 1.25765629 -0.8003267 #> 4 4 0.50395263 0.9575100 0.05039526 0.05039526 1.86462473 -0.4031621 #> 5 5 0.73869087 0.2841119 NA -0.17046712 1.19326987 1.1932699 #> T7 T8 T9 #> 1 -0.5847053 -0.05847053 2.5727035 #> 2 -0.1616904 -0.64676167 0.3233808 #> 3 -0.8003267 1.25765629 1.2576563 #> 4 -0.8567195 -0.85671947 -1.3102768 #> 5 -1.0796251 -1.07962512 -1.0796251"},{"path":"https://kaiyanm.github.io/MolPad/reference/test_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Test data — test_data","title":"Test data — test_data","text":"synthetically generated dataset created basic testing purposes.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/test_data.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Test data — test_data","text":"two datasets: data annotations","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/test_data.html","id":"data","dir":"Reference","previous_headings":"","what":"data","title":"Test data — test_data","text":"data frame 100 rows 12 variables: ID row ID T1~T10 count value 10 timepoints type type ~D","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/test_data.html","id":"annotations","dir":"Reference","previous_headings":"","what":"annotations","title":"Test data — test_data","text":"data frame 100 rows 5 variables: ID row ID GO_ID go ID KEGG_ID kegg ID system primary lable: 'Integumentary System', 'Skeletal System', 'Muscular System', 'Nervous System', 'Endocrine System', 'Cardiovascular System', 'Lymphatic System', 'Respiratory System', 'Digestive System', 'Urinary System' class secondary label: 'antibodies', 'contractile proteins', 'enzymes', 'hormonal proteins', 'structural proteins', 'storage proteins', 'transport proteins'","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/test_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Test data — test_data","text":"","code":"data(test_data)"},{"path":"https://kaiyanm.github.io/MolPad/reference/transpose_dataframe__.html","id":null,"dir":"Reference","previous_headings":"","what":"Transpose dataframe — transpose_dataframe__","title":"Transpose dataframe — transpose_dataframe__","text":"function transposes provided data frame, using values first column new column names.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/transpose_dataframe__.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transpose dataframe — transpose_dataframe__","text":"","code":"transpose_dataframe__(data)"},{"path":"https://kaiyanm.github.io/MolPad/reference/transpose_dataframe__.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transpose dataframe — transpose_dataframe__","text":"expected input data frame first column serves Time, subsequent columns contain observations various features.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/transpose_dataframe__.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transpose dataframe — transpose_dataframe__","text":"","code":"a <- data.frame(\"Day\"=c(\"Day1\",\"Day2\",\"Day3\"),\"feature_1\" =c(1,2,3),\"feature_2\" =c(0,4,1),\"feature_3\" =c(1,1,0)) a #> Day feature_1 feature_2 feature_3 #> 1 Day1 1 0 1 #> 2 Day2 2 4 1 #> 3 Day3 3 1 0 transpose_dataframe__(a) #> Day1 Day2 Day3 #> 1 1 2 3 #> 2 0 4 1 #> 3 1 1 0"}]
+[{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"CC0 1.0 Universal","title":"CC0 1.0 Universal","text":"CREATIVE COMMONS CORPORATION LAW FIRM PROVIDE LEGAL SERVICES. DISTRIBUTION DOCUMENT CREATE ATTORNEY-CLIENT RELATIONSHIP. CREATIVE COMMONS PROVIDES INFORMATION “-” BASIS. CREATIVE COMMONS MAKES WARRANTIES REGARDING USE DOCUMENT INFORMATION WORKS PROVIDED HEREUNDER, DISCLAIMS LIABILITY DAMAGES RESULTING USE DOCUMENT INFORMATION WORKS PROVIDED HEREUNDER.","code":""},{"path":"https://kaiyanm.github.io/MolPad/LICENSE.html","id":"statement-of-purpose","dir":"","previous_headings":"","what":"Statement of Purpose","title":"CC0 1.0 Universal","text":"laws jurisdictions throughout world automatically confer exclusive Copyright Related Rights (defined ) upon creator subsequent owner(s) (, “owner”) original work authorship /database (, “Work”). 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Affirmer disclaims responsibility clearing rights persons may apply Work use thereof, including without limitation person’s Copyright Related Rights Work. , Affirmer disclaims responsibility obtaining necessary consents, permissions rights required use Work. Affirmer understands acknowledges Creative Commons party document duty obligation respect CC0 use Work.","code":""},{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/articles/FAQ.html","id":"how-can-i-tell-if-my-dataset-is-suitable-for-using-this-dashboard","dir":"Articles","previous_headings":"Common Questions:","what":"How can I tell if my dataset is suitable for using this dashboard?","title":"FAQs","text":"lot longitudinal data least two descriptive columns (like types), match minimal needs starting dashboard. addition, data specific web-ID columns, MolPad make convenient createing links databases automatically.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/FAQ.html","id":"how-can-i-customize-my-dashboard","dir":"Articles","previous_headings":"Common Questions:","what":"How can I customize my dashboard?","title":"FAQs","text":"can edit title within gDashboard() function. additional personalization, adjusting colors sizes figures, can fork repository, create copy, make modifications dashboard.css file.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/FAQ.html","id":"what-should-i-do-if-i-only-have-a-few-time-points5","dir":"Articles","previous_headings":"Common Questions:","what":"What should I do if I only have a few time points(<5)?","title":"FAQs","text":"Firstly, strongly recommend extending length longitudinal samples. can achieve increasing number measurements two days incorporating simulated data. options don’t suit needs, can also consider using “linear” method gNetwork() function.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Case Study: Cheese Communities","text":"vignette provides comprehensive guide using MolPad case study, including data pre-processing, network generation, result analysis.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"washed-rind-cheese-microbial-communities","dir":"Articles","previous_headings":"","what":"Washed-Rind Cheese Microbial Communities","title":"Case Study: Cheese Communities","text":"Cheese making ancient craft involves coagulation milk proteins form curds, separated liquid whey. curds processed, shaped, aged develop desired texture flavor. Various factors, type milk, specific cultures bacteria, aging conditions, contribute unique characteristics cheese. One crucial step process aging practice, regular washing brine solution plays significant role. process producing cheese, regular washing brine solution aging practice can homogenize microbial communities cheese’ surface facilitate intermicrobial interactions. following parts, analyze longitudinal data set three washed-rind cheese communities collected cheese ripening.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"data","dir":"Articles","previous_headings":"","what":"Data","title":"Case Study: Cheese Communities","text":"analysis, use data set contained study Smith et al. (2022) microbial communities cheese. original research investigated successional dynamics occur within cheese rind microbial communities using combination 16S rRNA amplicon, Illumina, PacBio sequencing. functionally taxonomically annotate (using eggNOG (21) MMseqs2 (22)) contigs generated Illumina reads, demonstrate utility MolPad using single-omic. Specifically, focus Cheese Sample Cheese Sample C. detailed information attached data, please check documentation.","code":"data(\"cheese\") str(cheese) #> tibble [106,239 × 18] (S3: tbl_df/tbl/data.frame) #> $ ID : chr [1:106239] \"1\" \"2\" \"3\" \"4\" ... #> $ A_1 : int [1:106239] 38 23 24 3 58 12 1 14 1 3 ... #> $ A_2 : int [1:106239] 23 6 5 2 14 9 1 7 1 1 ... #> $ A_3 : int [1:106239] 27 4 37 4 45 14 0 14 3 5 ... #> $ A_4 : int [1:106239] 5 0 10 2 13 4 1 4 4 0 ... #> $ A_5 : int [1:106239] 11 9 19 16 32 13 0 4 1 1 ... #> $ C_1 : int [1:106239] 13 21 3 56 82 2 4 17 7 2 ... #> $ C_3 : int [1:106239] 1 1 0 1 3 0 0 0 0 1 ... #> $ C_4 : int [1:106239] 3 0 1 7 8 1 0 1 2 2 ... #> $ C_5 : int [1:106239] 0 2 4 17 3 4 0 1 8 6 ... #> $ GO_ID : chr [1:106239] NA NA NA NA ... #> $ KEGG_ID: chr [1:106239] NA NA NA NA ... #> $ domain : chr [1:106239] NA NA NA NA ... #> $ phylum : chr [1:106239] NA NA NA NA ... #> $ class : chr [1:106239] NA NA NA NA ... #> $ order : chr [1:106239] NA NA NA NA ... #> $ family : chr [1:106239] NA NA NA NA ... #> $ genus : chr [1:106239] NA NA NA NA ... str(annotations) #> tibble [86,156 × 9] (S3: tbl_df/tbl/data.frame) #> $ ID : chr [1:86156] \"9\" \"10\" \"11\" \"12\" ... #> $ GO_ID : chr [1:86156] \"-\" \"-\" \"-\" \"-\" ... #> $ KEGG_ID: chr [1:86156] \"-\" \"-\" \"-\" \"-\" ... #> $ domain : chr [1:86156] \"Bacteria\" \"Bacteria\" \"Bacteria\" \"Bacteria\" ... #> $ phylum : chr [1:86156] \"Pseudomonadota\" \"Pseudomonadota\" \"Pseudomonadota\" \"Pseudomonadota\" ... #> $ class : chr [1:86156] \"Alphaproteobacteria\" \"Alphaproteobacteria\" \"Alphaproteobacteria\" \"Alphaproteobacteria\" ... #> $ order : chr [1:86156] \"Caulobacterales\" \"Caulobacterales\" \"Hyphomicrobiales\" \"Hyphomicrobiales\" ... #> $ family : chr [1:86156] \"Caulobacteraceae\" \"Caulobacteraceae\" \"Bartonellaceae\" \"Brucellaceae;-_Brucella/Ochrobactrum group\" ... #> $ genus : chr [1:86156] \"Caulobacter\" \"Caulobacter;-_unclassified Caulobacter\" \"Bartonella\" \"Brucella\" ..."},{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"data-and-annotations","dir":"Articles","previous_headings":"Data > Pre-process","what":"Data and Annotations","title":"Case Study: Cheese Communities","text":"select ‘type’ column phylum describe characteristic cheese data. Also, columns phylum class taken tags elemental composition. section, introduce data preparing steps analysis. annotations dataset contains various columns describe characteristics properties samples. First, select ‘type’ column phylum provide broad categorization microbial communities present cheese surface. categorization helps understanding overall composition diversity microbes high taxonomic level. run pre_process() function clean standardize data. annotate dataset, also use columns phylum class tags elemental composition microbial communities. phylum column represents major taxonomic rank, giving us broad overview microbial distribution. class column provides detailed information, allowing us delve deeper specific types microbes present. pre-processing, two datasets put dashboard look like:","code":"cheesedata <- cheese |> select(ID, A_1:C_5, phylum) |> rename(type=phylum) |> pre_process() pathchee <- gAnnotation(annotations,\"phylum\",\"class\") # data cheesedata[112:115,] #> # A tibble: 4 × 11 #> ID A_1 A_2 A_3 A_4 A_5 C_1 C_3 C_4 C_5 type #> #> 1 112 0.943 -0.471 -0.471 -0.471 -0.471 2.36 -0.471 -0.471 -0.471 Other #> 2 113 0.786 -0.124 0.126 -0.623 -0.637 2.33 -0.667 -0.593 -0.593 Other #> 3 114 1.22 -1.09 -0.430 -0.829 -0.170 1.43 -1.30 0.455 0.715 Other #> 4 115 2.67 -0.333 -0.333 -0.333 -0.333 -0.333 -0.333 -0.333 -0.333 Other # annotation pathchee[112:115,] #> # A tibble: 4 × 9 #> ID GO_ID KEGG_ID domain Pathway taxonomic.scope order family genus #> #> 1 145 - ko:K00004 Bacte… Pseudo… Unknown NA NA NA #> 2 147 - ko:K00004 Bacte… Actino… Actinomycetes NA NA NA #> 3 148 - ko:K00004 Bacte… Bacill… Bacilli Baci… Bacil… Lysi… #> 4 149 - ko:K00004,ko:K0… Bacte… Actino… Actinomycetes NA NA NA"},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"cluster-input","dir":"Articles","previous_headings":"Data","what":"Cluster Input","title":"Case Study: Cheese Communities","text":"section, generate clusters first dataset using gClusters function. function takes cheese dataset (cheesedata) input generates clusters based specified parameters. , set number clusters 10 (ncluster = 10) specify maximum number clusters consider determining optimal number clusters (elbow.max=15).","code":"cluschee <- gClusters(cheesedata,ncluster = 10,elbow.max=15)"},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"network-input","dir":"Articles","previous_headings":"Data","what":"network input","title":"Case Study: Cheese Communities","text":"generating clusters major patterns, proceed obtain network results clusters. Taking cluster centroids nodes, prediction process edges divided individual regression tasks, cluster centroid independentally predicted expression cluster centroids, using random forests. pick top 3 related predictors cluster centroid save network output future use. achieved using gNetwork() function. gain insight network results, can visualize details using gNetwork_view() function, shown .","code":"networkchee <- gNetwork(cluschee,ntop = 3) gNetwork_view(networkchee)"},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"run-dashboard","dir":"Articles","previous_headings":"","what":"Run Dashboard","title":"Case Study: Cheese Communities","text":"clusters network results obtained, can proceed run dashboard. involves declaring annotations executing dashboard using gDashboard() function. , pass cheese dataset (cheesedata), cluster results (cluschee), network results (networkchee), specify column names types annotation identifiers.","code":"gDashboard(cheesedata, cluschee, pathchee, networkchee, id_colname = c(\"GO_ID\",\"KEGG_ID\"), id_type = c(\"GO\",\"KEGG\"))"},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"results","dir":"Articles","previous_headings":"","what":"Results","title":"Case Study: Cheese Communities","text":"example, filter Actinomycetota (Actinobacteria) functional group, see edges connecting group 10 group 3- clusters features associated Actinomycetoa Cheese sample (Fig 1.3.). Fig 1: Dashboard showing Actinomycetota filtered network () enrichment pattern Cheese Sample-(B) Cheese Sample-C (C); Cluster pattern Group 9, also enriched Type IV secretion genes (D). Looking pattern traces groups, (Fig 1.3.B), peak samples A4 (week 9) A5 (week 13), mirrors 16S rRNA results Saak et al. Since two clusters edges connecting groups, suggests Actinomycetoa features found groups follow distinct longitudinal succession patterns independent. looking Actinomycetoa within Cheese Sample C see different pattern. Groups 2 5, features associated Actinomycetoa, highly connected groups (Fig 1.3.). results, can hypothesize though Actinomycetoa features abundant later time points cheese samples, dynamics differentially influenced. authors found Type VI secretion enriched Pseudomonadota bacteria (specifically, Psychrobacter), hypothesized enrichment due importance physical species interactions occur habitat. Using MolPad, searched secretion systems associated genes, understand dynamics within community. Focusing KEGG annotated Type IV secretion genes, found Group 9 contained 12/13 genes. Within group, features cluster ones peak Cheese sample C5 (week 13, Fig 1.3.D). also taxonomically diverse sample. , can hypothesize increased taxonomic diversity also associated increases genes related competitive species interactions.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"installation","dir":"Articles","previous_headings":"","what":"Installation","title":"Get started","text":"can either install MolPad devtools, clone repository local computer (example, onto ./Github) installing: , load package:","code":"# Install the package in R: install.packages(\"devtools\") library(devtools) install_github(\"KaiyanM/MolPad\") # Download through Github and install the package in R: setwd(\"./GitHub\") install(\"MolPad\") library(MolPad)"},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"before-input","dir":"Articles","previous_headings":"","what":"Before Input","title":"Get started","text":"recommend carefully reviewing data considering applying normalization (standardization, quantile normalization, log transformation, …) imputation (KNN, PMM, Mean/Median/Mode, …) address issues related library size missing data beforehead, otherwise pre_processing() function default converting NAs 0s.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"tips-for-normalization-and-imputation","dir":"Articles","previous_headings":"Before Input","what":"Tips for normalization and imputation","title":"Get started","text":"choose normalization method, first consider data distribution. Use Z-score normalization data normally distributed, log transformation robust scaling ’s skewed. analysis requires data within specific range, like [0, 1], opt Min-Max normalization. datasets outliers, robust scaling rank normalization better choices. selecting imputation method, assess amount pattern missing data. small amount missing data, simple methods like mean, median, mode imputation effective. missing data related variables, consider sophisticated approaches like Predictive Mean Matching k-NN imputation. computational resources concern, simpler imputation methods might practical, resources allow, advanced methods can improve data quality. ’s hard decide, can always experiment different methods use cross-validation compare effectiveness. Let choice guided domain knowledge specific requirements analysis increase quality following steps.","code":""},{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"multi-omics-yes-or-no","dir":"Articles","previous_headings":"Data Format","what":"Multi-omics: Yes or No","title":"Get started","text":"visualization pipeline starts pre-processing module includes built-functions reshape data inputs standard format streamline data preparation. Depending nature datasets, two primary conditions consider: research object includes multi-omics measurements, assume provide list dataframes, collected different omics type. example, might datasets peptides, metabolites, lipids. combine together standard long dataset row represents feature, simply run pre_process() function, conduct data preparation yield standard input format demonstrated . Datasets multi-omics, can still utilize dashboard long ensuring data inputs reformatted standard longitudinal format. situation, can manually assign type column category label describe major groups data, input data pre_processing() module data preparation . case study, utilized “Kingdom” type label column cheese data. datasets multi-omics, can simply apply dashboard reformatting data standard longitudinal format. case, manually assign category label type column represent major groups. can still input data pre_processing() module cleaning preparation. case study, used “Kingdom” type label cheese datasets.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"data-format-and-pre-process","dir":"Articles","previous_headings":"Data Format","what":"Data Format and Pre-process","title":"Get started","text":"figure, , B, C can represent datasets different sources different aspects measurements. two examples input data might look like transform required format pre_process() modules.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"if-your-input-is-a-list-of-datasets","dir":"Articles","previous_headings":"Data Format > Data Format and Pre-process","what":"If your input is a list of datasets:","title":"Get started","text":"list datasets, following format: ID column identical column names. pre_process():","code":"str(fungal_dlist) #> List of 3 #> $ :'data.frame': 149786 obs. of 12 variables: #> ..$ ID : chr [1:149786] \"grp1_1\" \"grp1_2\" \"grp1_6\" \"grp1_7\" ... #> ..$ D0_01 : num [1:149786] -0.33 -0.33 1.591 1.106 0.659 ... #> ..$ D1_02 : num [1:149786] 0.628 0.628 3.087 3.618 2.843 ... #> ..$ D2_03 : num [1:149786] -1.223 -1.223 0.846 1.178 1.435 ... #> ..$ D3_04 : num [1:149786] -2.411 -1.808 1.239 1.753 0.258 ... #> ..$ D4_05 : num [1:149786] -1.41 -1.18 2.05 1.64 1.21 ... #> ..$ D5_06 : num [1:149786] -2.245 -2.309 0.83 1.64 0.395 ... #> ..$ D6_07 : num [1:149786] -0.784 -1.125 2.758 2.888 2.516 ... #> ..$ D7_08 : num [1:149786] -2.61197 -2.68724 0.42236 0.92846 0.00892 ... #> ..$ D8_09 : num [1:149786] -0.951 -1.3 2.417 2.454 1.418 ... #> ..$ D9_10 : num [1:149786] -1.91 -2.61 1.33 1.8 2.25 ... #> ..$ D10_11: num [1:149786] -2.871 -3.417 1.101 0.619 1.275 ... #> $ :'data.frame': 150 obs. of 12 variables: #> ..$ ID : chr [1:150] \"grp2_1\" \"grp2_2\" \"grp2_3\" \"grp2_4\" ... #> ..$ D0_01 : num [1:150] 0.947 1.012 0.156 0.31 0.138 ... #> ..$ D1_02 : num [1:150] -1.616 -0.917 -1.581 -2.717 -2.578 ... #> ..$ D2_03 : num [1:150] -2.4 -1.67 -2.31 -3.02 -3.66 ... #> ..$ D3_04 : num [1:150] -0.5734 0.0336 -0.351 -1.3052 -1.737 ... #> ..$ D4_05 : num [1:150] 0.662 0.854 1.002 0.12 -0.371 ... #> ..$ D5_06 : num [1:150] -1.09 -1.28 -1.04 -1.82 -3.15 ... #> ..$ D6_07 : num [1:150] -0.212 0.718 0.189 -0.627 -1.081 ... #> ..$ D7_08 : num [1:150] 0.969 2.117 1.462 0.339 -0.038 ... #> ..$ D8_09 : num [1:150] -0.712 0.43 -0.281 -1.297 -1.822 ... #> ..$ D9_10 : num [1:150] -0.649 0.45 -0.124 -1.77 -1.962 ... #> ..$ D10_11: num [1:150] -1.05 -0.85 -1.58 -1.91 -4.05 ... #> $ :'data.frame': 369 obs. of 12 variables: #> ..$ ID : chr [1:369] \"grp3_1\" \"grp3_2\" \"grp3_3\" \"grp3_4\" ... #> ..$ D0_01 : num [1:369] -0.918 -2.112 -1.372 -0.4 -1.75 ... #> ..$ D1_02 : num [1:369] -1.219 -3.502 -1.504 -0.615 -2.11 ... #> ..$ D2_03 : num [1:369] -0.542 -2.324 -0.592 0.161 -1.342 ... #> ..$ D3_04 : num [1:369] -0.101 -1.495 0.1 0.603 -1.058 ... #> ..$ D4_05 : num [1:369] 1.715 1.557 1.89 2.296 0.653 ... #> ..$ D5_06 : num [1:369] -1.452 -2.37 -1.182 -0.846 -2.531 ... #> ..$ D6_07 : num [1:369] 0.0879 -0.9982 0.4382 0.6215 -0.9464 ... #> ..$ D7_08 : num [1:369] 0.0502 0.4668 0.5378 0.8353 -0.9213 ... #> ..$ D8_09 : num [1:369] -0.0268 -0.4226 0.4528 0.6328 -1.0998 ... #> ..$ D9_10 : num [1:369] -0.5807 -1.7755 -0.1956 0.0866 -1.5876 ... #> ..$ D10_11: num [1:369] 0.118 -0.16 0.687 0.572 -1.037 ... #> ID D0_01 D1_02 D2_03 D3_04 D4_05 D5_06 #> 1 grp1_1 -0.3297741 0.6284682 -1.2232968 -2.411339 -1.413062 -2.2445487 #> 2 grp1_2 -0.3297741 0.6284682 -1.2232968 -1.807533 -1.183498 -2.3088022 #> 3 grp1_6 1.5908054 3.0871284 0.8460835 1.239411 2.054109 0.8300876 #> 4 grp1_7 1.1061795 3.6176250 1.1781012 1.753078 1.643661 1.6400102 #> D6_07 D7_08 D8_09 D9_10 D10_11 #> 1 -0.7839412 -2.6119663 -0.9511467 -1.914273 -2.8711810 #> 2 -1.1251434 -2.6872417 -1.2996614 -2.610647 -3.4165861 #> 3 2.7576288 0.4223618 2.4165602 1.333387 1.1012825 #> 4 2.8877343 0.9284640 2.4538561 1.799019 0.6187648 #> ID D0_01 D1_02 D2_03 D3_04 D4_05 D5_06 #> 1 grp2_1 0.9467431 -1.6162900 -2.400394 -0.57337845 0.6616711 -1.090914 #> 2 grp2_2 1.0120048 -0.9165994 -1.674330 0.03361378 0.8540950 -1.283964 #> 3 grp2_3 0.1561394 -1.5805490 -2.307281 -0.35095093 1.0018741 -1.035030 #> 4 grp2_4 0.3104578 -2.7173471 -3.022886 -1.30521496 0.1198075 -1.816677 #> D6_07 D7_08 D8_09 D9_10 D10_11 #> 1 -0.2116778 0.9688582 -0.7123773 -0.6489204 -1.0504845 #> 2 0.7181970 2.1174460 0.4302981 0.4504066 -0.8503481 #> 3 0.1885422 1.4619677 -0.2805926 -0.1235090 -1.5757556 #> 4 -0.6271954 0.3393109 -1.2967938 -1.7697248 -1.9073101 #> ID D0_01 D1_02 D2_03 D3_04 D4_05 D5_06 #> 1 grp3_1 -0.9178218 -1.2193128 -0.5415780 -0.1007493 1.715495 -1.4515238 #> 2 grp3_2 -2.1120636 -3.5015326 -2.3236475 -1.4949366 1.557463 -2.3698578 #> 3 grp3_3 -1.3724446 -1.5043101 -0.5922380 0.1004978 1.889573 -1.1815469 #> 4 grp3_4 -0.4002445 -0.6145252 0.1613003 0.6031744 2.295751 -0.8464012 #> D6_07 D7_08 D8_09 D9_10 D10_11 #> 1 0.08792489 0.05020749 -0.02681783 -0.58065562 0.1177374 #> 2 -0.99822332 0.46681389 -0.42255125 -1.77554842 -0.1603138 #> 3 0.43822354 0.53780990 0.45276561 -0.19558682 0.6870272 #> 4 0.62145080 0.83528878 0.63275758 0.08663959 0.5720591 data <- pre_process(fungal_dlist, typenameList = c('DataSource_A','DataSource_B','DataSource_C'), replaceNA = TRUE, scale = TRUE, autoColName = \"Section_\") #> [1] \"Reformat a list of datasets:\" head(data,10) #> ID D0_01 D1_02 D2_03 D3_04 D4_05 D5_06 #> 1 grp1_1 1.06146675 1.956656 0.2267383 -0.88312967 0.04945980 -0.7273146 #> 2 grp1_2 1.08465667 1.916983 0.3085454 -0.19892034 0.34311497 -0.6343214 #> 3 grp1_6 -0.01899274 1.717871 -0.8834318 -0.42687491 0.51878884 -0.9019992 #> 4 grp1_7 -0.75869657 2.051470 -0.6782202 -0.03485388 -0.15728499 -0.1613700 #> 5 grp1_8 -0.68132827 1.648062 0.1465161 -1.10875822 -0.09101615 -0.9629427 #> 6 grp1_11 0.27709905 1.981042 -0.5786717 0.02888008 0.54647685 -0.7013406 #> 7 grp1_12 0.90487968 2.180079 -0.6439891 -0.14841544 -0.69040101 -0.3834938 #> 8 grp1_13 0.46335031 1.695812 -1.0938459 -0.76868293 0.74415950 -0.6861036 #> 9 grp1_14 0.72014702 1.708604 -0.9344900 -0.35796963 0.63730277 -0.9872778 #> 10 grp1_15 0.54526798 1.784716 -0.8846753 -0.49538649 0.62916563 -0.9742293 #> D6_07 D7_08 D8_09 D9_10 D10_11 type #> 1 0.6371841 -1.0705560 0.4809808 -0.41877137 -1.31271417 DataSource_A #> 2 0.3938012 -0.9630327 0.2422154 -0.89650312 -1.59653914 DataSource_A #> 3 1.3354026 -1.3752687 0.9395058 -0.31779210 -0.58720856 DataSource_A #> 4 1.2347631 -0.9575502 0.7492778 0.01655139 -1.30408627 DataSource_A #> 5 1.2995756 -1.3741657 0.1285139 1.01932257 -0.02377948 DataSource_A #> 6 1.1406352 -1.6339536 0.2041455 -0.45793129 -0.80638109 DataSource_A #> 7 0.4794959 -1.6340692 0.5360766 -0.31430867 -0.28585374 DataSource_A #> 8 1.2235141 -1.2241931 0.6790383 -0.28487806 -0.74817082 DataSource_A #> 9 1.1466328 -1.4265286 0.5357111 -0.51799361 -0.52413804 DataSource_A #> 10 1.1813273 -1.2944121 0.6417621 -0.46906150 -0.66447461 DataSource_A"},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"if-your-input-is-a-single-dataset","dir":"Articles","previous_headings":"Data Format > Data Format and Pre-process","what":"If your input is a single dataset:","title":"Get started","text":"dataset already includes labeled ‘type’ column: pre_process():","code":"#> ID Day_1 Day_2 Day_3 Day_4 Day_5 Day_6 #> 1 1 NA NA -0.82919412 0.08237778 1.30438699 0.4875914 #> 2 2 NA NA -0.41606256 1.42523475 -0.44792066 1.1425154 #> 3 3 1.0354708 NA 0.78841026 -1.49585251 -0.41361867 -0.3271995 #> 4 4 -0.3005323 0.6998150 -1.86018783 1.14167433 -1.04204735 -2.4930409 #> 5 5 -0.1307903 0.7839983 0.25834857 0.62421147 -0.07202124 -0.9214203 #> 6 6 -0.9910321 1.8059465 -1.25395397 -0.45260649 -0.57303842 0.9990113 #> 7 7 -0.9160734 1.4475673 -3.10452531 -0.06560360 -1.27374744 -0.5703149 #> 8 8 -0.7381729 -0.4844302 1.10281675 0.12049188 0.20892638 -0.3115689 #> 9 9 -0.2581716 -0.5906591 0.09794986 2.44249606 1.13135657 0.4886958 #> 10 10 1.8831483 -1.2490571 0.55981559 -2.31731640 0.97323395 -0.7994677 #> Day_7 Day_8 type #> 1 -1.4632070 0.05832042 peptide #> 2 -0.4292379 -0.79047610 peptide #> 3 -0.3508094 -0.34995898 peptide #> 4 -0.3011731 0.67869535 peptide #> 5 1.1761504 -0.86303628 peptide #> 6 -0.8993647 -0.44801227 peptide #> 7 1.1593812 -0.90149427 peptide #> 8 0.5463595 -1.51687039 lipid #> 9 -0.5016034 -0.08241911 lipid #> 10 -0.9442708 NA metabolite x1 <- pre_process(x, replaceNA = TRUE,scale = TRUE) head(x1,10) #> ID Day_1 Day_2 Day_3 Day_4 Day_5 Day_6 #> 1 1 0.05451890 0.05451890 -0.9508450 0.1543986 1.6360347 0.64570353 #> 2 2 -0.07554744 -0.07554744 -0.5950354 1.7039742 -0.6348129 1.35097605 #> 3 3 1.49874204 0.17759695 1.1835204 -1.7309438 -0.3501343 -0.23987305 #> 4 4 0.10376561 0.87801613 -1.1033792 1.2200072 -0.4701534 -1.59319589 #> 5 5 -0.31530188 0.89803359 0.2008348 0.6860993 -0.2373532 -1.36395879 #> 6 6 -0.71746086 1.90776216 -0.9642373 -0.2120986 -0.3251351 1.15037885 #> 7 7 -0.27002806 1.37506283 -1.7931878 0.3218978 -0.5189688 -0.02938062 #> 8 8 -0.74469269 -0.43190500 1.5246885 0.3137802 0.4227931 -0.21881953 #> 9 9 -0.58919072 -0.91616395 -0.2389755 2.0666865 0.7772922 0.14528989 #> 10 10 1.56649401 -0.74805373 0.5886154 -1.5374455 0.8941115 -0.41582898 #> Day_7 Day_8 type #> 1 -1.7195596 0.1252300 peptide #> 2 -0.6114859 -1.0625212 peptide #> 3 -0.2699967 -0.2689116 peptide #> 4 0.1032697 0.8616699 peptide #> 5 1.4181669 -1.2865208 peptide #> 6 -0.6314225 -0.2077865 peptide #> 7 1.1744856 -0.2598810 peptide #> 8 0.8387456 -1.7045902 lipid #> 9 -0.8285852 -0.4163531 lipid #> 10 -0.5228315 0.1749388 metabolite"},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"choose-your-annotation","dir":"Articles","previous_headings":"","what":"Choose Your Annotation","title":"Get started","text":"addition specifying data type mentioned , methods support three levels information: functional annotation, taxonomy annotation, feature annotation. annotations matched ID columns annotation data, serving another crucial input generating dashboard. facilitate automatic feature link generation using KeggID GOID, users set corresponding column names beforehand. example annotation formats align data inputs. data two layers labels (like cheese dataset), can specify two layers want focus network using gAnnotation() function.","code":"#> ID taxonomic.scope ko_term Pathway #> 1 grp1_24 Bacteria K02706 00195_Photosynthesis #> 2 grp1_25 Bacteria K02706 00195_Photosynthesis #> 3 grp1_26 Bacteria K02706 00195_Photosynthesis #> 4 grp1_1075 Bacteria K02112 00195_Photosynthesis #> 5 grp1_1076 Bacteria K02112 00195_Photosynthesis #> 6 grp1_1358 Bacteria K02112 00195_Photosynthesis Annotation_path_taxon <- gAnnotation(annotations,\"phylum\",\"class\")"},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"network-generation","dir":"Articles","previous_headings":"","what":"Network Generation","title":"Get started","text":"generate network, first scale cluster trajectories across input molecular features. Trajectories clustered using K-means, built-elbow method used select optimal K. build network clustered trajectories adapting GENIE3 algorithm. Specifically, network learned series regressions, cluster centroid predicted expression patterns cluster centroids using random forests. choose random forests potential model interacting features non-linearity without strong assumptions. determine feature clusters predictive given trajectory, Mean Decrease Accuracy subset top predictors whose expression directly influences expression target cluster taken indication putative link.","code":"cluster_result <- data |> gClusters(ncluster = 5,elbow.max=15) network_result <- cluster_result |> gNetwork(ntop = 3) gNetwork_view(network_result) #> Warning: Removed 1 rows containing missing values (`geom_segment()`). #> Warning: Removed 1 rows containing missing values (`geom_point()`)."},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"dashboard-navigation","dir":"Articles","previous_headings":"","what":"Dashboard Navigation","title":"Get started","text":"workflow inputs related gDashboard function. key function processes data runs dashboard, requiring input previous steps. ’ve launched Shiny dashboard, can zoom make adjustments explore interesting findings within data. effectively navigate dashboard generated MolPad, ’ll follow three main steps:","code":"gDashboard(data, cluster_result, Annotation_path_taxon, network_result, id_colname = \"ko_term\" , id_type = \"KEGG\")"},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"choose-a-primary-functional-annotation-and-adjust-edge-density","dir":"Articles","previous_headings":"Dashboard Navigation","what":"1. Choose a Primary Functional Annotation and Adjust Edge Density","title":"Get started","text":"Start selecting primary functional annotation available options. , fine-tune edge density adjusting threshold value importance score. Nodes turn bright green indicate clusters containing features related chosen functional annotation.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"explore-the-network","dir":"Articles","previous_headings":"Dashboard Navigation","what":"2. Explore the Network","title":"Get started","text":"Brushing network unveils patterns taxonomic composition typical trajectories. can also zoom specific taxonomic annotations applying filters.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"investigate-feature-details-and-related-function-annotations","dir":"Articles","previous_headings":"Dashboard Navigation","what":"3. Investigate Feature Details and Related Function Annotations","title":"Get started","text":"Delve feature table examine specifics features within selected clusters. Explore additional related function annotations using drop-options. Click provided links access online information items interest. interface encourages iterative exploration, enabling conduct multiple steps answer specific questions, comparing pattern distributions two functions identifying functionally important community members metabolizing feature interest.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"related-visualization-outputs","dir":"Articles","previous_headings":"","what":"Related Visualization Outputs","title":"Get started","text":"following visualizations integrated dashboard, ’s need run function plot individually. ’ve linked stacked bar plot ribbon plot network facilitate focus context explorations. setup helps detecting similar longitudinal patterns understanding relationships.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"network-plot","dir":"Articles","previous_headings":"Related Visualization Outputs","what":"Network Plot","title":"Get started","text":"network plot powerful visualization tool displays relationships different groups features within data. MolPad, network plot helps identify clusters features share similar patterns, revealing underlying connections might immediately obvious. visualizing connections, users can gain clearer understanding structure within data, making easier pinpoint significant associations trends.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"stack-bar-plot","dir":"Articles","previous_headings":"Related Visualization Outputs","what":"Stack Bar Plot","title":"Get started","text":"stacked bar plot provides detailed view composition cluster group dataset. stacking different categories top within single bar, plot allows quick comparison relative proportions across multiple groups. particularly useful microbiome experiments understanding distribution taxa across different conditions time points crucial. stacked bar plot makes easy see distributions change experimental conditions, facilitating deeper insights.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"ribbon-plot","dir":"Articles","previous_headings":"Related Visualization Outputs","what":"Ribbon Plot","title":"Get started","text":"ribbon plot designed visualize changes time, making ideal tool tracking longitudinal data. MolPad, ribbon plot illustrates abundance expression levels features vary across different time points conditions. smooth, flowing design ribbons helps emphasize trends patterns, enabling users quickly identify periods significant change stability. plot particularly advantageous comparing multiple groups, clearly shows overlapping trends divergences, providing comprehensive view temporal dynamics data.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/whymolpad.html","id":"why-molpad","dir":"Articles","previous_headings":"","what":"Why MolPad?","title":"Why MolPad?","text":"increasing multi-omics data longitudinal designs integrated microbiome experiments, growing need present network, especially complex variations across biological modalities. Network perspective helps detect underlying co-occurrence among microbiome samples, allowing high-level insights global structure. Yet comes experimental data records time series 100,000 features, network collapse entangled clumps therefore unable read.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/whymolpad.html","id":"improvements","dir":"Articles","previous_headings":"","what":"Improvements","title":"Why MolPad?","text":"aim network interpretation, MolPad shows improvements 3 important aspects: Capture groups features share longitudinal patterns functional properties within network. Annotate network complex experimental datasets, way dig specific components notable features. Streamlines exploration modularized functions interactive dashboard. package especially facilitates exploration longitudinal molecular co-expression microbiome experiments using focus-plus-context approach, empowering researchers uncover global patterns insights longitudinal microbiomics data. Unlike existing tools, MolPad stands rich network interactivity support contextualization. also equipped beginner-friendly interface self-contained data processing functions, making accessible users limited programming experience.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/whymolpad.html","id":"key-features","dir":"Articles","previous_headings":"","what":"Key Features","title":"Why MolPad?","text":"Trajectory clustering organizing data clusters, helps identify patterns, relationships, underlying structures within dataset. essence, clustering detects groups features observations behave similarly, allowing deeper insights effective decision-making. Network extracting build network clustered trajectories adapting GENIE3 algorithm. Specifically, network learned series regressions, cluster centroid predicted expression patterns cluster centroids using random forests. choose random forests potential model interacting features non-linearity without strong assumptions. Interactive Dashboard package provides interactive dashboard designed help users explore data uncover relationships within complex networks. utilizing focus-plus-context visualization, dashboard allows users zoom specific details keeping broader dataset view. approach enables deeper understanding data, making easier identify patterns make informed decisions.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/whymolpad.html","id":"what-does-it-look-like","dir":"Articles","previous_headings":"","what":"What does it look like?","title":"Why MolPad?","text":"Now, let’s see can get dashboard. ’ll start overview demonstrate discover patterns within data.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/whymolpad.html","id":"molpad-dashboard-overview","dir":"Articles","previous_headings":"What does it look like?","what":"MolPad Dashboard Overview","title":"Why MolPad?","text":"overview MolPad Dashboard. explore dashboard effectively, can start following sequence -B-C-D. approach guide cluster-level network, taxonomic-level bar plot, type-level line plot, feature-level table. , can modify detailed settings identify investigate special patterns within data.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/whymolpad.html","id":"discovering-related-patterns","dir":"Articles","previous_headings":"What does it look like?","what":"Discovering related patterns","title":"Why MolPad?","text":"short example discovering related patterns using network plot: shade edges represents proximity nodes. brushed area, Groups 1-7-8 (circled solid black lines) Groups 1-2 (circled blue dashed lines) strongly linked. Groups 1, 7, 8, patterns form W-shape noticeable peak time section. Although Group 1 shows higher volatility compared Group 2, groups display highly overlapping increasing trend. detailed instructions, please visit “Get Started” page relevant function reference page.","code":""},{"path":"https://kaiyanm.github.io/MolPad/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Kaiyan Ma. Author, maintainer.","code":""},{"path":"https://kaiyanm.github.io/MolPad/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Ma K (2024). MolPad: MolPad: R-Shiny Package Cluster Co-Expression Analysis Longitudinal Microbiomics. R package version 0.1.0, https://kaiyanm.github.io/MolPad/.","code":"@Manual{, title = {MolPad: MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics}, author = {Kaiyan Ma}, year = {2024}, note = {R package version 0.1.0}, url = {https://kaiyanm.github.io/MolPad/}, }"},{"path":"https://kaiyanm.github.io/MolPad/index.html","id":"molpad-","dir":"","previous_headings":"","what":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","title":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","text":"R-Shiny Package Cluster Co-Expression Analysis Longitudinal Microbiomics","code":""},{"path":"https://kaiyanm.github.io/MolPad/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","text":"MolPad offers visualization dashboard tool designed enhance understanding molecular co-expression works context microbiome data. approach involves using cluster network provide initial overview relationships across multiple omics, added functionality interactively zoom specific areas interest. facilitate analysis, ’ve developed focus-plus-context strategy connects online curated annotations. Additionally, package simplifies entire pipeline creating dashboard. user-friendly design makes accessible even people limited R programming experience. Check cheese-data demo .","code":""},{"path":"https://kaiyanm.github.io/MolPad/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","text":"can either install devtools, {r, eval = FALSE} # Install package R: install.packages(\"devtools\") library(devtools) install_github(\"KaiyanM/MolPad\") clone repository local computer (example, onto ./Github) installing: {r, eval = FALSE} # Download install package R: setwd(\"./GitHub\") install(\"MolPad\") , load package: {r,eval=FALSE} library(MolPad)","code":""},{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/index.html","id":"molpad-could-help-you-with","dir":"","previous_headings":"Usage","what":"MolPad could help you with:","title":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","text":"Clustering data k-means building group network. Find significant trend patterns datasets. Target interaction groups, taxons, pathways. Visualize distribution features specific pathways group network. Search particular features user-defined labels. Check detailed information feature automatically generated hyperlinks. better overall understanding datasets.","code":""},{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/index.html","id":"getting-help","dir":"","previous_headings":"","what":"Getting Help","title":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","text":"need assistance MolPad, two primary ways seek help: Ask us anything related MolPad! add question, create issue repository. Stack Overflow another excellent resource answering common issues R. Remember ’s particularly effective can provide reproducible example shows specific problem ’re .","code":""},{"path":"https://kaiyanm.github.io/MolPad/index.html","id":"contribution","dir":"","previous_headings":"","what":"Contribution","title":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","text":"contribute project, use following workflow: fork repository –> create local copy –> submit pull request.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":null,"dir":"Reference","previous_headings":"","what":"Cheese data — cheese","title":"Cheese data — cheese","text":"context cheese production, regular application brine solution maturation technique promotes uniformity microbial populations cheese's surface facilitates interactions among microorganisms. investigation involved analysis longitudinal dataset encompassing three washed-rind cheese communities sampled ripening process.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Cheese data — cheese","text":"two datasets: cheese annotations","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Cheese data — cheese","text":"Reference: doi:10.1128/msystems.00701-22","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Cheese data — cheese","text":"study uncovered remarkably consistent microbial progression within cheese. bacterial community, Firmicutes dominate outset, Proteobacteria swiftly assuming dominance end ripening period. Additionally, Cheese Cheese C consistently demonstrate establishment Actinobacteria Bacteroidetes, distinct manner. corroborate findings using MolPad dashboard, conducted analysis two cheeses (C) three production batches, spanning weeks 2 13.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":"cheese-data","dir":"Reference","previous_headings":"","what":"cheese data","title":"Cheese data — cheese","text":"data frame 106239 rows 18 variables: ID sample ID A_1~A_5 Time series data measured cheese . C_1~C_5 Time series data measured cheese C. domain category feature phylum category feature class category feature order category feature family category feature genus category feature","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":"annotation-data","dir":"Reference","previous_headings":"","what":"annotation data","title":"Cheese data — cheese","text":"data frame 86156 rows 9 variables: ID sample ID GO_ID GO IDs, represents link gene product type molecular function KEGG_ID KEGG IDs, linking genomic information higher order functional information. domain category feature phylum category feature class category feature order category feature family category feature genus category feature","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cheese data — cheese","text":"","code":"data(cheese) head(annotations) #> # A tibble: 6 × 9 #> ID GO_ID KEGG_ID domain phylum class order family genus #> #> 1 9 - - Bacteria Pseudomonadota Alphaproteobac… Caul… Caulo… Caul… #> 2 10 - - Bacteria Pseudomonadota Alphaproteobac… Caul… Caulo… Caul… #> 3 11 - - Bacteria Pseudomonadota Alphaproteobac… Hyph… Barto… Bart… #> 4 12 - - Bacteria Pseudomonadota Alphaproteobac… Hyph… Bruce… Bruc… #> 5 13 - - Bacteria Pseudomonadota Alphaproteobac… Hyph… Nitro… Brad… #> 6 14 - - Bacteria Pseudomonadota Alphaproteobac… Hyph… Phyll… Meso… head(cheese) #> # A tibble: 6 × 18 #> ID A_1 A_2 A_3 A_4 A_5 C_1 C_3 C_4 C_5 GO_ID KEGG_ID #> #> 1 1 38 23 27 5 11 13 1 3 0 NA NA #> 2 2 23 6 4 0 9 21 1 0 2 NA NA #> 3 3 24 5 37 10 19 3 0 1 4 NA NA #> 4 4 3 2 4 2 16 56 1 7 17 NA NA #> 5 5 58 14 45 13 32 82 3 8 3 NA NA #> 6 6 12 9 14 4 13 2 0 1 4 NA NA #> # ℹ 6 more variables: domain , phylum , class , order , #> # family , genus "},{"path":"https://kaiyanm.github.io/MolPad/reference/color_palettes__.html","id":null,"dir":"Reference","previous_headings":"","what":"Color palettes — color_palettes__","title":"Color palettes — color_palettes__","text":"internal function built-color palettes.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/color_palettes__.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Color palettes — color_palettes__","text":"","code":"color_palettes__(name)"},{"path":"https://kaiyanm.github.io/MolPad/reference/color_palettes__.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Color palettes — color_palettes__","text":"name string two options: \"graytone\" \"darkwarm\".","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/color_palettes__.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Color palettes — color_palettes__","text":"","code":"color_palettes__(\"graytone\") #> [1] \"#c3c5c7\" \"#30beba\" \"#998a73\" \"#807566\" \"#a5b1c9\" \"#5d5232\" \"#9a2b41\" #> [8] \"#d6b7a2\" \"#882db4\" \"#b47a53\" color_palettes__(\"darkwarm\") #> [1] \"#251305\" \"#C70A80\" \"#FBCB0A\" \"#ff1122\" \"#7D7463\" \"#CECE5A\" \"#FF9B9B\" #> [8] \"#A459D1\" \"#00235B\" \"#7E1717\""},{"path":"https://kaiyanm.github.io/MolPad/reference/convert_range.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert range — convert_range","title":"Convert range — convert_range","text":"internal function range conversion. element x, returns distance minimal values divided range.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/convert_range.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert range — convert_range","text":"","code":"convert_range(x)"},{"path":"https://kaiyanm.github.io/MolPad/reference/convert_range.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert range — convert_range","text":"x vector numbers.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/convert_range.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert range — convert_range","text":"vector calculated (x - min(x)) / (max(x) - min(x))","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/convert_range.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert range — convert_range","text":"","code":"convert_range(5:10) #> [1] 0.0 0.2 0.4 0.6 0.8 1.0"},{"path":"https://kaiyanm.github.io/MolPad/reference/extend.color__.html","id":null,"dir":"Reference","previous_headings":"","what":"Extend color palettes — extend.color__","title":"Extend color palettes — extend.color__","text":"internal function designed pair vectors color palettes automatically generate colors longer vectors find match.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/extend.color__.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extend color palettes — extend.color__","text":"","code":"extend.color__(n, colors, extendby = 1, alpha = 1, ...)"},{"path":"https://kaiyanm.github.io/MolPad/reference/extend.color__.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extend color palettes — extend.color__","text":"n number. length feature vector want match colors. colors vector colors (finite). extendby number select 1, 2, 3, 4 5, representing distinct auto-fill schemes. alpha number select range 0,1.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gAnnotation.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate processed annotation — gAnnotation","title":"Generate processed annotation — gAnnotation","text":"gAnnotation() provides standard input format dashboard, allowing users select two columns primary factors wish visualize describe data.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gAnnotation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate processed annotation — gAnnotation","text":"","code":"gAnnotation(data, first_order, second_order)"},{"path":"https://kaiyanm.github.io/MolPad/reference/gAnnotation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate processed annotation — gAnnotation","text":"data data.frame containing annotations used describing features measured time point. also required include ID least two categorical variables. first_order string. name one column categorical variable data. second_order string. name another column categorical variable data.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gAnnotation.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate processed annotation — gAnnotation","text":"Guidelines Selecting Annotations: first-order annotation recommended functional, pathway functional system. parameter primarily serve purpose filtering one network time displaying dashboard. second-order annotation utilized illustrate composition first-order annotation using bar plot. Therefore, better set taxon, class label, etc.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gAnnotation.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate processed annotation — gAnnotation","text":"","code":"data(test_data) test_annotations_processed <- gAnnotation(test_annotations,system,class) head(test_annotations_processed) #> ID #> 1 1 #> 2 2 #> 3 3 #> 4 4 #> 5 5 #> 6 6 #> GO_ID #> 1 #> 2 #> 3 GO:0003674,GO:0003824,GO:0005575,GO:0006629,GO:0006644,GO:0006650,GO:0006655,GO:0006793,GO:0006796,GO:0008150,GO:0008152,GO:0008610,GO:0008654,GO:0008808,GO:0009058,GO:0009987,GO:0016020,GO:0016740,GO:0016772,GO:0016780,GO:0019637,GO:0030572,GO:0032048,GO:0032049,GO:0044237,GO:0044238,GO:0044249,GO:0044255,GO:0045017,GO:0046471,GO:0046474,GO:0046486,GO:0071704,GO:0090407,GO:1901576 #> 4 GO:0003674,GO:0003676,GO:0003723,GO:0003729,GO:0003824,GO:0004654,GO:0005488,GO:0005575,GO:0005622,GO:0005623,GO:0005737,GO:0006139,GO:0006401,GO:0006402,GO:0006725,GO:0006807,GO:0008150,GO:0008152,GO:0009056,GO:0009057,GO:0009892,GO:0009987,GO:0010468,GO:0010605,GO:0010629,GO:0016070,GO:0016071,GO:0016740,GO:0016772,GO:0016779,GO:0019222,GO:0019439,GO:0034641,GO:0034655,GO:0043170,GO:0044237,GO:0044238,GO:0044248,GO:0044260,GO:0044265,GO:0044270,GO:0044424,GO:0044464,GO:0046483,GO:0046700,GO:0048519,GO:0050789,GO:0060255,GO:0065007,GO:0071704,GO:0090304,GO:0097159,GO:1901360,GO:1901361,GO:1901363,GO:1901575 #> 5 #> 6 #> KEGG_ID Pathway taxonomic.scope #> 1 K07124 Integumentary System hormonal proteins #> 2 Skeletal System structural proteins #> 3 K06131 Muscular System enzymes #> 4 K00962 Nervous System contractile proteins #> 5 K02083 Endocrine System contractile proteins #> 6 Cardiovascular System structural proteins"},{"path":"https://kaiyanm.github.io/MolPad/reference/gClusters.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate clusters — gClusters","title":"Generate clusters — gClusters","text":"gClusters() returns clusters generated k-means yield elbow plot way finding optimal parameter.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gClusters.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate clusters — gClusters","text":"","code":"gClusters(data, ncluster = 20, elbow.max = 50, ...)"},{"path":"https://kaiyanm.github.io/MolPad/reference/gClusters.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate clusters — gClusters","text":"data scaled data.frame contain variables ID, value time_1, ..., value time_k, type extracting patterns across time. See also pre_process(). ncluster number clusters. related complexity information network: choosing ncluster, suggest thinking many nodes show visualization representative want clustered pattern. elbow.max number maximum value x-axis elbow method plot. larger expected ncluster smaller sample size. iter.max number maximum iterations allowed k-means. See also stats::kmeans. nstart number random attempts generating initial configurations. k-means algorithm choose best one among attempts. larger data, 'nstart' can set lower just set 1. See also stats::kmeans.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gClusters.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate clusters — gClusters","text":"function return list 2 elements: k-means cluster result elbow method plot.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gClusters.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate clusters — gClusters","text":"determine optimal number clusters (ncluster), advised closely examine elbow plot identify point graph substantial change 'elbow' occurs. often indicative suitable cluster count. cases dataset extensive intricate, might consider increasing value elbow.max ensure comprehensive exploration potential cluster counts. can help achieving accurate meaningful results, especially working larger complex datasets. function can executed data parameter outset. However, achieve best clustering results, adjustments recommended. initial run, users expected adjust function's parameters based clustering outcomes elbow plot analysis.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gClusters.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate clusters — gClusters","text":"","code":"data(test_data) reslist <- gClusters(test_data_processed) # k-means result reslist[[1]] #> K-means clustering with 20 clusters of sizes 6, 3, 6, 5, 5, 8, 6, 5, 5, 6, 4, 11, 3, 3, 3, 2, 4, 2, 2, 11 #> #> Cluster means: #> T1 T2 T3 T4 T5 T6 #> 1 2.0919348 -0.33320285 -0.50786563 -0.5579659 -0.5579659 1.5455977 #> 2 0.8277105 1.32036794 -0.09261332 1.2325986 0.4199176 -0.9576634 #> 3 0.5077879 -0.24564283 0.53503708 0.2363183 1.0793829 1.3583848 #> 4 0.2914366 -0.41308350 -0.76541563 -0.9724353 -0.9724353 1.6040887 #> 5 1.2081037 -0.66790409 -0.66790409 -0.9275526 -0.4204221 1.2719890 #> 6 0.1155971 -0.19000668 -0.25629794 -0.1487697 -0.4383348 2.5830148 #> 7 1.2574004 -0.64914997 -0.87502695 0.9353966 0.4760273 1.2681050 #> 8 0.9389212 -0.86278238 0.43887930 1.0009533 1.3733646 -1.0423264 #> 9 -0.8135539 -0.84696982 -0.32573017 1.1218534 2.0955812 -0.5620731 #> 10 -0.3802219 -0.36840199 0.02254271 1.6459653 1.9633618 -0.3246455 #> 11 -0.1225506 -0.44139873 -0.19548572 1.0299038 2.3840629 -0.2147528 #> 12 2.5784883 -0.02446156 -0.60355041 -0.2547486 -0.5120230 -0.3947043 #> 13 0.9922755 0.32399254 -0.94565021 -0.7125240 -0.5104935 -0.9456502 #> 14 0.2699801 -0.08094677 -0.28926312 -0.7813127 -0.6065051 -0.1494523 #> 15 1.7947637 -0.47746004 -0.52783931 -0.7635958 -0.7635958 -0.6438957 #> 16 0.1789247 1.26157166 0.82115649 0.3899368 1.2707671 -0.2614905 #> 17 1.4517382 0.26236463 1.31600581 -0.2531012 0.2004547 -1.1238942 #> 18 -0.2784769 -0.82258408 -0.82258408 -0.8225841 1.2338761 -0.8225841 #> 19 2.1235865 1.05868538 -0.39467611 0.1315587 -0.1315587 0.3946761 #> 20 -0.2262821 -0.73107613 -0.66098092 -0.4323193 -0.4035074 -0.3836848 #> T7 T8 T9 T10 #> 1 -0.425513174 -0.557965933 -0.30571480 -0.391338269 #> 2 -1.160107459 -0.295057401 -0.64757654 -0.647576540 #> 3 -1.006282312 -0.766188425 -0.64803067 -1.050766722 #> 4 0.301678216 -0.371684852 0.66435231 0.633498705 #> 5 1.209152596 -0.095960244 -0.32347149 -0.586030729 #> 6 -0.006327004 -0.327677972 -0.66559889 -0.665598891 #> 7 -0.354972016 -0.601904415 -0.82778139 -0.628094550 #> 8 -1.159101258 -0.345888528 -0.27147562 -0.070544200 #> 9 -0.651292750 -0.498839228 0.42446013 0.056564270 #> 10 -0.647007074 -0.667438225 -0.63229313 -0.611861976 #> 11 -0.400726188 -0.616324716 -0.71136401 -0.711364008 #> 12 -0.526022073 -0.185303954 -0.06989626 -0.007778173 #> 13 -0.945650212 0.121962031 1.19430596 1.427432164 #> 14 -0.763902351 -0.412975440 0.39220455 2.422173070 #> 15 -0.260645101 0.389799714 -0.08327821 1.335746686 #> 16 -0.912917717 -0.481698036 -1.13312530 -1.133125299 #> 17 -0.987867401 -0.340013670 -0.33085684 -0.194830041 #> 18 -0.822584085 1.233876127 1.23387613 0.689768939 #> 19 -0.795567972 -0.795567972 -0.79556797 -0.795567972 #> 20 -0.612869917 -0.006483243 1.70560793 1.751595921 #> #> Clustering vector: #> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 #> 20 1 18 16 3 20 5 19 10 9 8 14 15 12 2 9 6 20 17 12 #> 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 #> 7 20 6 1 12 9 7 12 6 11 5 11 16 18 10 3 8 13 20 2 #> 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 #> 6 1 20 20 7 20 6 3 1 6 15 5 4 13 6 5 4 10 12 10 #> 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 #> 8 3 7 13 3 1 12 7 17 17 14 9 6 19 14 10 2 11 8 7 #> 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 #> 8 20 15 4 4 1 17 12 4 12 10 5 11 12 20 3 9 12 20 12 #> #> Within cluster sum of squares by cluster: #> [1] 3.0260293 5.0357352 13.2343206 13.1118010 10.3695240 8.0412334 #> [7] 11.7251902 8.3255313 3.4617088 2.4693324 1.6353685 13.0645655 #> [13] 2.9416715 3.1842269 5.0413438 2.4669004 9.9418120 0.9921913 #> [19] 0.9833964 12.4042363 #> (between_SS / total_SS = 83.1 %) #> #> Available components: #> #> [1] \"cluster\" \"centers\" \"totss\" \"withinss\" \"tot.withinss\" #> [6] \"betweenss\" \"size\" \"iter\" \"ifault\" # elbow plot reslist[[2]]"},{"path":"https://kaiyanm.github.io/MolPad/reference/gDashboard.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate shiny dashboard — gDashboard","title":"Generate shiny dashboard — gDashboard","text":"outputs g-functions, ready create custom Molpad dashboard. sure specify web ID columns corresponding column names.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gDashboard.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate shiny dashboard — gDashboard","text":"","code":"gDashboard( data, cluster, annotation, networkres, dashboardtitle = \"MolPad Dashboard\", id_colname = NULL, id_type = NULL )"},{"path":"https://kaiyanm.github.io/MolPad/reference/gDashboard.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate shiny dashboard — gDashboard","text":"data output pre_process():scaled data.frame contain variables ID, value time_1, ..., value time_k, type. cluster output gClusters(): list contains result k-means Cluster means, vectors sum squares. annotation output gAnnotation(): data.frame containing annotations describing features. Variables must include ID, Pathway, taxonomic.scope. Note NA permitted 3 variables. networkres output gNetwork(): data.frame 4 variables weight,IncNodePurity,var_names,. dashboardtitle string customized dashboard name. id_colname single string string vector. column names annotation dataset vector contains external database IDs. id_type single string string vector. corresponding database names columns, must chosen \"KEGG\" \"GO\".","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gDashboard.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate shiny dashboard — gDashboard","text":"Please ensure columns containing external database IDs adhere following standards: KEGG ID: Begin 'K' followed 5 digits, example, K05685 K06671. GO ID: Begin 'GO:' followed 7 digits, GO:0003674.\"","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gDashboard.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate shiny dashboard — gDashboard","text":"","code":"if (FALSE) { data(test_data) gDashboard(test_data_processed,test_cluster,test_annotations_processed,test_network,id_colname = c(\"GO_ID\",\"KEGG_ID\"),id_type = c(\"GO\",\"KEGG\")) }"},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate prediction network — gNetwork","title":"Generate prediction network — gNetwork","text":"gNetwork() generates prediction network functional annotation. every feature, features considered independent variables, top predictors selected based %IncMSE.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate prediction network — gNetwork","text":"","code":"gNetwork(clusters, ntop = 10, method = \"randomforest\")"},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate prediction network — gNetwork","text":"clusters list two outputs gClusters(). first element k-means result (See also stats::kmeans), element plot automatically omitted ease directly passing results function. ntop number pick set top n predictors(default: top 10) feature important ones. used construct network among clustered patterns. seed number random seed.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate prediction network — gNetwork","text":"assess relationships clusters using Mean Squared Error (MSE) changes resulting random shuffling. gNetwork()'s output includes edge weights node pairs, essential inputs dashboard.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate prediction network — gNetwork","text":"","code":"data(test_data) networkres <- gNetwork(test_cluster, ntop = 3) head(networkres) #> weight IncNodePurity var_names from #> 1 1.4215243 1.1776012 Group_5 Group_1 #> 2 0.7911266 0.7011049 Group_3 Group_1 #> 3 -1.9891284 0.5811186 Group_2 Group_1 #> 4 5.8098014 1.1107592 Group_4 Group_2 #> 5 5.4000389 0.8744358 Group_5 Group_2 #> 6 1.2492288 1.1816976 Group_1 Group_2 gNetwork_view(networkres)"},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork_view.html","id":null,"dir":"Reference","previous_headings":"","what":"View the feature importance plot — gNetwork_view","title":"View the feature importance plot — gNetwork_view","text":"gNetwork_view() Generate feature importance plot random forest result. plot visually highlights importance individual cluster within dataset, helping identify key factors predictive modeling.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork_view.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"View the feature importance plot — gNetwork_view","text":"","code":"gNetwork_view(data)"},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork_view.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"View the feature importance plot — gNetwork_view","text":"data dataframe; output gNetwork(), including 4 variables: weight, IncNodePurity, var_names . weight edge value, IncNodePurity(%IncMSE) measure predictions result var_names permuted. dependent variable round random forest.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork_view.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"View the feature importance plot — gNetwork_view","text":"","code":"data(test_data) gNetwork_view(test_network) #> Warning: Removed 1 rows containing missing values (`geom_segment()`). #> Warning: Removed 1 rows containing missing values (`geom_point()`)."},{"path":"https://kaiyanm.github.io/MolPad/reference/make_line_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Make line plot — make_line_plot","title":"Make line plot — make_line_plot","text":"Generate line ribbon plot dashboard.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_line_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make line plot — make_line_plot","text":"","code":"make_line_plot(dfgroup_long, selected_groups, selected_taxa)"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_line_plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Make line plot — make_line_plot","text":"function makes ribbon plot every brush action Shiny show min, max, mean value clustered pattern group across time. ribbon plot grouped colored type variable input datasets. See also ggplot2::geom_ribbon.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_line_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Make line plot — make_line_plot","text":"","code":"data(test_data) make_line_plot(test_maindata, \"Group_5\", c(\"hormonal proteins\",\"structural proteins\",\"enzymes\",\"storage proteins\",\"antibodies\",\"transport proteins\"))"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_stackbar_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Make stackbar plot — make_stackbar_plot","title":"Make stackbar plot — make_stackbar_plot","text":"Generate stackbar plot dashboard.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_stackbar_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make stackbar plot — make_stackbar_plot","text":"","code":"make_stackbar_plot(dfgroup_long, selected_groups, selected_taxa)"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_stackbar_plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Make stackbar plot — make_stackbar_plot","text":"function makes bar plot every brush action Shiny show components clustered pattern group. bar plot colored taxonomic.scope variable processed annotation dataset generated gAnnotation(). See also ggplot2::geom_bar.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_stackbar_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Make stackbar plot — make_stackbar_plot","text":"","code":"data(test_data) make_stackbar_plot(test_maindata, \"Group_5\", c(\"hormonal proteins\",\"structural proteins\",\"enzymes\",\"storage proteins\",\"antibodies\",\"transport proteins\"))"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_graph.html","id":null,"dir":"Reference","previous_headings":"","what":"Make graph plot — make_the_graph","title":"Make graph plot — make_the_graph","text":"Generate graph dashboard.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_graph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make graph plot — make_the_graph","text":"","code":"make_the_graph(ptw, network_output, min_weight, s_ptw, graph_layout)"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_graph.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Make graph plot — make_the_graph","text":"function makes network plot every Pathway selection Shiny show relationship among clustered pattern groups. network plot built dataset generated gNetwork() can adjusted layout minimum weights. See also ggraph::ggraph.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_graph.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Make graph plot — make_the_graph","text":"","code":"data(test_data) make_the_graph(test_graphptw, test_network, 0.03, \"Muscular System\",\"kk\")"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_table.html","id":null,"dir":"Reference","previous_headings":"","what":"Make table from brushed region — make_the_table","title":"Make table from brushed region — make_the_table","text":"Generate table selected groups dashboard.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make table from brushed region — make_the_table","text":"","code":"make_the_table(p)"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Make table from brushed region — make_the_table","text":"p ggplot output, see also ggplot2::ggplot_build.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_table.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Make table from brushed region — make_the_table","text":"function simply aims collect position information brushed area network plot returns annotation table corresponding features. takes plot object produce object information table.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/mass_produce_lm__.html","id":null,"dir":"Reference","previous_headings":"","what":"Mass produce linear model — mass_produce_lm__","title":"Mass produce linear model — mass_produce_lm__","text":"Take column dependent vairable time, produce n linear models n columns dataset.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/mass_produce_lm__.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mass produce linear model — mass_produce_lm__","text":"","code":"mass_produce_lm__(data)"},{"path":"https://kaiyanm.github.io/MolPad/reference/mass_produce_lm__.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mass produce linear model — mass_produce_lm__","text":"data dataframe.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/mass_produce_lm__.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Mass produce linear model — mass_produce_lm__","text":"internal function designed automatically generate list functions regression. column considered response variable columns. resulting functions can utilized inputs random forest regression gNetwork().","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/match.color__.html","id":null,"dir":"Reference","previous_headings":"","what":"Match color — match.color__","title":"Match color — match.color__","text":"Match vector finite list colors.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/match.color__.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Match color — match.color__","text":"","code":"match.color__(valist, mycolors, extendby = 5)"},{"path":"https://kaiyanm.github.io/MolPad/reference/match.color__.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Match color — match.color__","text":"valist vector want specify colors element. mycolors vector colors. extendby number select 1, 2, 3, 4, 5, representing distinct auto-fill schemes.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/match.color__.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Match color — match.color__","text":"","code":"my_vector <- paste0(\"N_\",1:10) match.color__(my_vector,c(\"red\",\"yellow\",\"blue\")) #> N_1 N_2 N_3 N_4 N_5 #> \"red\" \"yellow\" \"blue\" \"white\" \"antiquewhite3\" #> N_6 N_7 N_8 N_9 N_10 #> \"aquamarine3\" \"azure3\" \"bisque2\" \"blue\" \"blueviolet\""},{"path":"https://kaiyanm.github.io/MolPad/reference/match_database.html","id":null,"dir":"Reference","previous_headings":"","what":"Match database — match_database","title":"Match database — match_database","text":"matches selected columns online database names.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/match_database.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Match database — match_database","text":"","code":"match_database(data, id_colname, id_type)"},{"path":"https://kaiyanm.github.io/MolPad/reference/match_database.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Match database — match_database","text":"match_database() internal function matches selected columns dataset corresponding databases. Currently MolPad support two types annotation database: GO KEGG. want custom URL platform, download modify paste_URL() function.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/match_database.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Match database — match_database","text":"","code":"data(test_data) head(test_annotations_processed) #> ID #> 1 1 #> 2 2 #> 3 3 #> 4 4 #> 5 5 #> 6 6 #> GO_ID #> 1 #> 2 #> 3 GO:0003674,GO:0003824,GO:0005575,GO:0006629,GO:0006644,GO:0006650,GO:0006655,GO:0006793,GO:0006796,GO:0008150,GO:0008152,GO:0008610,GO:0008654,GO:0008808,GO:0009058,GO:0009987,GO:0016020,GO:0016740,GO:0016772,GO:0016780,GO:0019637,GO:0030572,GO:0032048,GO:0032049,GO:0044237,GO:0044238,GO:0044249,GO:0044255,GO:0045017,GO:0046471,GO:0046474,GO:0046486,GO:0071704,GO:0090407,GO:1901576 #> 4 GO:0003674,GO:0003676,GO:0003723,GO:0003729,GO:0003824,GO:0004654,GO:0005488,GO:0005575,GO:0005622,GO:0005623,GO:0005737,GO:0006139,GO:0006401,GO:0006402,GO:0006725,GO:0006807,GO:0008150,GO:0008152,GO:0009056,GO:0009057,GO:0009892,GO:0009987,GO:0010468,GO:0010605,GO:0010629,GO:0016070,GO:0016071,GO:0016740,GO:0016772,GO:0016779,GO:0019222,GO:0019439,GO:0034641,GO:0034655,GO:0043170,GO:0044237,GO:0044238,GO:0044248,GO:0044260,GO:0044265,GO:0044270,GO:0044424,GO:0044464,GO:0046483,GO:0046700,GO:0048519,GO:0050789,GO:0060255,GO:0065007,GO:0071704,GO:0090304,GO:0097159,GO:1901360,GO:1901361,GO:1901363,GO:1901575 #> 5 #> 6 #> KEGG_ID Pathway taxonomic.scope #> 1 K07124 Integumentary System hormonal proteins #> 2 Skeletal System structural proteins #> 3 K06131 Muscular System enzymes #> 4 K00962 Nervous System contractile proteins #> 5 K02083 Endocrine System contractile proteins #> 6 Cardiovascular System structural proteins head(match_database(test_annotations_processed,id_colname = c(\"GO_ID\",\"KEGG_ID\"),id_type = c(\"GO\",\"KEGG\"))) #> ID #> 1 1 #> 2 2 #> 3 3 #> 4 4 #> 5 5 #> 6 6 #> GO_ID #> 1 #> 2 #> 3 GO:0003674<\/a>
GO:0003824<\/a>
GO:0005575<\/a>
GO:0006629<\/a>
GO:0006644<\/a>
GO:0006650<\/a>
GO:0006655<\/a>
GO:0006793<\/a>
GO:0006796<\/a>
GO:0008150<\/a>
GO:0008152<\/a>
GO:0008610<\/a>
GO:0008654<\/a>
GO:0008808<\/a>
GO:0009058<\/a>
GO:0009987<\/a>
GO:0016020<\/a>
GO:0016740<\/a>
GO:0016772<\/a>
GO:0016780<\/a>
GO:0019637<\/a>
GO:0030572<\/a>
GO:0032048<\/a>
GO:0032049<\/a>
GO:0044237<\/a>
GO:0044238<\/a>
GO:0044249<\/a>
GO:0044255<\/a>
GO:0045017<\/a>
GO:0046471<\/a>
GO:0046474<\/a>
GO:0046486<\/a>
GO:0071704<\/a>
GO:0090407<\/a>
GO:1901576<\/a> #> 4 GO:0003674<\/a>
GO:0003676<\/a>
GO:0003723<\/a>
GO:0003729<\/a>
GO:0003824<\/a>
GO:0004654<\/a>
GO:0005488<\/a>
GO:0005575<\/a>
GO:0005622<\/a>
GO:0005623<\/a>
GO:0005737<\/a>
GO:0006139<\/a>
GO:0006401<\/a>
GO:0006402<\/a>
GO:0006725<\/a>
GO:0006807<\/a>
GO:0008150<\/a>
GO:0008152<\/a>
GO:0009056<\/a>
GO:0009057<\/a>
GO:0009892<\/a>
GO:0009987<\/a>
GO:0010468<\/a>
GO:0010605<\/a>
GO:0010629<\/a>
GO:0016070<\/a>
GO:0016071<\/a>
GO:0016740<\/a>
GO:0016772<\/a>
GO:0016779<\/a>
GO:0019222<\/a>
GO:0019439<\/a>
GO:0034641<\/a>
GO:0034655<\/a>
GO:0043170<\/a>
GO:0044237<\/a>
GO:0044238<\/a>
GO:0044248<\/a>
GO:0044260<\/a>
GO:0044265<\/a>
GO:0044270<\/a>
GO:0044424<\/a>
GO:0044464<\/a>
GO:0046483<\/a>
GO:0046700<\/a>
GO:0048519<\/a>
GO:0050789<\/a>
GO:0060255<\/a>
GO:0065007<\/a>
GO:0071704<\/a>
GO:0090304<\/a>
GO:0097159<\/a>
GO:1901360<\/a>
GO:1901361<\/a>
GO:1901363<\/a>
GO:1901575<\/a> #> 5 #> 6 #> KEGG_ID #> 1 K07124<\/a> #> 2 #> 3 K06131<\/a> #> 4 K00962<\/a> #> 5 K02083<\/a> #> 6 #> Pathway taxonomic.scope #> 1 Integumentary System hormonal proteins #> 2 Skeletal System structural proteins #> 3 Muscular System enzymes #> 4 Nervous System contractile proteins #> 5 Endocrine System contractile proteins #> 6 Cardiovascular System structural proteins"},{"path":"https://kaiyanm.github.io/MolPad/reference/paste_URL.html","id":null,"dir":"Reference","previous_headings":"","what":"Paste URL — paste_URL","title":"Paste URL — paste_URL","text":"Retrieve database IDs associate respective URLs.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/paste_URL.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Paste URL — paste_URL","text":"","code":"paste_URL(x, id_type)"},{"path":"https://kaiyanm.github.io/MolPad/reference/paste_URL.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Paste URL — paste_URL","text":"","code":"data(test_data) paste_URL(test_annotations$GO_ID[1:4], id_type = \"GO\") #> [1] NA #> [2] NA #> [3] \"GO:0003674<\/a>
GO:0003824<\/a>
GO:0005575<\/a>
GO:0006629<\/a>
GO:0006644<\/a>
GO:0006650<\/a>
GO:0006655<\/a>
GO:0006793<\/a>
GO:0006796<\/a>
GO:0008150<\/a>
GO:0008152<\/a>
GO:0008610<\/a>
GO:0008654<\/a>
GO:0008808<\/a>
GO:0009058<\/a>
GO:0009987<\/a>
GO:0016020<\/a>
GO:0016740<\/a>
GO:0016772<\/a>
GO:0016780<\/a>
GO:0019637<\/a>
GO:0030572<\/a>
GO:0032048<\/a>
GO:0032049<\/a>
GO:0044237<\/a>
GO:0044238<\/a>
GO:0044249<\/a>
GO:0044255<\/a>
GO:0045017<\/a>
GO:0046471<\/a>
GO:0046474<\/a>
GO:0046486<\/a>
GO:0071704<\/a>
GO:0090407<\/a>
GO:1901576<\/a>\" #> [4] \"GO:0003674<\/a>
GO:0003676<\/a>
GO:0003723<\/a>
GO:0003729<\/a>
GO:0003824<\/a>
GO:0004654<\/a>
GO:0005488<\/a>
GO:0005575<\/a>
GO:0005622<\/a>
GO:0005623<\/a>
GO:0005737<\/a>
GO:0006139<\/a>
GO:0006401<\/a>
GO:0006402<\/a>
GO:0006725<\/a>
GO:0006807<\/a>
GO:0008150<\/a>
GO:0008152<\/a>
GO:0009056<\/a>
GO:0009057<\/a>
GO:0009892<\/a>
GO:0009987<\/a>
GO:0010468<\/a>
GO:0010605<\/a>
GO:0010629<\/a>
GO:0016070<\/a>
GO:0016071<\/a>
GO:0016740<\/a>
GO:0016772<\/a>
GO:0016779<\/a>
GO:0019222<\/a>
GO:0019439<\/a>
GO:0034641<\/a>
GO:0034655<\/a>
GO:0043170<\/a>
GO:0044237<\/a>
GO:0044238<\/a>
GO:0044248<\/a>
GO:0044260<\/a>
GO:0044265<\/a>
GO:0044270<\/a>
GO:0044424<\/a>
GO:0044464<\/a>
GO:0046483<\/a>
GO:0046700<\/a>
GO:0048519<\/a>
GO:0050789<\/a>
GO:0060255<\/a>
GO:0065007<\/a>
GO:0071704<\/a>
GO:0090304<\/a>
GO:0097159<\/a>
GO:1901360<\/a>
GO:1901361<\/a>
GO:1901363<\/a>
GO:1901575<\/a>\""},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":null,"dir":"Reference","previous_headings":"","what":"Pre-processing datasets — pre_process","title":"Pre-processing datasets — pre_process","text":"pre_process() function aids processing data inputs automatically establishes standardized format future use. allows two types data input: list datasets different sources long dataset containing specified last column type.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pre-processing datasets — pre_process","text":"","code":"pre_process( data, typenameList = NULL, replaceNA = TRUE, scale = TRUE, autoColName = \"Sec_\" )"},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pre-processing datasets — pre_process","text":"data data.frame describe feature one row. data contain variables ID value time_1, ..., value time_k, type extracting patterns across time. Note initial last column must exactly ID type. multiple data.frame format needs analyzed, also put list data.frame argument. case, variable type required generated next argument typenameList. typenameList vector strings. parameter used clarify source names data.frame, applicable input data list data.frame. default, set \"Dataset_1\", \"Dataset_2\", ..., etc. scale Logical; scale TRUE (default), standardize data.frame row base::scale. converts original value z-score. See also scale_by_row__(). autoColName string; autoColName -NULL (default), automatically set uniform column names data.frames. parameter applicable input data list data.frame. replaceNa Logical; replaceNa TRUE (default), replace NA 0.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pre-processing datasets — pre_process","text":"function returns long data.frame columns ID, value time_1, ..., value time_k, type.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Pre-processing datasets — pre_process","text":"consider two distinct scenarios application: one scenario, individuals collect several datasets various aspects instruments objects. example, might separately detecting lipids, metabolites, peptides specific soil sample. scenario, data uniform quality, can categorized larger groups exhibit significant differences. cases, pre_process() function serves valuable versatile tool. Yet, function optional generating dashboard. Users can perform processing long format matches required output. However, mindful number samples (timepoints) must greater 5 avoid potential errors subsequent prediction section.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pre-processing datasets — pre_process","text":"","code":"data(test_data) head(test_data, 10) #> ID T1 T2 T3 T4 T5 T6 T7 T8 T9 T10 type #> 1 1 1 0 0 1 1 0 0 1 6 6 type_A #> 2 2 6 0 0 0 0 3 1 0 2 1 type_A #> 3 3 1 0 0 0 2 0 0 2 2 1 type_A #> 4 4 4 5 3 3 7 2 1 1 0 0 type_A #> 5 5 4 3 NA 2 5 5 0 0 0 0 type_A #> 6 6 4 1 0 1 3 1 3 5 11 14 type_A #> 7 7 1 0 0 0 1 3 3 1 1 1 type_A #> 8 8 4 2 1 1 1 1 0 0 0 0 type_A #> 9 9 1 1 1 19 22 1 2 1 1 2 type_A #> 10 10 1 1 3 5 8 2 2 2 5 2 type_A a <- pre_process(test_data) head(a, 10) #> ID T1 T2 T3 T4 T5 T6 #> 1 1 -0.25354628 -0.6761234 -0.67612340 -0.25354628 -0.25354628 -0.6761234 #> 2 2 2.41458180 -0.6678631 -0.66786305 -0.66786305 -0.66786305 0.8733594 #> 3 3 0.21764288 -0.8705715 -0.87057150 -0.87057150 1.30585725 -0.8705715 #> 4 4 0.61658123 1.0569964 0.17616607 0.17616607 1.93782672 -0.2642491 #> 5 5 0.96186009 0.5038315 -0.87025436 0.04580286 1.41988870 1.4198887 #> 6 6 -0.06459959 -0.7105955 -0.92592741 -0.71059546 -0.27993154 -0.7105955 #> 7 7 -0.09086738 -0.9995412 -0.99954118 -0.99954118 -0.09086738 1.7264802 #> 8 8 2.40535118 0.8017837 0.00000000 0.00000000 0.00000000 0.0000000 #> 9 9 -0.50260633 -0.5026063 -0.50260633 1.70395805 2.07171878 -0.5026063 #> 10 10 -0.94019379 -0.9401938 -0.04477113 0.85065153 2.19378551 -0.4924825 #> T7 T8 T9 T10 type #> 1 -0.6761234 -0.25354628 1.85933936 1.85933936 type_A #> 2 -0.1541222 -0.66786305 0.35961857 -0.15412224 type_A #> 3 -0.8705715 1.30585725 1.30585725 0.21764288 type_A #> 4 -0.7046643 -0.70466426 -1.14507943 -1.14507943 type_A #> 5 -0.8702544 -0.87025436 -0.87025436 -0.87025436 type_A #> 6 -0.2799315 0.15073237 1.44272411 2.08871998 type_A #> 7 1.7264802 -0.09086738 -0.09086738 -0.09086738 type_A #> 8 -0.8017837 -0.80178373 -0.80178373 -0.80178373 type_A #> 9 -0.3800194 -0.50260633 -0.50260633 -0.38001942 type_A #> 10 -0.4924825 -0.49248246 0.85065153 -0.49248246 type_A"},{"path":"https://kaiyanm.github.io/MolPad/reference/reshape_for_make_functions.html","id":null,"dir":"Reference","previous_headings":"","what":"Reshape for 'make' functions — reshape_for_make_functions","title":"Reshape for 'make' functions — reshape_for_make_functions","text":"internal function produces three primary datasets dashboard intended \"make\" functions.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/reshape_for_make_functions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reshape for 'make' functions — reshape_for_make_functions","text":"","code":"reshape_for_make_functions(data, cluster, annotation, id_colname, id_type)"},{"path":"https://kaiyanm.github.io/MolPad/reference/reshape_for_make_functions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reshape for 'make' functions — reshape_for_make_functions","text":"data output pre_process() cluster output gClusters() annotation output gPathway() id_colname columns contain database IDs. id_type corresponding database names columns.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/reshape_for_make_functions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reshape for 'make' functions — reshape_for_make_functions","text":"","code":"data(test_data) l <- reshape_for_make_functions(test_data_processed, test_cluster, test_annotations_processed, id_colname = c(\"GO_ID\",\"KEGG_ID\"),id_type = c(\"GO\",\"KEGG\")) head(l[[1]]) #> ID cluster #> 1 1 Group_2 #> 2 2 Group_5 #> 3 3 Group_2 #> 4 4 Group_4 #> 5 5 Group_4 #> 6 6 Group_2 #> GO_ID #> 1 #> 2 #> 3 GO:0003674,GO:0003824,GO:0005575,GO:0006629,GO:0006644,GO:0006650,GO:0006655,GO:0006793,GO:0006796,GO:0008150,GO:0008152,GO:0008610,GO:0008654,GO:0008808,GO:0009058,GO:0009987,GO:0016020,GO:0016740,GO:0016772,GO:0016780,GO:0019637,GO:0030572,GO:0032048,GO:0032049,GO:0044237,GO:0044238,GO:0044249,GO:0044255,GO:0045017,GO:0046471,GO:0046474,GO:0046486,GO:0071704,GO:0090407,GO:1901576 #> 4 GO:0003674,GO:0003676,GO:0003723,GO:0003729,GO:0003824,GO:0004654,GO:0005488,GO:0005575,GO:0005622,GO:0005623,GO:0005737,GO:0006139,GO:0006401,GO:0006402,GO:0006725,GO:0006807,GO:0008150,GO:0008152,GO:0009056,GO:0009057,GO:0009892,GO:0009987,GO:0010468,GO:0010605,GO:0010629,GO:0016070,GO:0016071,GO:0016740,GO:0016772,GO:0016779,GO:0019222,GO:0019439,GO:0034641,GO:0034655,GO:0043170,GO:0044237,GO:0044238,GO:0044248,GO:0044260,GO:0044265,GO:0044270,GO:0044424,GO:0044464,GO:0046483,GO:0046700,GO:0048519,GO:0050789,GO:0060255,GO:0065007,GO:0071704,GO:0090304,GO:0097159,GO:1901360,GO:1901361,GO:1901363,GO:1901575 #> 5 #> 6 #> KEGG_ID Pathway taxonomic.scope #> 1 K07124 Integumentary System hormonal proteins #> 2 Skeletal System structural proteins #> 3 K06131 Muscular System enzymes #> 4 K00962 Nervous System contractile proteins #> 5 K02083 Endocrine System contractile proteins #> 6 Cardiovascular System structural proteins head(l[[2]]) #> # A tibble: 6 × 6 #> ID type cluster day value taxonomic.scope #> #> 1 1 type_A Group_2 T1 -0.254 hormonal proteins #> 2 1 type_A Group_2 T2 -0.676 hormonal proteins #> 3 1 type_A Group_2 T3 -0.676 hormonal proteins #> 4 1 type_A Group_2 T4 -0.254 hormonal proteins #> 5 1 type_A Group_2 T5 -0.254 hormonal proteins #> 6 1 type_A Group_2 T6 -0.676 hormonal proteins head(l[[3]]) #> ID cluster #> 1 1 Group_2 #> 2 2 Group_5 #> 3 3 Group_2 #> 4 4 Group_4 #> 5 5 Group_4 #> 6 6 Group_2 #> GO_ID #> 1 #> 2 #> 3 GO:0003674<\/a>
GO:0003824<\/a>
GO:0005575<\/a>
GO:0006629<\/a>
GO:0006644<\/a>
GO:0006650<\/a>
GO:0006655<\/a>
GO:0006793<\/a>
GO:0006796<\/a>
GO:0008150<\/a>
GO:0008152<\/a>
GO:0008610<\/a>
GO:0008654<\/a>
GO:0008808<\/a>
GO:0009058<\/a>
GO:0009987<\/a>
GO:0016020<\/a>
GO:0016740<\/a>
GO:0016772<\/a>
GO:0016780<\/a>
GO:0019637<\/a>
GO:0030572<\/a>
GO:0032048<\/a>
GO:0032049<\/a>
GO:0044237<\/a>
GO:0044238<\/a>
GO:0044249<\/a>
GO:0044255<\/a>
GO:0045017<\/a>
GO:0046471<\/a>
GO:0046474<\/a>
GO:0046486<\/a>
GO:0071704<\/a>
GO:0090407<\/a>
GO:1901576<\/a> #> 4 GO:0003674<\/a>
GO:0003676<\/a>
GO:0003723<\/a>
GO:0003729<\/a>
GO:0003824<\/a>
GO:0004654<\/a>
GO:0005488<\/a>
GO:0005575<\/a>
GO:0005622<\/a>
GO:0005623<\/a>
GO:0005737<\/a>
GO:0006139<\/a>
GO:0006401<\/a>
GO:0006402<\/a>
GO:0006725<\/a>
GO:0006807<\/a>
GO:0008150<\/a>
GO:0008152<\/a>
GO:0009056<\/a>
GO:0009057<\/a>
GO:0009892<\/a>
GO:0009987<\/a>
GO:0010468<\/a>
GO:0010605<\/a>
GO:0010629<\/a>
GO:0016070<\/a>
GO:0016071<\/a>
GO:0016740<\/a>
GO:0016772<\/a>
GO:0016779<\/a>
GO:0019222<\/a>
GO:0019439<\/a>
GO:0034641<\/a>
GO:0034655<\/a>
GO:0043170<\/a>
GO:0044237<\/a>
GO:0044238<\/a>
GO:0044248<\/a>
GO:0044260<\/a>
GO:0044265<\/a>
GO:0044270<\/a>
GO:0044424<\/a>
GO:0044464<\/a>
GO:0046483<\/a>
GO:0046700<\/a>
GO:0048519<\/a>
GO:0050789<\/a>
GO:0060255<\/a>
GO:0065007<\/a>
GO:0071704<\/a>
GO:0090304<\/a>
GO:0097159<\/a>
GO:1901360<\/a>
GO:1901361<\/a>
GO:1901363<\/a>
GO:1901575<\/a> #> 5 #> 6 #> KEGG_ID #> 1 K07124<\/a> #> 2 #> 3 K06131<\/a> #> 4 K00962<\/a> #> 5 K02083<\/a> #> 6 #> Pathway taxonomic.scope #> 1 Integumentary System hormonal proteins #> 2 Skeletal System structural proteins #> 3 Muscular System enzymes #> 4 Nervous System contractile proteins #> 5 Endocrine System contractile proteins #> 6 Cardiovascular System structural proteins"},{"path":"https://kaiyanm.github.io/MolPad/reference/scale_by_row__.html","id":null,"dir":"Reference","previous_headings":"","what":"Scale by row — scale_by_row__","title":"Scale by row — scale_by_row__","text":"Scales values sample, row independently processed.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/scale_by_row__.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Scale by row — scale_by_row__","text":"","code":"scale_by_row__(data)"},{"path":"https://kaiyanm.github.io/MolPad/reference/scale_by_row__.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Scale by row — scale_by_row__","text":"input expected data frame first column ID following columns containing observations different time points. ID column remains unaltered, columns double (dbl) format scaled.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/scale_by_row__.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Scale by row — scale_by_row__","text":"","code":"data(test_data) scale_by_row__(test_data[1:5,1:10]) #> ID T1 T2 T3 T4 T5 T6 #> 1 1 -0.05847053 -0.5847053 -0.58470535 -0.05847053 -0.05847053 -0.5847053 #> 2 2 2.26366583 -0.6467617 -0.64676167 -0.64676167 -0.64676167 0.8084521 #> 3 3 0.22866478 -0.8003267 -0.80032673 -0.80032673 1.25765629 -0.8003267 #> 4 4 0.50395263 0.9575100 0.05039526 0.05039526 1.86462473 -0.4031621 #> 5 5 0.73869087 0.2841119 NA -0.17046712 1.19326987 1.1932699 #> T7 T8 T9 #> 1 -0.5847053 -0.05847053 2.5727035 #> 2 -0.1616904 -0.64676167 0.3233808 #> 3 -0.8003267 1.25765629 1.2576563 #> 4 -0.8567195 -0.85671947 -1.3102768 #> 5 -1.0796251 -1.07962512 -1.0796251"},{"path":"https://kaiyanm.github.io/MolPad/reference/test_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Test data — test_data","title":"Test data — test_data","text":"synthetically generated dataset created basic testing purposes.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/test_data.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Test data — test_data","text":"two datasets: data annotations","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/test_data.html","id":"data","dir":"Reference","previous_headings":"","what":"data","title":"Test data — test_data","text":"data frame 100 rows 12 variables: ID row ID T1~T10 count value 10 timepoints type type ~D","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/test_data.html","id":"annotations","dir":"Reference","previous_headings":"","what":"annotations","title":"Test data — test_data","text":"data frame 100 rows 5 variables: ID row ID GO_ID go ID KEGG_ID kegg ID system primary lable: 'Integumentary System', 'Skeletal System', 'Muscular System', 'Nervous System', 'Endocrine System', 'Cardiovascular System', 'Lymphatic System', 'Respiratory System', 'Digestive System', 'Urinary System' class secondary label: 'antibodies', 'contractile proteins', 'enzymes', 'hormonal proteins', 'structural proteins', 'storage proteins', 'transport proteins'","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/test_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Test data — test_data","text":"","code":"data(test_data)"},{"path":"https://kaiyanm.github.io/MolPad/reference/transpose_dataframe__.html","id":null,"dir":"Reference","previous_headings":"","what":"Transpose dataframe — transpose_dataframe__","title":"Transpose dataframe — transpose_dataframe__","text":"function transposes provided data frame, using values first column new column names.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/transpose_dataframe__.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transpose dataframe — transpose_dataframe__","text":"","code":"transpose_dataframe__(data)"},{"path":"https://kaiyanm.github.io/MolPad/reference/transpose_dataframe__.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transpose dataframe — transpose_dataframe__","text":"expected input data frame first column serves Time, subsequent columns contain observations various features.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/transpose_dataframe__.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transpose dataframe — transpose_dataframe__","text":"","code":"a <- data.frame(\"Day\"=c(\"Day1\",\"Day2\",\"Day3\"),\"feature_1\" =c(1,2,3),\"feature_2\" =c(0,4,1),\"feature_3\" =c(1,1,0)) a #> Day feature_1 feature_2 feature_3 #> 1 Day1 1 0 1 #> 2 Day2 2 4 1 #> 3 Day3 3 1 0 transpose_dataframe__(a) #> Day1 Day2 Day3 #> 1 1 2 3 #> 2 0 4 1 #> 3 1 1 0"}]
diff --git a/man/figures/.DS_Store b/man/figures/.DS_Store
index bbe3881..862bd8d 100644
Binary files a/man/figures/.DS_Store and b/man/figures/.DS_Store differ
diff --git a/man/figures/cheesecase.png b/man/figures/cheesecase.png
deleted file mode 100644
index d91f265..0000000
Binary files a/man/figures/cheesecase.png and /dev/null differ
diff --git a/man/figures/cheesecase1.png b/man/figures/cheesecase1.png
new file mode 100644
index 0000000..6d4d8aa
Binary files /dev/null and b/man/figures/cheesecase1.png differ
diff --git a/vignettes/cheese.Rmd b/vignettes/cheese.Rmd
index a26acd4..18967b8 100644
--- a/vignettes/cheese.Rmd
+++ b/vignettes/cheese.Rmd
@@ -98,3 +98,18 @@ gDashboard(cheesedata,
id_colname = c("GO_ID","KEGG_ID"),
id_type = c("GO","KEGG"))
```
+
+# Results
+
+For example, when we filter for Actinomycetota (Actinobacteria) as the functional group, we see that there are no edges connecting to group 10 and group 3- the clusters that have the most features associated with Actinomycetoa for Cheese sample A (Fig 1.3.A).
+
+
+
+ { width=100% }
+ *Fig 1: Dashboard showing Actinomycetota filtered network (A) with enrichment pattern for Cheese Sample-A (B) and Cheese Sample-C (C); Cluster pattern for Group 9, which also is enriched for Type IV secretion genes (D).*
+
+
+
+Looking at the pattern traces of these groups, (Fig 1.3.B), there is a peak in samples A4 (week 9) and A5 (week 13), which mirrors the 16S rRNA results of Saak et al. Since these two clusters do not have edges connecting them to other groups, this suggests that the Actinomycetoa features found in these groups follow distinct longitudinal succession patterns that are independent. When looking at Actinomycetoa within Cheese Sample C we see a different pattern. Groups 2 and 5, have the most features associated with Actinomycetoa, but they are highly connected to the other groups (Fig 1.3.A). From these results, we can hypothesize that though Actinomycetoa features are more abundant in later time points for both cheese samples, their dynamics are differentially influenced.
+
+The authors found that Type VI secretion was enriched in Pseudomonadota bacteria (specifically, Psychrobacter), and hypothesized this enrichment was due to the importance of physical species interactions that occur with this habitat. Using MolPad, we searched for other secretion systems associated genes, to understand their dynamics within the community. Focusing on KEGG annotated Type IV secretion genes, we found that Group 9 contained 12/13 of these genes. Within this group, features that cluster are ones that peak in Cheese sample C5 (week 13, Fig 1.3.D). This is also the most taxonomically diverse sample. From this, we can hypothesize that increased taxonomic diversity is also associated with increases in genes that are related to competitive species interactions.
\ No newline at end of file
diff --git a/vignettes/getstarted.Rmd b/vignettes/getstarted.Rmd
index b9ec236..5724477 100644
--- a/vignettes/getstarted.Rmd
+++ b/vignettes/getstarted.Rmd
@@ -45,6 +45,8 @@ library(MolPad)
load("/Users/hazelma/Documents/GitHub/MolPad_Data/multiomics_simulate.RData")
```
+
+
# Before Input
We recommend carefully reviewing your data and considering applying normalization (standardization, quantile normalization, log transformation, ...) and imputation (KNN, PMM, Mean/Median/Mode, ...) to address issues related to library size and missing data beforehead, otherwise the `pre_processing()` function will default to converting NAs to 0s.
@@ -57,6 +59,7 @@ We recommend carefully reviewing your data and considering applying normalizatio
* If it's hard to decide, you can always experiment with different methods and use cross-validation to compare their effectiveness. Let your choice be guided by domain knowledge and the specific requirements of your analysis to increase the quality of the following steps.
+
# Data Format
@@ -72,14 +75,17 @@ If your datasets are not multi-omics, you can simply apply it to our dashboard b
### Data Format and Pre-process
+
{ width=70% }
-
+
In the figure, A, B, and C can represent datasets from different sources or different aspects of measurements. Below are two examples of what the input data might look like and how to transform it into the required format through `pre_process()` modules.
+
+
#### If your input is a list of datasets:
Here is a list of datasets, each following the same format: they all have an ID column and identical column names.
@@ -105,6 +111,8 @@ data <- pre_process(fungal_dlist, typenameList = c('DataSource_A','DataSource_B'
head(data,10)
```
+
+
#### If your input is a single dataset:
Here is a dataset that already includes a labeled 'type' column:
@@ -121,6 +129,7 @@ x1 <- pre_process(x, replaceNA = TRUE,scale = TRUE)
head(x1,10)
```
+
# Choose Your Annotation
@@ -137,15 +146,18 @@ If your data has more than two layers of labels (like the cheese dataset), you c
Annotation_path_taxon <- gAnnotation(annotations,"phylum","class")
```
+
+
# Network Generation
To generate the network, we first scale and cluster the trajectories across the input molecular features. Trajectories are clustered using K-means, and a built-in elbow method is used to select the optimal K. We build network between clustered trajectories by adapting the GENIE3 algorithm. Specifically, the network is learned through a series of regressions, where each cluster centroid is predicted from the expression patterns of all the other cluster centroids using random forests. We choose random forests because of their potential to model interacting features and non-linearity without strong assumptions.
+
{ width=50% }
-
+
To determine which feature clusters are predictive of a given trajectory, the Mean Decrease Accuracy of a subset of top predictors whose expression directly influences the expression of the target cluster is taken as an indication of a putative link.
@@ -159,6 +171,8 @@ network_result <- cluster_result |>
gNetwork_view(network_result)
```
+
+
# Dashboard Navigation
@@ -173,46 +187,68 @@ gDashboard(data,
id_type = "KEGG")
```
+
{ width=70% }
+
Once you've launched the Shiny dashboard, you can zoom in or make adjustments to explore interesting findings within your data. To effectively navigate the dashboard generated by MolPad, you'll follow three main steps:
-### Choose a Primary Functional Annotation and Adjust Edge Density
+### 1. Choose a Primary Functional Annotation and Adjust Edge Density
Start by selecting a primary functional annotation from the available options. Then, fine-tune the edge density by adjusting the threshold value on the importance score. Nodes that turn bright green indicate clusters containing the most features related to the chosen functional annotation.
-### Explore the Network
+### 2. Explore the Network
Brushing on the network unveils patterns of taxonomic composition and typical trajectories. You can also zoom into specific taxonomic annotations by applying filters.
-### Investigate Feature Details and Related Function Annotations
+### 3. Investigate Feature Details and Related Function Annotations
Delve into the feature table to examine the specifics of the features within the selected clusters. Explore additional related function annotations using the drop-down options. Click on the provided links to access online information about the items of interest. The interface encourages iterative exploration, enabling you to conduct multiple steps to answer specific questions, such as comparing pattern distributions between two functions or identifying functionally important community members metabolizing a feature of interest.
+
+
+
{ width=100% }
+
# Related Visualization Outputs
The following visualizations are integrated into the dashboard, so there's no need to run function for each plot individually. We've linked a stacked bar plot and a ribbon plot with the network to facilitate focus and context explorations. This setup helps in detecting similar longitudinal patterns and understanding their relationships.
+
+
+
### Network Plot
+
+The network plot is a powerful visualization tool that displays the relationships between different groups or features within your data. In MolPad, the network plot helps to identify clusters of features that share similar patterns, revealing underlying connections that might not be immediately obvious. By visualizing these connections, users can gain a clearer understanding of the structure within their data, making it easier to pinpoint significant associations and trends.
+
```{r,echo=FALSE}
data("test_data")
make_the_graph(test_graphptw, test_network, 0.03, "Muscular System","nicely")
```
+
+
### Stack Bar Plot
+
+The stacked bar plot provides a detailed view of the composition of each cluster or group in your dataset. By stacking different categories on top of each other within a single bar, this plot allows for a quick comparison of relative proportions across multiple groups. This is particularly useful in microbiome experiments where understanding the distribution of taxa across different conditions or time points is crucial. The stacked bar plot makes it easy to see how these distributions change between experimental conditions, facilitating deeper insights.
+
```{r,echo=FALSE}
make_stackbar_plot(test_maindata, c("Group_1","Group_2","Group_3","Group_4"), c("hormonal proteins","structural proteins","enzymes","storage proteins","antibodies","transport proteins"))
```
+
+
+
### Ribbon Plot
+The ribbon plot is designed to visualize changes over time, making it an ideal tool for tracking longitudinal data. In MolPad, the ribbon plot illustrates how the abundance or expression levels of features vary across different time points or conditions. The smooth, flowing design of the ribbons helps to emphasize trends and patterns, enabling users to quickly identify periods of significant change or stability. This plot is particularly advantageous when comparing multiple groups, as it clearly shows overlapping trends and divergences, providing a comprehensive view of temporal dynamics in the data.
+
```{r,echo=FALSE}
make_line_plot(test_maindata, c("Group_1","Group_2","Group_3","Group_4"), c("hormonal proteins","structural proteins","enzymes","storage proteins","antibodies","transport proteins"))
diff --git a/vignettes/whymolpad.Rmd b/vignettes/whymolpad.Rmd
index c308dba..38cb484 100644
--- a/vignettes/whymolpad.Rmd
+++ b/vignettes/whymolpad.Rmd
@@ -24,11 +24,17 @@ library(dplyr)
# Why MolPad?
With the increasing multi-omics data and longitudinal designs integrated into microbiome experiments, there is a growing need to present the network, especially with complex variations across biological modalities. Network perspective helps detect the underlying co-occurrence among microbiome samples, allowing for high-level insights into the global structure. Yet when it comes to experimental data that records time series for 100,000 features, the network will collapse into some entangled clumps and therefore unable to read.
+
+
{ width=50% }
+
+
+# Improvements
+
For the aim of network interpretation, MolPad shows improvements in 3 important aspects:
* Capture groups of features that share longitudinal patterns and functional properties within the network.
@@ -39,6 +45,7 @@ This package especially facilitates the exploration of longitudinal molecular co
Unlike existing tools, MolPad stands out for its rich network interactivity and support for contextualization. It is also equipped with a beginner-friendly interface and self-contained data processing functions, making it accessible to users with limited programming experience.
+
# Key Features
@@ -54,25 +61,31 @@ We build network between clustered trajectories by adapting the GENIE3 algorithm
This package provides an interactive dashboard designed to help users explore data and uncover relationships within complex networks. By utilizing focus-plus-context visualization, the dashboard allows users to zoom in on specific details while keeping the broader dataset in view. This approach enables a deeper understanding of the data, making it easier to identify patterns and make informed decisions.
+
+
# What does it look like?
+Now, let’s see what you can get from the dashboard. We’ll start with an overview and then demonstrate how to discover patterns within your data.
+
+
### MolPad Dashboard Overview
+
{ width=100% }
-
+
Above is the overview of the MolPad Dashboard. To explore the dashboard effectively, you can start by following the sequence A-B-C-D. This approach will guide you through the cluster-level network, taxonomic-level bar plot, type-level line plot, and feature-level table. From there, you can modify detailed settings to identify and investigate special patterns within your data.
-
### Discovering related patterns
{ width=100% }
+
Here is a short example of discovering related patterns using the network plot: The shade of the edges represents the proximity of nodes. In the brushed area, Groups 1-7-8 (circled by solid black lines) and Groups 1-2 (circled by blue dashed lines) are strongly linked. For Groups 1, 7, and 8, the patterns form a W-shape with a noticeable peak at the same time section. Although Group 1 shows higher volatility compared to Group 2, both groups display a highly overlapping increasing trend.
MolPad Dashboard Overview
Above is the overview of the MolPad Dashboard. To explore the dashboard effectively, you can start by following the sequence A-B-C-D. This approach will guide you through the cluster-level network, @@ -170,6 +181,7 @@
Discovering related patterns
+
Here is a short example of discovering related patterns using the
network plot: The shade of the edges represents the proximity of nodes.
In the brushed area, Groups 1-7-8 (circled by solid black lines) and
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
index 24820a2..106fea2 100644
--- a/docs/pkgdown.yml
+++ b/docs/pkgdown.yml
@@ -6,7 +6,7 @@ articles:
cheese: cheese.html
getstarted: getstarted.html
whymolpad: whymolpad.html
-last_built: 2024-08-15T06:34Z
+last_built: 2024-08-15T07:35Z
urls:
reference: https://kaiyanm.github.io/MolPad/reference
article: https://kaiyanm.github.io/MolPad/articles
diff --git a/docs/reference/figures/cheesecase.png b/docs/reference/figures/cheesecase.png
deleted file mode 100644
index d91f265..0000000
Binary files a/docs/reference/figures/cheesecase.png and /dev/null differ
diff --git a/docs/reference/figures/cheesecase1.png b/docs/reference/figures/cheesecase1.png
new file mode 100644
index 0000000..6d4d8aa
Binary files /dev/null and b/docs/reference/figures/cheesecase1.png differ
diff --git a/docs/reference/gClusters-1.png b/docs/reference/gClusters-1.png
index 993d2a4..463b550 100644
Binary files a/docs/reference/gClusters-1.png and b/docs/reference/gClusters-1.png differ
diff --git a/docs/reference/gClusters.html b/docs/reference/gClusters.html
index 28642a8..566cc47 100644
--- a/docs/reference/gClusters.html
+++ b/docs/reference/gClusters.html
@@ -102,70 +102,70 @@
Examplesreslist <- gClusters(test_data_processed)
# k-means result
reslist[[1]]
-#> K-means clustering with 20 clusters of sizes 7, 6, 7, 10, 8, 2, 3, 5, 4, 6, 10, 3, 3, 4, 4, 6, 4, 3, 2, 3
+#> K-means clustering with 20 clusters of sizes 6, 3, 6, 5, 5, 8, 6, 5, 5, 6, 4, 11, 3, 3, 3, 2, 4, 2, 2, 11
#>
#> Cluster means:
-#> T1 T2 T3 T4 T5 T6
-#> 1 2.22789830 0.02950883 -0.75836275 -0.2619747 -0.5953255 -0.70974025
-#> 2 2.77349939 -0.21724464 -0.35972402 -0.4172516 -0.5000122 -0.11678355
-#> 3 0.93534606 -0.39771771 0.31238452 1.1531782 1.1995301 -1.05203742
-#> 4 -0.27715339 -0.39760069 -0.06466866 1.3995407 2.1316423 -0.28068841
-#> 5 0.11559709 -0.19000668 -0.25629794 -0.1487697 -0.4383348 2.58301481
-#> 6 -0.27847690 -0.82258408 -0.82258408 -0.8225841 1.2338761 -0.82258408
-#> 7 0.26998015 -0.08094677 -0.28926312 -0.7813127 -0.6065051 -0.14945230
-#> 8 -0.81355388 -0.84696982 -0.32573017 1.1218534 2.0955812 -0.56207309
-#> 9 1.45173820 0.26236463 1.31600581 -0.2531012 0.2004547 -1.12389420
-#> 10 0.34700978 0.13673201 0.82464908 0.2571569 1.2656031 0.63541302
-#> 11 -0.19020083 -0.69654961 -0.66836949 -0.4657663 -0.3851487 -0.55904213
-#> 12 1.62582942 1.51619556 -0.35316244 0.2978109 -0.1777508 0.02299732
-#> 13 -0.09187847 -0.87257293 -0.87257293 -0.8725729 -0.5696817 2.05668333
-#> 14 0.85679806 -0.37993358 -0.46099690 0.3934155 0.5857582 1.85027957
-#> 15 1.12205113 0.09185659 -0.89815993 -0.7233153 -0.5717924 -0.86037548
-#> 16 2.09193480 -0.33320285 -0.50786563 -0.5579659 -0.5579659 1.54559773
-#> 17 1.56599593 -0.49119311 -0.83000857 -0.6160199 -0.2092753 1.19151568
-#> 18 0.89463785 0.01698143 -0.57023879 -1.1815834 -1.1815834 1.18824796
-#> 19 -0.13543375 -0.90710305 -0.66248003 -0.4178570 -0.6624800 1.37010464
-#> 20 1.31631350 -0.76059916 -0.76059916 1.5193444 0.6006058 0.84385791
-#> T7 T8 T9 T10
-#> 1 -0.567081109 0.132060988 0.03929621 0.46372005
-#> 2 -0.357532815 -0.298895212 -0.29119673 -0.21485863
-#> 3 -1.222209771 -0.334925678 -0.36853535 -0.22501291
-#> 4 -0.548494719 -0.646992821 -0.66392148 -0.65166279
-#> 5 -0.006327004 -0.327677972 -0.66559889 -0.66559889
-#> 6 -0.822584085 1.233876127 1.23387613 0.68976894
-#> 7 -0.763902351 -0.412975440 0.39220455 2.42217307
-#> 8 -0.651292750 -0.498839228 0.42446013 0.05656427
-#> 9 -0.987867401 -0.340013670 -0.33085684 -0.19483004
-#> 10 -1.004339966 -0.620506186 -0.71949086 -1.12222690
-#> 11 -0.566522777 0.002653353 1.73917982 1.78976662
-#> 12 -0.770498738 -0.620423681 -0.77049874 -0.77049874
-#> 13 0.882337606 0.086536293 0.12686085 0.12686085
-#> 14 -0.526130916 -0.896529515 -0.89652951 -0.52613092
-#> 15 -0.822591022 0.091471523 1.00908283 1.56177203
-#> 16 -0.425513174 -0.557965933 -0.30571480 -0.39133827
-#> 17 0.774154665 0.196302112 -0.62663627 -0.95483532
-#> 18 0.894637851 -0.836550610 0.41343688 0.36201421
-#> 19 -0.907103054 -0.417856999 1.37010464 1.37010464
-#> 20 -0.666108041 -0.666108041 -0.66610804 -0.76059916
+#> T1 T2 T3 T4 T5 T6
+#> 1 2.0919348 -0.33320285 -0.50786563 -0.5579659 -0.5579659 1.5455977
+#> 2 0.8277105 1.32036794 -0.09261332 1.2325986 0.4199176 -0.9576634
+#> 3 0.5077879 -0.24564283 0.53503708 0.2363183 1.0793829 1.3583848
+#> 4 0.2914366 -0.41308350 -0.76541563 -0.9724353 -0.9724353 1.6040887
+#> 5 1.2081037 -0.66790409 -0.66790409 -0.9275526 -0.4204221 1.2719890
+#> 6 0.1155971 -0.19000668 -0.25629794 -0.1487697 -0.4383348 2.5830148
+#> 7 1.2574004 -0.64914997 -0.87502695 0.9353966 0.4760273 1.2681050
+#> 8 0.9389212 -0.86278238 0.43887930 1.0009533 1.3733646 -1.0423264
+#> 9 -0.8135539 -0.84696982 -0.32573017 1.1218534 2.0955812 -0.5620731
+#> 10 -0.3802219 -0.36840199 0.02254271 1.6459653 1.9633618 -0.3246455
+#> 11 -0.1225506 -0.44139873 -0.19548572 1.0299038 2.3840629 -0.2147528
+#> 12 2.5784883 -0.02446156 -0.60355041 -0.2547486 -0.5120230 -0.3947043
+#> 13 0.9922755 0.32399254 -0.94565021 -0.7125240 -0.5104935 -0.9456502
+#> 14 0.2699801 -0.08094677 -0.28926312 -0.7813127 -0.6065051 -0.1494523
+#> 15 1.7947637 -0.47746004 -0.52783931 -0.7635958 -0.7635958 -0.6438957
+#> 16 0.1789247 1.26157166 0.82115649 0.3899368 1.2707671 -0.2614905
+#> 17 1.4517382 0.26236463 1.31600581 -0.2531012 0.2004547 -1.1238942
+#> 18 -0.2784769 -0.82258408 -0.82258408 -0.8225841 1.2338761 -0.8225841
+#> 19 2.1235865 1.05868538 -0.39467611 0.1315587 -0.1315587 0.3946761
+#> 20 -0.2262821 -0.73107613 -0.66098092 -0.4323193 -0.4035074 -0.3836848
+#> T7 T8 T9 T10
+#> 1 -0.425513174 -0.557965933 -0.30571480 -0.391338269
+#> 2 -1.160107459 -0.295057401 -0.64757654 -0.647576540
+#> 3 -1.006282312 -0.766188425 -0.64803067 -1.050766722
+#> 4 0.301678216 -0.371684852 0.66435231 0.633498705
+#> 5 1.209152596 -0.095960244 -0.32347149 -0.586030729
+#> 6 -0.006327004 -0.327677972 -0.66559889 -0.665598891
+#> 7 -0.354972016 -0.601904415 -0.82778139 -0.628094550
+#> 8 -1.159101258 -0.345888528 -0.27147562 -0.070544200
+#> 9 -0.651292750 -0.498839228 0.42446013 0.056564270
+#> 10 -0.647007074 -0.667438225 -0.63229313 -0.611861976
+#> 11 -0.400726188 -0.616324716 -0.71136401 -0.711364008
+#> 12 -0.526022073 -0.185303954 -0.06989626 -0.007778173
+#> 13 -0.945650212 0.121962031 1.19430596 1.427432164
+#> 14 -0.763902351 -0.412975440 0.39220455 2.422173070
+#> 15 -0.260645101 0.389799714 -0.08327821 1.335746686
+#> 16 -0.912917717 -0.481698036 -1.13312530 -1.133125299
+#> 17 -0.987867401 -0.340013670 -0.33085684 -0.194830041
+#> 18 -0.822584085 1.233876127 1.23387613 0.689768939
+#> 19 -0.795567972 -0.795567972 -0.79556797 -0.795567972
+#> 20 -0.612869917 -0.006483243 1.70560793 1.751595921
#>
#> Clustering vector:
#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
-#> 11 16 6 10 10 11 13 12 4 8 3 7 15 2 3 8 5 11 9 1
+#> 20 1 18 16 3 20 5 19 10 9 8 14 15 12 2 9 6 20 17 12
#> 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40
-#> 17 11 5 16 2 8 20 1 5 4 17 4 10 6 4 10 3 15 11 12
+#> 7 20 6 1 12 9 7 12 6 11 5 11 16 18 10 3 8 13 20 2
#> 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
-#> 5 16 11 11 14 11 5 14 16 5 1 17 18 15 5 17 13 4 1 4
+#> 6 1 20 20 7 20 6 3 1 6 15 5 4 13 6 5 4 10 12 10
#> 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80
-#> 3 14 20 15 10 16 2 14 9 9 7 8 5 12 7 4 3 4 3 20
+#> 8 3 7 13 3 1 12 7 17 17 14 9 6 19 14 10 2 11 8 7
#> 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100
-#> 3 19 1 13 19 16 9 2 18 2 4 18 4 1 11 10 8 2 11 1
+#> 8 20 15 4 4 1 17 12 4 12 10 5 11 12 20 3 9 12 20 12
#>
#> Within cluster sum of squares by cluster:
-#> [1] 13.8467580 2.0713631 15.4102772 5.9461649 8.0412334 0.9921913
-#> [7] 3.1842269 3.4617088 9.9418120 17.6305488 8.0515257 4.9390800
-#> [13] 4.0041862 7.3057948 4.7723673 3.0260293 7.3290085 5.6033175
-#> [19] 0.9549143 2.4583646
-#> (between_SS / total_SS = 83.4 %)
+#> [1] 3.0260293 5.0357352 13.2343206 13.1118010 10.3695240 8.0412334
+#> [7] 11.7251902 8.3255313 3.4617088 2.4693324 1.6353685 13.0645655
+#> [13] 2.9416715 3.1842269 5.0413438 2.4669004 9.9418120 0.9921913
+#> [19] 0.9833964 12.4042363
+#> (between_SS / total_SS = 83.1 %)
#>
#> Available components:
#>
diff --git a/docs/reference/gNetwork-1.png b/docs/reference/gNetwork-1.png
index efb4385..14d64f3 100644
Binary files a/docs/reference/gNetwork-1.png and b/docs/reference/gNetwork-1.png differ
diff --git a/docs/reference/gNetwork.html b/docs/reference/gNetwork.html
index dc8f058..f0279e4 100644
--- a/docs/reference/gNetwork.html
+++ b/docs/reference/gNetwork.html
@@ -83,12 +83,12 @@ Examplesnetworkres <- gNetwork(test_cluster, ntop = 3)
head(networkres)
#> weight IncNodePurity var_names from
-#> 1 1.6429045 0.7371260 Group_3 Group_1
-#> 2 0.4588961 0.9310697 Group_5 Group_1
-#> 3 -0.7833169 0.6761286 Group_2 Group_1
-#> 4 5.0076415 0.8821582 Group_5 Group_2
-#> 5 4.1618215 1.1976058 Group_4 Group_2
-#> 6 2.5714583 1.2820391 Group_1 Group_2
+#> 1 1.4215243 1.1776012 Group_5 Group_1
+#> 2 0.7911266 0.7011049 Group_3 Group_1
+#> 3 -1.9891284 0.5811186 Group_2 Group_1
+#> 4 5.8098014 1.1107592 Group_4 Group_2
+#> 5 5.4000389 0.8744358 Group_5 Group_2
+#> 6 1.2492288 1.1816976 Group_1 Group_2
gNetwork_view(networkres)
diff --git a/docs/reference/make_the_graph-1.png b/docs/reference/make_the_graph-1.png
index 061c537..76d9b9c 100644
Binary files a/docs/reference/make_the_graph-1.png and b/docs/reference/make_the_graph-1.png differ
diff --git a/docs/search.json b/docs/search.json
index 782a610..33c4d18 100644
--- a/docs/search.json
+++ b/docs/search.json
@@ -1 +1 @@
-[{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"CC0 1.0 Universal","title":"CC0 1.0 Universal","text":"CREATIVE COMMONS CORPORATION LAW FIRM PROVIDE LEGAL SERVICES. DISTRIBUTION DOCUMENT CREATE ATTORNEY-CLIENT RELATIONSHIP. CREATIVE COMMONS PROVIDES INFORMATION “-” BASIS. CREATIVE COMMONS MAKES WARRANTIES REGARDING USE DOCUMENT INFORMATION WORKS PROVIDED HEREUNDER, DISCLAIMS LIABILITY DAMAGES RESULTING USE DOCUMENT INFORMATION WORKS PROVIDED HEREUNDER.","code":""},{"path":"https://kaiyanm.github.io/MolPad/LICENSE.html","id":"statement-of-purpose","dir":"","previous_headings":"","what":"Statement of Purpose","title":"CC0 1.0 Universal","text":"laws jurisdictions throughout world automatically confer exclusive Copyright Related Rights (defined ) upon creator subsequent owner(s) (, “owner”) original work authorship /database (, “Work”). Certain owners wish permanently relinquish rights Work purpose contributing commons creative, cultural scientific works (“Commons”) public can reliably without fear later claims infringement build upon, modify, incorporate works, reuse redistribute freely possible form whatsoever purposes, including without limitation commercial purposes. owners may contribute Commons promote ideal free culture production creative, cultural scientific works, gain reputation greater distribution Work part use efforts others. /purposes motivations, without expectation additional consideration compensation, person associating CC0 Work (“Affirmer”), extent owner Copyright Related Rights Work, voluntarily elects apply CC0 Work publicly distribute Work terms, knowledge Copyright Related Rights Work meaning intended legal effect CC0 rights. Copyright Related Rights. Work made available CC0 may protected copyright related neighboring rights (“Copyright Related Rights”). Copyright Related Rights include, limited , following: right reproduce, adapt, distribute, perform, display, communicate, translate Work; moral rights retained original author(s) /performer(s); publicity privacy rights pertaining person’s image likeness depicted Work; rights protecting unfair competition regards Work, subject limitations paragraph 4(), ; rights protecting extraction, dissemination, use reuse data Work; database rights (arising Directive 96/9/EC European Parliament Council 11 March 1996 legal protection databases, national implementation thereof, including amended successor version directive); similar, equivalent corresponding rights throughout world based applicable law treaty, national implementations thereof. Waiver. greatest extent permitted , contravention , applicable law, Affirmer hereby overtly, fully, permanently, irrevocably unconditionally waives, abandons, surrenders Affirmer’s Copyright Related Rights associated claims causes action, whether now known unknown (including existing well future claims causes action), Work () territories worldwide, (ii) maximum duration provided applicable law treaty (including future time extensions), (iii) current future medium number copies, (iv) purpose whatsoever, including without limitation commercial, advertising promotional purposes (“Waiver”). Affirmer makes Waiver benefit member public large detriment Affirmer’s heirs successors, fully intending Waiver shall subject revocation, rescission, cancellation, termination, legal equitable action disrupt quiet enjoyment Work public contemplated Affirmer’s express Statement Purpose. 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License shall deemed effective date CC0 applied Affirmer Work. part License reason judged legally invalid ineffective applicable law, partial invalidity ineffectiveness shall invalidate remainder License, case Affirmer hereby affirms () exercise remaining Copyright Related Rights Work (ii) assert associated claims causes action respect Work, either case contrary Affirmer’s express Statement Purpose. Limitations Disclaimers. trademark patent rights held Affirmer waived, abandoned, surrendered, licensed otherwise affected document. Affirmer offers Work -makes representations warranties kind concerning Work, express, implied, statutory otherwise, including without limitation warranties title, merchantability, fitness particular purpose, non infringement, absence latent defects, accuracy, present absence errors, whether discoverable, greatest extent permissible applicable law. Affirmer disclaims responsibility clearing rights persons may apply Work use thereof, including without limitation person’s Copyright Related Rights Work. , Affirmer disclaims responsibility obtaining necessary consents, permissions rights required use Work. Affirmer understands acknowledges Creative Commons party document duty obligation respect CC0 use Work.","code":""},{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/articles/FAQ.html","id":"how-can-i-tell-if-my-dataset-is-suitable-for-using-this-dashboard","dir":"Articles","previous_headings":"Common Questions:","what":"How can I tell if my dataset is suitable for using this dashboard?","title":"FAQs","text":"lot longitudinal data least two descriptive columns (like types), match minimal needs starting dashboard. addition, data specific web-ID columns, MolPad make convenient createing links databases automatically.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/FAQ.html","id":"how-can-i-customize-my-dashboard","dir":"Articles","previous_headings":"Common Questions:","what":"How can I customize my dashboard?","title":"FAQs","text":"can edit title within gDashboard() function. additional personalization, adjusting colors sizes figures, can fork repository, create copy, make modifications dashboard.css file.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/FAQ.html","id":"what-should-i-do-if-i-only-have-a-few-time-points5","dir":"Articles","previous_headings":"Common Questions:","what":"What should I do if I only have a few time points(<5)?","title":"FAQs","text":"Firstly, strongly recommend extending length longitudinal samples. can achieve increasing number measurements two days incorporating simulated data. options don’t suit needs, can also consider using “linear” method gNetwork() function.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Case Study: Cheese Communities","text":"vignette provides comprehensive guide using MolPad case study, including data pre-processing, network generation, result analysis.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"washed-rind-cheese-microbial-communities","dir":"Articles","previous_headings":"","what":"Washed-Rind Cheese Microbial Communities","title":"Case Study: Cheese Communities","text":"Cheese making ancient craft involves coagulation milk proteins form curds, separated liquid whey. curds processed, shaped, aged develop desired texture flavor. Various factors, type milk, specific cultures bacteria, aging conditions, contribute unique characteristics cheese. One crucial step process aging practice, regular washing brine solution plays significant role. process producing cheese, regular washing brine solution aging practice can homogenize microbial communities cheese’ surface facilitate intermicrobial interactions. following parts, analyze longitudinal data set three washed-rind cheese communities collected cheese ripening.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"data","dir":"Articles","previous_headings":"","what":"Data","title":"Case Study: Cheese Communities","text":"analysis, use data set contained study Smith et al. (2022) microbial communities cheese. original research investigated successional dynamics occur within cheese rind microbial communities using combination 16S rRNA amplicon, Illumina, PacBio sequencing. functionally taxonomically annotate (using eggNOG (21) MMseqs2 (22)) contigs generated Illumina reads, demonstrate utility MolPad using single-omic. Specifically, focus Cheese Sample Cheese Sample C. detailed information attached data, please check documentation.","code":"data(\"cheese\") str(cheese) #> tibble [106,239 × 18] (S3: tbl_df/tbl/data.frame) #> $ ID : chr [1:106239] \"1\" \"2\" \"3\" \"4\" ... #> $ A_1 : int [1:106239] 38 23 24 3 58 12 1 14 1 3 ... #> $ A_2 : int [1:106239] 23 6 5 2 14 9 1 7 1 1 ... #> $ A_3 : int [1:106239] 27 4 37 4 45 14 0 14 3 5 ... #> $ A_4 : int [1:106239] 5 0 10 2 13 4 1 4 4 0 ... #> $ A_5 : int [1:106239] 11 9 19 16 32 13 0 4 1 1 ... #> $ C_1 : int [1:106239] 13 21 3 56 82 2 4 17 7 2 ... #> $ C_3 : int [1:106239] 1 1 0 1 3 0 0 0 0 1 ... #> $ C_4 : int [1:106239] 3 0 1 7 8 1 0 1 2 2 ... #> $ C_5 : int [1:106239] 0 2 4 17 3 4 0 1 8 6 ... #> $ GO_ID : chr [1:106239] NA NA NA NA ... #> $ KEGG_ID: chr [1:106239] NA NA NA NA ... #> $ domain : chr [1:106239] NA NA NA NA ... #> $ phylum : chr [1:106239] NA NA NA NA ... #> $ class : chr [1:106239] NA NA NA NA ... #> $ order : chr [1:106239] NA NA NA NA ... #> $ family : chr [1:106239] NA NA NA NA ... #> $ genus : chr [1:106239] NA NA NA NA ... str(annotations) #> tibble [86,156 × 9] (S3: tbl_df/tbl/data.frame) #> $ ID : chr [1:86156] \"9\" \"10\" \"11\" \"12\" ... #> $ GO_ID : chr [1:86156] \"-\" \"-\" \"-\" \"-\" ... #> $ KEGG_ID: chr [1:86156] \"-\" \"-\" \"-\" \"-\" ... #> $ domain : chr [1:86156] \"Bacteria\" \"Bacteria\" \"Bacteria\" \"Bacteria\" ... #> $ phylum : chr [1:86156] \"Pseudomonadota\" \"Pseudomonadota\" \"Pseudomonadota\" \"Pseudomonadota\" ... #> $ class : chr [1:86156] \"Alphaproteobacteria\" \"Alphaproteobacteria\" \"Alphaproteobacteria\" \"Alphaproteobacteria\" ... #> $ order : chr [1:86156] \"Caulobacterales\" \"Caulobacterales\" \"Hyphomicrobiales\" \"Hyphomicrobiales\" ... #> $ family : chr [1:86156] \"Caulobacteraceae\" \"Caulobacteraceae\" \"Bartonellaceae\" \"Brucellaceae;-_Brucella/Ochrobactrum group\" ... #> $ genus : chr [1:86156] \"Caulobacter\" \"Caulobacter;-_unclassified Caulobacter\" \"Bartonella\" \"Brucella\" ..."},{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"data-and-annotations","dir":"Articles","previous_headings":"Data > Pre-process","what":"Data and Annotations","title":"Case Study: Cheese Communities","text":"select ‘type’ column phylum describe characteristic cheese data. Also, columns phylum class taken tags elemental composition. section, introduce data preparing steps analysis. annotations dataset contains various columns describe characteristics properties samples. First, select ‘type’ column phylum provide broad categorization microbial communities present cheese surface. categorization helps understanding overall composition diversity microbes high taxonomic level. run pre_process() function clean standardize data. annotate dataset, also use columns phylum class tags elemental composition microbial communities. phylum column represents major taxonomic rank, giving us broad overview microbial distribution. class column provides detailed information, allowing us delve deeper specific types microbes present. pre-processing, two datasets put dashboard look like:","code":"cheesedata <- cheese |> select(ID, A_1:C_5, phylum) |> rename(type=phylum) |> pre_process() pathchee <- gAnnotation(annotations,\"phylum\",\"class\") # data cheesedata[112:115,] #> # A tibble: 4 × 11 #> ID A_1 A_2 A_3 A_4 A_5 C_1 C_3 C_4 C_5 type #> #> 1 112 0.943 -0.471 -0.471 -0.471 -0.471 2.36 -0.471 -0.471 -0.471 Other #> 2 113 0.786 -0.124 0.126 -0.623 -0.637 2.33 -0.667 -0.593 -0.593 Other #> 3 114 1.22 -1.09 -0.430 -0.829 -0.170 1.43 -1.30 0.455 0.715 Other #> 4 115 2.67 -0.333 -0.333 -0.333 -0.333 -0.333 -0.333 -0.333 -0.333 Other # annotation pathchee[112:115,] #> # A tibble: 4 × 9 #> ID GO_ID KEGG_ID domain Pathway taxonomic.scope order family genus #> #> 1 145 - ko:K00004 Bacte… Pseudo… Unknown NA NA NA #> 2 147 - ko:K00004 Bacte… Actino… Actinomycetes NA NA NA #> 3 148 - ko:K00004 Bacte… Bacill… Bacilli Baci… Bacil… Lysi… #> 4 149 - ko:K00004,ko:K0… Bacte… Actino… Actinomycetes NA NA NA"},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"cluster-input","dir":"Articles","previous_headings":"Data","what":"Cluster Input","title":"Case Study: Cheese Communities","text":"section, generate clusters first dataset using gClusters function. function takes cheese dataset (cheesedata) input generates clusters based specified parameters. , set number clusters 10 (ncluster = 10) specify maximum number clusters consider determining optimal number clusters (elbow.max=15).","code":"cluschee <- gClusters(cheesedata,ncluster = 10,elbow.max=15)"},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"network-input","dir":"Articles","previous_headings":"Data","what":"network input","title":"Case Study: Cheese Communities","text":"generating clusters major patterns, proceed obtain network results clusters. Taking cluster centroids nodes, prediction process edges divided individual regression tasks, cluster centroid independentally predicted expression cluster centroids, using random forests. pick top 3 related predictors cluster centroid save network output future use. achieved using gNetwork() function. gain insight network results, can visualize details using gNetwork_view() function, shown .","code":"networkchee <- gNetwork(cluschee,ntop = 3) gNetwork_view(networkchee)"},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"run-dashboard","dir":"Articles","previous_headings":"","what":"Run Dashboard","title":"Case Study: Cheese Communities","text":"clusters network results obtained, can proceed run dashboard. involves declaring annotations executing dashboard using gDashboard() function. , pass cheese dataset (cheesedata), cluster results (cluschee), network results (networkchee), specify column names types annotation identifiers.","code":"gDashboard(cheesedata, cluschee, pathchee, networkchee, id_colname = c(\"GO_ID\",\"KEGG_ID\"), id_type = c(\"GO\",\"KEGG\"))"},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"installation","dir":"Articles","previous_headings":"","what":"Installation","title":"Get started","text":"can either install MolPad devtools, clone repository local computer (example, onto ./Github) installing: , load package:","code":"# Install the package in R: install.packages(\"devtools\") library(devtools) install_github(\"KaiyanM/MolPad\") # Download through Github and install the package in R: setwd(\"./GitHub\") install(\"MolPad\") library(MolPad)"},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"before-input","dir":"Articles","previous_headings":"","what":"Before Input","title":"Get started","text":"recommend carefully reviewing data considering applying normalization (standardization, quantile normalization, log transformation, …) imputation (KNN, PMM, Mean/Median/Mode, …) address issues related library size missing data beforehead, otherwise pre_processing() function default converting NAs 0s.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"tips-for-normalization-and-imputation","dir":"Articles","previous_headings":"Before Input","what":"Tips for normalization and imputation","title":"Get started","text":"choose normalization method, first consider data distribution. Use Z-score normalization data normally distributed, log transformation robust scaling ’s skewed. analysis requires data within specific range, like [0, 1], opt Min-Max normalization. datasets outliers, robust scaling rank normalization better choices. selecting imputation method, assess amount pattern missing data. small amount missing data, simple methods like mean, median, mode imputation effective. missing data related variables, consider sophisticated approaches like Predictive Mean Matching k-NN imputation. computational resources concern, simpler imputation methods might practical, resources allow, advanced methods can improve data quality. ’s hard decide, can always experiment different methods use cross-validation compare effectiveness. Let choice guided domain knowledge specific requirements analysis increase quality following steps.","code":""},{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"multi-omics-yes-or-no","dir":"Articles","previous_headings":"Data Format","what":"Multi-omics: Yes or No","title":"Get started","text":"visualization pipeline starts pre-processing module includes built-functions reshape data inputs standard format streamline data preparation. Depending nature datasets, two primary conditions consider: research object includes multi-omics measurements, assume provide list dataframes, collected different omics type. example, might datasets peptides, metabolites, lipids. combine together standard long dataset row represents feature, simply run pre_process() function, conduct data preparation yield standard input format demonstrated . Datasets multi-omics, can still utilize dashboard long ensuring data inputs reformatted standard longitudinal format. situation, can manually assign type column category label describe major groups data, input data pre_processing() module data preparation . case study, utilized “Kingdom” type label column cheese data. datasets multi-omics, can simply apply dashboard reformatting data standard longitudinal format. case, manually assign category label type column represent major groups. can still input data pre_processing() module cleaning preparation. case study, used “Kingdom” type label cheese datasets.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"data-format-and-pre-process","dir":"Articles","previous_headings":"Data Format","what":"Data Format and Pre-process","title":"Get started","text":"figure, , B, C can represent datasets different sources different aspects measurements. two examples input data might look like transform required format pre_process() modules.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"if-your-input-is-a-list-of-datasets","dir":"Articles","previous_headings":"Data Format > Data Format and Pre-process","what":"If your input is a list of datasets:","title":"Get started","text":"list datasets, following format: ID column identical column names. pre_process():","code":"str(fungal_dlist) #> List of 3 #> $ :'data.frame': 149786 obs. of 12 variables: #> ..$ ID : chr [1:149786] \"grp1_1\" \"grp1_2\" \"grp1_6\" \"grp1_7\" ... #> ..$ D0_01 : num [1:149786] -0.33 -0.33 1.591 1.106 0.659 ... #> ..$ D1_02 : num [1:149786] 0.628 0.628 3.087 3.618 2.843 ... #> ..$ D2_03 : num [1:149786] -1.223 -1.223 0.846 1.178 1.435 ... #> ..$ D3_04 : num [1:149786] -2.411 -1.808 1.239 1.753 0.258 ... #> ..$ D4_05 : num [1:149786] -1.41 -1.18 2.05 1.64 1.21 ... #> ..$ D5_06 : num [1:149786] -2.245 -2.309 0.83 1.64 0.395 ... #> ..$ D6_07 : num [1:149786] -0.784 -1.125 2.758 2.888 2.516 ... #> ..$ D7_08 : num [1:149786] -2.61197 -2.68724 0.42236 0.92846 0.00892 ... #> ..$ D8_09 : num [1:149786] -0.951 -1.3 2.417 2.454 1.418 ... #> ..$ D9_10 : num [1:149786] -1.91 -2.61 1.33 1.8 2.25 ... #> ..$ D10_11: num [1:149786] -2.871 -3.417 1.101 0.619 1.275 ... #> $ :'data.frame': 150 obs. of 12 variables: #> ..$ ID : chr [1:150] \"grp2_1\" \"grp2_2\" \"grp2_3\" \"grp2_4\" ... #> ..$ D0_01 : num [1:150] 0.947 1.012 0.156 0.31 0.138 ... #> ..$ D1_02 : num [1:150] -1.616 -0.917 -1.581 -2.717 -2.578 ... #> ..$ D2_03 : num [1:150] -2.4 -1.67 -2.31 -3.02 -3.66 ... #> ..$ D3_04 : num [1:150] -0.5734 0.0336 -0.351 -1.3052 -1.737 ... #> ..$ D4_05 : num [1:150] 0.662 0.854 1.002 0.12 -0.371 ... #> ..$ D5_06 : num [1:150] -1.09 -1.28 -1.04 -1.82 -3.15 ... #> ..$ D6_07 : num [1:150] -0.212 0.718 0.189 -0.627 -1.081 ... #> ..$ D7_08 : num [1:150] 0.969 2.117 1.462 0.339 -0.038 ... #> ..$ D8_09 : num [1:150] -0.712 0.43 -0.281 -1.297 -1.822 ... #> ..$ D9_10 : num [1:150] -0.649 0.45 -0.124 -1.77 -1.962 ... #> ..$ D10_11: num [1:150] -1.05 -0.85 -1.58 -1.91 -4.05 ... #> $ :'data.frame': 369 obs. of 12 variables: #> ..$ ID : chr [1:369] \"grp3_1\" \"grp3_2\" \"grp3_3\" \"grp3_4\" ... #> ..$ D0_01 : num [1:369] -0.918 -2.112 -1.372 -0.4 -1.75 ... #> ..$ D1_02 : num [1:369] -1.219 -3.502 -1.504 -0.615 -2.11 ... #> ..$ D2_03 : num [1:369] -0.542 -2.324 -0.592 0.161 -1.342 ... #> ..$ D3_04 : num [1:369] -0.101 -1.495 0.1 0.603 -1.058 ... #> ..$ D4_05 : num [1:369] 1.715 1.557 1.89 2.296 0.653 ... #> ..$ D5_06 : num [1:369] -1.452 -2.37 -1.182 -0.846 -2.531 ... #> ..$ D6_07 : num [1:369] 0.0879 -0.9982 0.4382 0.6215 -0.9464 ... #> ..$ D7_08 : num [1:369] 0.0502 0.4668 0.5378 0.8353 -0.9213 ... #> ..$ D8_09 : num [1:369] -0.0268 -0.4226 0.4528 0.6328 -1.0998 ... #> ..$ D9_10 : num [1:369] -0.5807 -1.7755 -0.1956 0.0866 -1.5876 ... #> ..$ D10_11: num [1:369] 0.118 -0.16 0.687 0.572 -1.037 ... #> ID D0_01 D1_02 D2_03 D3_04 D4_05 D5_06 #> 1 grp1_1 -0.3297741 0.6284682 -1.2232968 -2.411339 -1.413062 -2.2445487 #> 2 grp1_2 -0.3297741 0.6284682 -1.2232968 -1.807533 -1.183498 -2.3088022 #> 3 grp1_6 1.5908054 3.0871284 0.8460835 1.239411 2.054109 0.8300876 #> 4 grp1_7 1.1061795 3.6176250 1.1781012 1.753078 1.643661 1.6400102 #> D6_07 D7_08 D8_09 D9_10 D10_11 #> 1 -0.7839412 -2.6119663 -0.9511467 -1.914273 -2.8711810 #> 2 -1.1251434 -2.6872417 -1.2996614 -2.610647 -3.4165861 #> 3 2.7576288 0.4223618 2.4165602 1.333387 1.1012825 #> 4 2.8877343 0.9284640 2.4538561 1.799019 0.6187648 #> ID D0_01 D1_02 D2_03 D3_04 D4_05 D5_06 #> 1 grp2_1 0.9467431 -1.6162900 -2.400394 -0.57337845 0.6616711 -1.090914 #> 2 grp2_2 1.0120048 -0.9165994 -1.674330 0.03361378 0.8540950 -1.283964 #> 3 grp2_3 0.1561394 -1.5805490 -2.307281 -0.35095093 1.0018741 -1.035030 #> 4 grp2_4 0.3104578 -2.7173471 -3.022886 -1.30521496 0.1198075 -1.816677 #> D6_07 D7_08 D8_09 D9_10 D10_11 #> 1 -0.2116778 0.9688582 -0.7123773 -0.6489204 -1.0504845 #> 2 0.7181970 2.1174460 0.4302981 0.4504066 -0.8503481 #> 3 0.1885422 1.4619677 -0.2805926 -0.1235090 -1.5757556 #> 4 -0.6271954 0.3393109 -1.2967938 -1.7697248 -1.9073101 #> ID D0_01 D1_02 D2_03 D3_04 D4_05 D5_06 #> 1 grp3_1 -0.9178218 -1.2193128 -0.5415780 -0.1007493 1.715495 -1.4515238 #> 2 grp3_2 -2.1120636 -3.5015326 -2.3236475 -1.4949366 1.557463 -2.3698578 #> 3 grp3_3 -1.3724446 -1.5043101 -0.5922380 0.1004978 1.889573 -1.1815469 #> 4 grp3_4 -0.4002445 -0.6145252 0.1613003 0.6031744 2.295751 -0.8464012 #> D6_07 D7_08 D8_09 D9_10 D10_11 #> 1 0.08792489 0.05020749 -0.02681783 -0.58065562 0.1177374 #> 2 -0.99822332 0.46681389 -0.42255125 -1.77554842 -0.1603138 #> 3 0.43822354 0.53780990 0.45276561 -0.19558682 0.6870272 #> 4 0.62145080 0.83528878 0.63275758 0.08663959 0.5720591 data <- pre_process(fungal_dlist, typenameList = c('DataSource_A','DataSource_B','DataSource_C'), replaceNA = TRUE, scale = TRUE, autoColName = \"Section_\") #> [1] \"Reformat a list of datasets:\" head(data,10) #> ID D0_01 D1_02 D2_03 D3_04 D4_05 D5_06 #> 1 grp1_1 1.06146675 1.956656 0.2267383 -0.88312967 0.04945980 -0.7273146 #> 2 grp1_2 1.08465667 1.916983 0.3085454 -0.19892034 0.34311497 -0.6343214 #> 3 grp1_6 -0.01899274 1.717871 -0.8834318 -0.42687491 0.51878884 -0.9019992 #> 4 grp1_7 -0.75869657 2.051470 -0.6782202 -0.03485388 -0.15728499 -0.1613700 #> 5 grp1_8 -0.68132827 1.648062 0.1465161 -1.10875822 -0.09101615 -0.9629427 #> 6 grp1_11 0.27709905 1.981042 -0.5786717 0.02888008 0.54647685 -0.7013406 #> 7 grp1_12 0.90487968 2.180079 -0.6439891 -0.14841544 -0.69040101 -0.3834938 #> 8 grp1_13 0.46335031 1.695812 -1.0938459 -0.76868293 0.74415950 -0.6861036 #> 9 grp1_14 0.72014702 1.708604 -0.9344900 -0.35796963 0.63730277 -0.9872778 #> 10 grp1_15 0.54526798 1.784716 -0.8846753 -0.49538649 0.62916563 -0.9742293 #> D6_07 D7_08 D8_09 D9_10 D10_11 type #> 1 0.6371841 -1.0705560 0.4809808 -0.41877137 -1.31271417 DataSource_A #> 2 0.3938012 -0.9630327 0.2422154 -0.89650312 -1.59653914 DataSource_A #> 3 1.3354026 -1.3752687 0.9395058 -0.31779210 -0.58720856 DataSource_A #> 4 1.2347631 -0.9575502 0.7492778 0.01655139 -1.30408627 DataSource_A #> 5 1.2995756 -1.3741657 0.1285139 1.01932257 -0.02377948 DataSource_A #> 6 1.1406352 -1.6339536 0.2041455 -0.45793129 -0.80638109 DataSource_A #> 7 0.4794959 -1.6340692 0.5360766 -0.31430867 -0.28585374 DataSource_A #> 8 1.2235141 -1.2241931 0.6790383 -0.28487806 -0.74817082 DataSource_A #> 9 1.1466328 -1.4265286 0.5357111 -0.51799361 -0.52413804 DataSource_A #> 10 1.1813273 -1.2944121 0.6417621 -0.46906150 -0.66447461 DataSource_A"},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"if-your-input-is-a-single-dataset","dir":"Articles","previous_headings":"Data Format > Data Format and Pre-process","what":"If your input is a single dataset:","title":"Get started","text":"dataset already includes labeled ‘type’ column: pre_process():","code":"#> ID Day_1 Day_2 Day_3 Day_4 Day_5 Day_6 #> 1 1 NA NA 0.104083550 -1.07999900 1.3416214 -1.733564471 #> 2 2 NA NA 0.882578509 0.08440181 0.7165712 0.004811372 #> 3 3 0.40203917 NA 0.675076590 0.97017740 -0.8210872 0.659073443 #> 4 4 1.02859068 0.30178418 0.412617771 0.39306149 -0.3427640 0.837009714 #> 5 5 -2.14036218 -0.06315709 0.656982200 -0.18301850 -1.3967562 -0.123327229 #> 6 6 -2.04354192 1.30960484 1.371349945 -1.67403067 -1.2946150 0.760315504 #> 7 7 0.31164367 2.26615374 0.972843085 -0.03956293 1.0190965 1.180680430 #> 8 8 -0.79692858 -0.86038791 -3.417872877 0.62118867 -2.1983197 -0.104229763 #> 9 9 -0.12033042 1.17585682 -0.001075426 0.01122359 0.3430992 -0.189234532 #> 10 10 0.02592915 0.48551867 0.841254390 -0.46430744 0.6891800 0.751766756 #> Day_7 Day_8 type #> 1 0.4638261 2.1318716 peptide #> 2 -0.2949926 -0.9310538 peptide #> 3 -0.2520195 0.7809977 peptide #> 4 -1.0120371 -0.1162361 peptide #> 5 -2.0085970 -0.8959521 peptide #> 6 0.7874222 -0.5106016 peptide #> 7 -0.7719437 -1.4686258 peptide #> 8 0.3541200 0.8224521 lipid #> 9 0.5674059 -0.9313882 lipid #> 10 -0.4097464 NA metabolite x1 <- pre_process(x, replaceNA = TRUE,scale = TRUE) head(x1,10) #> ID Day_1 Day_2 Day_3 Day_4 Day_5 Day_6 #> 1 1 -0.12499488 -0.1249949 -0.04022866 -1.00455207 0.9676290 -1.53681943 #> 2 2 -0.10246242 -0.1024624 1.46237249 0.04718422 1.1680372 -0.09393173 #> 3 3 0.16412393 -0.4940273 0.61109508 1.09418472 -1.8381738 0.58489741 #> 4 4 1.27432720 0.1728189 0.34079228 0.31115385 -0.8040231 0.98397755 #> 5 5 -1.35954882 0.7001734 1.41425156 0.58132077 -0.6222015 0.64050965 #> 6 6 -1.35505505 1.0595199 1.10398207 -1.08897287 -0.8157586 0.66398092 #> 7 7 -0.10291149 1.5438734 0.45418630 -0.39882285 0.4931574 0.62930098 #> 8 8 -0.06748422 -0.1105542 -1.84632390 0.89499455 -1.0186109 0.40265173 #> 9 9 -0.36947382 1.7376966 -0.17560476 -0.15561064 0.3839091 -0.48148903 #> 10 10 -0.41003145 0.4704748 1.15201231 -1.34925296 0.8606601 0.98056711 #> Day_7 Day_8 type #> 1 0.2527477 1.6112132 peptide #> 2 -0.6254920 -1.7532453 peptide #> 3 -0.9065915 0.7844915 peptide #> 4 -1.8183370 -0.4607098 peptide #> 5 -1.2288927 -0.1256124 peptide #> 6 0.6835002 -0.2511966 peptide #> 7 -1.0158949 -1.6028889 peptide #> 8 0.7137346 1.0315924 lipid #> 9 0.7485573 -1.6879846 lipid #> 10 -1.2447220 -0.4597079 metabolite"},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"choose-your-annotation","dir":"Articles","previous_headings":"","what":"Choose Your Annotation","title":"Get started","text":"addition specifying data type mentioned , methods support three levels information: functional annotation, taxonomy annotation, feature annotation. annotations matched ID columns annotation data, serving another crucial input generating dashboard. facilitate automatic feature link generation using KeggID GOID, users set corresponding column names beforehand. example annotation formats align data inputs. data two layers labels (like cheese dataset), can specify two layers want focus network using gAnnotation() function.","code":"#> ID taxonomic.scope ko_term Pathway #> 1 grp1_24 Bacteria K02706 00195_Photosynthesis #> 2 grp1_25 Bacteria K02706 00195_Photosynthesis #> 3 grp1_26 Bacteria K02706 00195_Photosynthesis #> 4 grp1_1075 Bacteria K02112 00195_Photosynthesis #> 5 grp1_1076 Bacteria K02112 00195_Photosynthesis #> 6 grp1_1358 Bacteria K02112 00195_Photosynthesis Annotation_path_taxon <- gAnnotation(annotations,\"phylum\",\"class\")"},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"network-generation","dir":"Articles","previous_headings":"","what":"Network Generation","title":"Get started","text":"generate network, first scale cluster trajectories across input molecular features. Trajectories clustered using K-means, built-elbow method used select optimal K. build network clustered trajectories adapting GENIE3 algorithm. Specifically, network learned series regressions, cluster centroid predicted expression patterns cluster centroids using random forests. choose random forests potential model interacting features non-linearity without strong assumptions. determine feature clusters predictive given trajectory, Mean Decrease Accuracy subset top predictors whose expression directly influences expression target cluster taken indication putative link.","code":"cluster_result <- data |> gClusters(ncluster = 5,elbow.max=15) network_result <- cluster_result |> gNetwork(ntop = 3) gNetwork_view(network_result)"},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"dashboard-navigation","dir":"Articles","previous_headings":"","what":"Dashboard Navigation","title":"Get started","text":"workflow inputs related gDashboard function. key function processes data runs dashboard, requiring input previous steps. ’ve launched Shiny dashboard, can zoom make adjustments explore interesting findings within data. effectively navigate dashboard generated MolPad, ’ll follow three main steps:","code":"gDashboard(data, cluster_result, Annotation_path_taxon, network_result, id_colname = \"ko_term\" , id_type = \"KEGG\")"},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"choose-a-primary-functional-annotation-and-adjust-edge-density","dir":"Articles","previous_headings":"Dashboard Navigation","what":"Choose a Primary Functional Annotation and Adjust Edge Density","title":"Get started","text":"Start selecting primary functional annotation available options. , fine-tune edge density adjusting threshold value importance score. Nodes turn bright green indicate clusters containing features related chosen functional annotation.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"explore-the-network","dir":"Articles","previous_headings":"Dashboard Navigation","what":"Explore the Network","title":"Get started","text":"Brushing network unveils patterns taxonomic composition typical trajectories. can also zoom specific taxonomic annotations applying filters.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"investigate-feature-details-and-related-function-annotations","dir":"Articles","previous_headings":"Dashboard Navigation","what":"Investigate Feature Details and Related Function Annotations","title":"Get started","text":"Delve feature table examine specifics features within selected clusters. Explore additional related function annotations using drop-options. Click provided links access online information items interest. interface encourages iterative exploration, enabling conduct multiple steps answer specific questions, comparing pattern distributions two functions identifying functionally important community members metabolizing feature interest.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"related-visualization-outputs","dir":"Articles","previous_headings":"","what":"Related Visualization Outputs","title":"Get started","text":"following visualizations integrated dashboard, ’s need run function plot individually. ’ve linked stacked bar plot ribbon plot network facilitate focus context explorations. setup helps detecting similar longitudinal patterns understanding relationships.","code":""},{"path":[]},{"path":[]},{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/articles/whymolpad.html","id":"why-molpad","dir":"Articles","previous_headings":"","what":"Why MolPad?","title":"Why MolPad?","text":"aim network interpretation, MolPad shows improvements 3 important aspects: Capture groups features share longitudinal patterns functional properties within network. Annotate network complex experimental datasets, way dig specific components notable features. Streamlines exploration modularized functions interactive dashboard. package especially facilitates exploration longitudinal molecular co-expression microbiome experiments using focus-plus-context approach, empowering researchers uncover global patterns insights longitudinal microbiomics data. Unlike existing tools, MolPad stands rich network interactivity support contextualization. also equipped beginner-friendly interface self-contained data processing functions, making accessible users limited programming experience.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/whymolpad.html","id":"key-features","dir":"Articles","previous_headings":"","what":"Key Features","title":"Why MolPad?","text":"Trajectory clustering organizing data clusters, helps identify patterns, relationships, underlying structures within dataset. essence, clustering detects groups features observations behave similarly, allowing deeper insights effective decision-making. Network extracting build network clustered trajectories adapting GENIE3 algorithm. Specifically, network learned series regressions, cluster centroid predicted expression patterns cluster centroids using random forests. choose random forests potential model interacting features non-linearity without strong assumptions. Interactive Dashboard package provides interactive dashboard designed help users explore data uncover relationships within complex networks. utilizing focus-plus-context visualization, dashboard allows users zoom specific details keeping broader dataset view. approach enables deeper understanding data, making easier identify patterns make informed decisions.","code":""},{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/articles/whymolpad.html","id":"molpad-dashboard-overview","dir":"Articles","previous_headings":"What does it look like?","what":"MolPad Dashboard Overview","title":"Why MolPad?","text":"overview MolPad Dashboard. explore dashboard effectively, can start following sequence -B-C-D. approach guide cluster-level network, taxonomic-level bar plot, type-level line plot, feature-level table. , can modify detailed settings identify investigate special patterns within data.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/whymolpad.html","id":"discovering-related-patterns","dir":"Articles","previous_headings":"What does it look like?","what":"Discovering related patterns","title":"Why MolPad?","text":"short example discovering related patterns using network plot: shade edges represents proximity nodes. brushed area, Groups 1-7-8 (circled solid black lines) Groups 1-2 (circled blue dashed lines) strongly linked. Groups 1, 7, 8, patterns form W-shape noticeable peak time section. Although Group 1 shows higher volatility compared Group 2, groups display highly overlapping increasing trend. detailed instructions, please visit “Get Started” page relevant function reference page.","code":""},{"path":"https://kaiyanm.github.io/MolPad/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Kaiyan Ma. Author, maintainer.","code":""},{"path":"https://kaiyanm.github.io/MolPad/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Ma K (2024). MolPad: MolPad: R-Shiny Package Cluster Co-Expression Analysis Longitudinal Microbiomics. R package version 0.1.0, https://kaiyanm.github.io/MolPad/.","code":"@Manual{, title = {MolPad: MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics}, author = {Kaiyan Ma}, year = {2024}, note = {R package version 0.1.0}, url = {https://kaiyanm.github.io/MolPad/}, }"},{"path":"https://kaiyanm.github.io/MolPad/index.html","id":"molpad-","dir":"","previous_headings":"","what":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","title":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","text":"R-Shiny Package Cluster Co-Expression Analysis Longitudinal Microbiomics","code":""},{"path":"https://kaiyanm.github.io/MolPad/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","text":"MolPad offers visualization dashboard tool designed enhance understanding molecular co-expression works context microbiome data. approach involves using cluster network provide initial overview relationships across multiple omics, added functionality interactively zoom specific areas interest. facilitate analysis, ’ve developed focus-plus-context strategy connects online curated annotations. Additionally, package simplifies entire pipeline creating dashboard. user-friendly design makes accessible even people limited R programming experience. Check cheese-data demo .","code":""},{"path":"https://kaiyanm.github.io/MolPad/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","text":"can either install devtools, {r, eval = FALSE} # Install package R: install.packages(\"devtools\") library(devtools) install_github(\"KaiyanM/MolPad\") clone repository local computer (example, onto ./Github) installing: {r, eval = FALSE} # Download install package R: setwd(\"./GitHub\") install(\"MolPad\") , load package: {r,eval=FALSE} library(MolPad)","code":""},{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/index.html","id":"molpad-could-help-you-with","dir":"","previous_headings":"Usage","what":"MolPad could help you with:","title":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","text":"Clustering data k-means building group network. Find significant trend patterns datasets. Target interaction groups, taxons, pathways. Visualize distribution features specific pathways group network. Search particular features user-defined labels. Check detailed information feature automatically generated hyperlinks. better overall understanding datasets.","code":""},{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/index.html","id":"getting-help","dir":"","previous_headings":"","what":"Getting Help","title":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","text":"need assistance MolPad, two primary ways seek help: Ask us anything related MolPad! add question, create issue repository. Stack Overflow another excellent resource answering common issues R. Remember ’s particularly effective can provide reproducible example shows specific problem ’re .","code":""},{"path":"https://kaiyanm.github.io/MolPad/index.html","id":"contribution","dir":"","previous_headings":"","what":"Contribution","title":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","text":"contribute project, use following workflow: fork repository –> create local copy –> submit pull request.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":null,"dir":"Reference","previous_headings":"","what":"Cheese data — cheese","title":"Cheese data — cheese","text":"context cheese production, regular application brine solution maturation technique promotes uniformity microbial populations cheese's surface facilitates interactions among microorganisms. investigation involved analysis longitudinal dataset encompassing three washed-rind cheese communities sampled ripening process.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Cheese data — cheese","text":"two datasets: cheese annotations","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Cheese data — cheese","text":"Reference: doi:10.1128/msystems.00701-22","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Cheese data — cheese","text":"study uncovered remarkably consistent microbial progression within cheese. bacterial community, Firmicutes dominate outset, Proteobacteria swiftly assuming dominance end ripening period. Additionally, Cheese Cheese C consistently demonstrate establishment Actinobacteria Bacteroidetes, distinct manner. corroborate findings using MolPad dashboard, conducted analysis two cheeses (C) three production batches, spanning weeks 2 13.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":"cheese-data","dir":"Reference","previous_headings":"","what":"cheese data","title":"Cheese data — cheese","text":"data frame 106239 rows 18 variables: ID sample ID A_1~A_5 Time series data measured cheese . C_1~C_5 Time series data measured cheese C. domain category feature phylum category feature class category feature order category feature family category feature genus category feature","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":"annotation-data","dir":"Reference","previous_headings":"","what":"annotation data","title":"Cheese data — cheese","text":"data frame 86156 rows 9 variables: ID sample ID GO_ID GO IDs, represents link gene product type molecular function KEGG_ID KEGG IDs, linking genomic information higher order functional information. domain category feature phylum category feature class category feature order category feature family category feature genus category feature","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cheese data — cheese","text":"","code":"data(cheese) head(annotations) #> # A tibble: 6 × 9 #> ID GO_ID KEGG_ID domain phylum class order family genus #> #> 1 9 - - Bacteria Pseudomonadota Alphaproteobac… Caul… Caulo… Caul… #> 2 10 - - Bacteria Pseudomonadota Alphaproteobac… Caul… Caulo… Caul… #> 3 11 - - Bacteria Pseudomonadota Alphaproteobac… Hyph… Barto… Bart… #> 4 12 - - Bacteria Pseudomonadota Alphaproteobac… Hyph… Bruce… Bruc… #> 5 13 - - Bacteria Pseudomonadota Alphaproteobac… Hyph… Nitro… Brad… #> 6 14 - - Bacteria Pseudomonadota Alphaproteobac… Hyph… Phyll… Meso… head(cheese) #> # A tibble: 6 × 18 #> ID A_1 A_2 A_3 A_4 A_5 C_1 C_3 C_4 C_5 GO_ID KEGG_ID #> #> 1 1 38 23 27 5 11 13 1 3 0 NA NA #> 2 2 23 6 4 0 9 21 1 0 2 NA NA #> 3 3 24 5 37 10 19 3 0 1 4 NA NA #> 4 4 3 2 4 2 16 56 1 7 17 NA NA #> 5 5 58 14 45 13 32 82 3 8 3 NA NA #> 6 6 12 9 14 4 13 2 0 1 4 NA NA #> # ℹ 6 more variables: domain , phylum , class , order , #> # family , genus "},{"path":"https://kaiyanm.github.io/MolPad/reference/color_palettes__.html","id":null,"dir":"Reference","previous_headings":"","what":"Color palettes — color_palettes__","title":"Color palettes — color_palettes__","text":"internal function built-color palettes.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/color_palettes__.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Color palettes — color_palettes__","text":"","code":"color_palettes__(name)"},{"path":"https://kaiyanm.github.io/MolPad/reference/color_palettes__.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Color palettes — color_palettes__","text":"name string two options: \"graytone\" \"darkwarm\".","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/color_palettes__.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Color palettes — color_palettes__","text":"","code":"color_palettes__(\"graytone\") #> [1] \"#c3c5c7\" \"#30beba\" \"#998a73\" \"#807566\" \"#a5b1c9\" \"#5d5232\" \"#9a2b41\" #> [8] \"#d6b7a2\" \"#882db4\" \"#b47a53\" color_palettes__(\"darkwarm\") #> [1] \"#251305\" \"#C70A80\" \"#FBCB0A\" \"#ff1122\" \"#7D7463\" \"#CECE5A\" \"#FF9B9B\" #> [8] \"#A459D1\" \"#00235B\" \"#7E1717\""},{"path":"https://kaiyanm.github.io/MolPad/reference/convert_range.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert range — convert_range","title":"Convert range — convert_range","text":"internal function range conversion. element x, returns distance minimal values divided range.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/convert_range.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert range — convert_range","text":"","code":"convert_range(x)"},{"path":"https://kaiyanm.github.io/MolPad/reference/convert_range.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert range — convert_range","text":"x vector numbers.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/convert_range.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert range — convert_range","text":"vector calculated (x - min(x)) / (max(x) - min(x))","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/convert_range.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert range — convert_range","text":"","code":"convert_range(5:10) #> [1] 0.0 0.2 0.4 0.6 0.8 1.0"},{"path":"https://kaiyanm.github.io/MolPad/reference/extend.color__.html","id":null,"dir":"Reference","previous_headings":"","what":"Extend color palettes — extend.color__","title":"Extend color palettes — extend.color__","text":"internal function designed pair vectors color palettes automatically generate colors longer vectors find match.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/extend.color__.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extend color palettes — extend.color__","text":"","code":"extend.color__(n, colors, extendby = 1, alpha = 1, ...)"},{"path":"https://kaiyanm.github.io/MolPad/reference/extend.color__.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extend color palettes — extend.color__","text":"n number. length feature vector want match colors. colors vector colors (finite). extendby number select 1, 2, 3, 4 5, representing distinct auto-fill schemes. alpha number select range 0,1.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gAnnotation.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate processed annotation — gAnnotation","title":"Generate processed annotation — gAnnotation","text":"gAnnotation() provides standard input format dashboard, allowing users select two columns primary factors wish visualize describe data.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gAnnotation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate processed annotation — gAnnotation","text":"","code":"gAnnotation(data, first_order, second_order)"},{"path":"https://kaiyanm.github.io/MolPad/reference/gAnnotation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate processed annotation — gAnnotation","text":"data data.frame containing annotations used describing features measured time point. also required include ID least two categorical variables. first_order string. name one column categorical variable data. second_order string. name another column categorical variable data.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gAnnotation.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate processed annotation — gAnnotation","text":"Guidelines Selecting Annotations: first-order annotation recommended functional, pathway functional system. parameter primarily serve purpose filtering one network time displaying dashboard. second-order annotation utilized illustrate composition first-order annotation using bar plot. Therefore, better set taxon, class label, etc.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gAnnotation.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate processed annotation — gAnnotation","text":"","code":"data(test_data) test_annotations_processed <- gAnnotation(test_annotations,system,class) head(test_annotations_processed) #> ID #> 1 1 #> 2 2 #> 3 3 #> 4 4 #> 5 5 #> 6 6 #> GO_ID #> 1 #> 2 #> 3 GO:0003674,GO:0003824,GO:0005575,GO:0006629,GO:0006644,GO:0006650,GO:0006655,GO:0006793,GO:0006796,GO:0008150,GO:0008152,GO:0008610,GO:0008654,GO:0008808,GO:0009058,GO:0009987,GO:0016020,GO:0016740,GO:0016772,GO:0016780,GO:0019637,GO:0030572,GO:0032048,GO:0032049,GO:0044237,GO:0044238,GO:0044249,GO:0044255,GO:0045017,GO:0046471,GO:0046474,GO:0046486,GO:0071704,GO:0090407,GO:1901576 #> 4 GO:0003674,GO:0003676,GO:0003723,GO:0003729,GO:0003824,GO:0004654,GO:0005488,GO:0005575,GO:0005622,GO:0005623,GO:0005737,GO:0006139,GO:0006401,GO:0006402,GO:0006725,GO:0006807,GO:0008150,GO:0008152,GO:0009056,GO:0009057,GO:0009892,GO:0009987,GO:0010468,GO:0010605,GO:0010629,GO:0016070,GO:0016071,GO:0016740,GO:0016772,GO:0016779,GO:0019222,GO:0019439,GO:0034641,GO:0034655,GO:0043170,GO:0044237,GO:0044238,GO:0044248,GO:0044260,GO:0044265,GO:0044270,GO:0044424,GO:0044464,GO:0046483,GO:0046700,GO:0048519,GO:0050789,GO:0060255,GO:0065007,GO:0071704,GO:0090304,GO:0097159,GO:1901360,GO:1901361,GO:1901363,GO:1901575 #> 5 #> 6 #> KEGG_ID Pathway taxonomic.scope #> 1 K07124 Integumentary System hormonal proteins #> 2 Skeletal System structural proteins #> 3 K06131 Muscular System enzymes #> 4 K00962 Nervous System contractile proteins #> 5 K02083 Endocrine System contractile proteins #> 6 Cardiovascular System structural proteins"},{"path":"https://kaiyanm.github.io/MolPad/reference/gClusters.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate clusters — gClusters","title":"Generate clusters — gClusters","text":"gClusters() returns clusters generated k-means yield elbow plot way finding optimal parameter.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gClusters.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate clusters — gClusters","text":"","code":"gClusters(data, ncluster = 20, elbow.max = 50, ...)"},{"path":"https://kaiyanm.github.io/MolPad/reference/gClusters.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate clusters — gClusters","text":"data scaled data.frame contain variables ID, value time_1, ..., value time_k, type extracting patterns across time. See also pre_process(). ncluster number clusters. related complexity information network: choosing ncluster, suggest thinking many nodes show visualization representative want clustered pattern. elbow.max number maximum value x-axis elbow method plot. larger expected ncluster smaller sample size. iter.max number maximum iterations allowed k-means. See also stats::kmeans. nstart number random attempts generating initial configurations. k-means algorithm choose best one among attempts. larger data, 'nstart' can set lower just set 1. See also stats::kmeans.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gClusters.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate clusters — gClusters","text":"function return list 2 elements: k-means cluster result elbow method plot.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gClusters.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate clusters — gClusters","text":"determine optimal number clusters (ncluster), advised closely examine elbow plot identify point graph substantial change 'elbow' occurs. often indicative suitable cluster count. cases dataset extensive intricate, might consider increasing value elbow.max ensure comprehensive exploration potential cluster counts. can help achieving accurate meaningful results, especially working larger complex datasets. function can executed data parameter outset. However, achieve best clustering results, adjustments recommended. initial run, users expected adjust function's parameters based clustering outcomes elbow plot analysis.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gClusters.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate clusters — gClusters","text":"","code":"data(test_data) reslist <- gClusters(test_data_processed) # k-means result reslist[[1]] #> K-means clustering with 20 clusters of sizes 7, 6, 7, 10, 8, 2, 3, 5, 4, 6, 10, 3, 3, 4, 4, 6, 4, 3, 2, 3 #> #> Cluster means: #> T1 T2 T3 T4 T5 T6 #> 1 2.22789830 0.02950883 -0.75836275 -0.2619747 -0.5953255 -0.70974025 #> 2 2.77349939 -0.21724464 -0.35972402 -0.4172516 -0.5000122 -0.11678355 #> 3 0.93534606 -0.39771771 0.31238452 1.1531782 1.1995301 -1.05203742 #> 4 -0.27715339 -0.39760069 -0.06466866 1.3995407 2.1316423 -0.28068841 #> 5 0.11559709 -0.19000668 -0.25629794 -0.1487697 -0.4383348 2.58301481 #> 6 -0.27847690 -0.82258408 -0.82258408 -0.8225841 1.2338761 -0.82258408 #> 7 0.26998015 -0.08094677 -0.28926312 -0.7813127 -0.6065051 -0.14945230 #> 8 -0.81355388 -0.84696982 -0.32573017 1.1218534 2.0955812 -0.56207309 #> 9 1.45173820 0.26236463 1.31600581 -0.2531012 0.2004547 -1.12389420 #> 10 0.34700978 0.13673201 0.82464908 0.2571569 1.2656031 0.63541302 #> 11 -0.19020083 -0.69654961 -0.66836949 -0.4657663 -0.3851487 -0.55904213 #> 12 1.62582942 1.51619556 -0.35316244 0.2978109 -0.1777508 0.02299732 #> 13 -0.09187847 -0.87257293 -0.87257293 -0.8725729 -0.5696817 2.05668333 #> 14 0.85679806 -0.37993358 -0.46099690 0.3934155 0.5857582 1.85027957 #> 15 1.12205113 0.09185659 -0.89815993 -0.7233153 -0.5717924 -0.86037548 #> 16 2.09193480 -0.33320285 -0.50786563 -0.5579659 -0.5579659 1.54559773 #> 17 1.56599593 -0.49119311 -0.83000857 -0.6160199 -0.2092753 1.19151568 #> 18 0.89463785 0.01698143 -0.57023879 -1.1815834 -1.1815834 1.18824796 #> 19 -0.13543375 -0.90710305 -0.66248003 -0.4178570 -0.6624800 1.37010464 #> 20 1.31631350 -0.76059916 -0.76059916 1.5193444 0.6006058 0.84385791 #> T7 T8 T9 T10 #> 1 -0.567081109 0.132060988 0.03929621 0.46372005 #> 2 -0.357532815 -0.298895212 -0.29119673 -0.21485863 #> 3 -1.222209771 -0.334925678 -0.36853535 -0.22501291 #> 4 -0.548494719 -0.646992821 -0.66392148 -0.65166279 #> 5 -0.006327004 -0.327677972 -0.66559889 -0.66559889 #> 6 -0.822584085 1.233876127 1.23387613 0.68976894 #> 7 -0.763902351 -0.412975440 0.39220455 2.42217307 #> 8 -0.651292750 -0.498839228 0.42446013 0.05656427 #> 9 -0.987867401 -0.340013670 -0.33085684 -0.19483004 #> 10 -1.004339966 -0.620506186 -0.71949086 -1.12222690 #> 11 -0.566522777 0.002653353 1.73917982 1.78976662 #> 12 -0.770498738 -0.620423681 -0.77049874 -0.77049874 #> 13 0.882337606 0.086536293 0.12686085 0.12686085 #> 14 -0.526130916 -0.896529515 -0.89652951 -0.52613092 #> 15 -0.822591022 0.091471523 1.00908283 1.56177203 #> 16 -0.425513174 -0.557965933 -0.30571480 -0.39133827 #> 17 0.774154665 0.196302112 -0.62663627 -0.95483532 #> 18 0.894637851 -0.836550610 0.41343688 0.36201421 #> 19 -0.907103054 -0.417856999 1.37010464 1.37010464 #> 20 -0.666108041 -0.666108041 -0.66610804 -0.76059916 #> #> Clustering vector: #> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 #> 11 16 6 10 10 11 13 12 4 8 3 7 15 2 3 8 5 11 9 1 #> 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 #> 17 11 5 16 2 8 20 1 5 4 17 4 10 6 4 10 3 15 11 12 #> 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 #> 5 16 11 11 14 11 5 14 16 5 1 17 18 15 5 17 13 4 1 4 #> 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 #> 3 14 20 15 10 16 2 14 9 9 7 8 5 12 7 4 3 4 3 20 #> 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 #> 3 19 1 13 19 16 9 2 18 2 4 18 4 1 11 10 8 2 11 1 #> #> Within cluster sum of squares by cluster: #> [1] 13.8467580 2.0713631 15.4102772 5.9461649 8.0412334 0.9921913 #> [7] 3.1842269 3.4617088 9.9418120 17.6305488 8.0515257 4.9390800 #> [13] 4.0041862 7.3057948 4.7723673 3.0260293 7.3290085 5.6033175 #> [19] 0.9549143 2.4583646 #> (between_SS / total_SS = 83.4 %) #> #> Available components: #> #> [1] \"cluster\" \"centers\" \"totss\" \"withinss\" \"tot.withinss\" #> [6] \"betweenss\" \"size\" \"iter\" \"ifault\" # elbow plot reslist[[2]]"},{"path":"https://kaiyanm.github.io/MolPad/reference/gDashboard.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate shiny dashboard — gDashboard","title":"Generate shiny dashboard — gDashboard","text":"outputs g-functions, ready create custom Molpad dashboard. sure specify web ID columns corresponding column names.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gDashboard.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate shiny dashboard — gDashboard","text":"","code":"gDashboard( data, cluster, annotation, networkres, dashboardtitle = \"MolPad Dashboard\", id_colname = NULL, id_type = NULL )"},{"path":"https://kaiyanm.github.io/MolPad/reference/gDashboard.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate shiny dashboard — gDashboard","text":"data output pre_process():scaled data.frame contain variables ID, value time_1, ..., value time_k, type. cluster output gClusters(): list contains result k-means Cluster means, vectors sum squares. annotation output gAnnotation(): data.frame containing annotations describing features. Variables must include ID, Pathway, taxonomic.scope. Note NA permitted 3 variables. networkres output gNetwork(): data.frame 4 variables weight,IncNodePurity,var_names,. dashboardtitle string customized dashboard name. id_colname single string string vector. column names annotation dataset vector contains external database IDs. id_type single string string vector. corresponding database names columns, must chosen \"KEGG\" \"GO\".","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gDashboard.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate shiny dashboard — gDashboard","text":"Please ensure columns containing external database IDs adhere following standards: KEGG ID: Begin 'K' followed 5 digits, example, K05685 K06671. GO ID: Begin 'GO:' followed 7 digits, GO:0003674.\"","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gDashboard.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate shiny dashboard — gDashboard","text":"","code":"if (FALSE) { data(test_data) gDashboard(test_data_processed,test_cluster,test_annotations_processed,test_network,id_colname = c(\"GO_ID\",\"KEGG_ID\"),id_type = c(\"GO\",\"KEGG\")) }"},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate prediction network — gNetwork","title":"Generate prediction network — gNetwork","text":"gNetwork() generates prediction network functional annotation. every feature, features considered independent variables, top predictors selected based %IncMSE.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate prediction network — gNetwork","text":"","code":"gNetwork(clusters, ntop = 10, method = \"randomforest\")"},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate prediction network — gNetwork","text":"clusters list two outputs gClusters(). first element k-means result (See also stats::kmeans), element plot automatically omitted ease directly passing results function. ntop number pick set top n predictors(default: top 10) feature important ones. used construct network among clustered patterns. seed number random seed.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate prediction network — gNetwork","text":"assess relationships clusters using Mean Squared Error (MSE) changes resulting random shuffling. gNetwork()'s output includes edge weights node pairs, essential inputs dashboard.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate prediction network — gNetwork","text":"","code":"data(test_data) networkres <- gNetwork(test_cluster, ntop = 3) head(networkres) #> weight IncNodePurity var_names from #> 1 1.6429045 0.7371260 Group_3 Group_1 #> 2 0.4588961 0.9310697 Group_5 Group_1 #> 3 -0.7833169 0.6761286 Group_2 Group_1 #> 4 5.0076415 0.8821582 Group_5 Group_2 #> 5 4.1618215 1.1976058 Group_4 Group_2 #> 6 2.5714583 1.2820391 Group_1 Group_2 gNetwork_view(networkres)"},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork_view.html","id":null,"dir":"Reference","previous_headings":"","what":"View the feature importance plot — gNetwork_view","title":"View the feature importance plot — gNetwork_view","text":"gNetwork_view() Generate feature importance plot random forest result. plot visually highlights importance individual cluster within dataset, helping identify key factors predictive modeling.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork_view.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"View the feature importance plot — gNetwork_view","text":"","code":"gNetwork_view(data)"},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork_view.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"View the feature importance plot — gNetwork_view","text":"data dataframe; output gNetwork(), including 4 variables: weight, IncNodePurity, var_names . weight edge value, IncNodePurity(%IncMSE) measure predictions result var_names permuted. dependent variable round random forest.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork_view.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"View the feature importance plot — gNetwork_view","text":"","code":"data(test_data) gNetwork_view(test_network) #> Warning: Removed 1 rows containing missing values (`geom_segment()`). #> Warning: Removed 1 rows containing missing values (`geom_point()`)."},{"path":"https://kaiyanm.github.io/MolPad/reference/make_line_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Make line plot — make_line_plot","title":"Make line plot — make_line_plot","text":"Generate line ribbon plot dashboard.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_line_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make line plot — make_line_plot","text":"","code":"make_line_plot(dfgroup_long, selected_groups, selected_taxa)"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_line_plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Make line plot — make_line_plot","text":"function makes ribbon plot every brush action Shiny show min, max, mean value clustered pattern group across time. ribbon plot grouped colored type variable input datasets. See also ggplot2::geom_ribbon.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_line_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Make line plot — make_line_plot","text":"","code":"data(test_data) make_line_plot(test_maindata, \"Group_5\", c(\"hormonal proteins\",\"structural proteins\",\"enzymes\",\"storage proteins\",\"antibodies\",\"transport proteins\"))"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_stackbar_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Make stackbar plot — make_stackbar_plot","title":"Make stackbar plot — make_stackbar_plot","text":"Generate stackbar plot dashboard.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_stackbar_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make stackbar plot — make_stackbar_plot","text":"","code":"make_stackbar_plot(dfgroup_long, selected_groups, selected_taxa)"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_stackbar_plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Make stackbar plot — make_stackbar_plot","text":"function makes bar plot every brush action Shiny show components clustered pattern group. bar plot colored taxonomic.scope variable processed annotation dataset generated gAnnotation(). See also ggplot2::geom_bar.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_stackbar_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Make stackbar plot — make_stackbar_plot","text":"","code":"data(test_data) make_stackbar_plot(test_maindata, \"Group_5\", c(\"hormonal proteins\",\"structural proteins\",\"enzymes\",\"storage proteins\",\"antibodies\",\"transport proteins\"))"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_graph.html","id":null,"dir":"Reference","previous_headings":"","what":"Make graph plot — make_the_graph","title":"Make graph plot — make_the_graph","text":"Generate graph dashboard.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_graph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make graph plot — make_the_graph","text":"","code":"make_the_graph(ptw, network_output, min_weight, s_ptw, graph_layout)"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_graph.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Make graph plot — make_the_graph","text":"function makes network plot every Pathway selection Shiny show relationship among clustered pattern groups. network plot built dataset generated gNetwork() can adjusted layout minimum weights. See also ggraph::ggraph.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_graph.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Make graph plot — make_the_graph","text":"","code":"data(test_data) make_the_graph(test_graphptw, test_network, 0.03, \"Muscular System\",\"kk\")"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_table.html","id":null,"dir":"Reference","previous_headings":"","what":"Make table from brushed region — make_the_table","title":"Make table from brushed region — make_the_table","text":"Generate table selected groups dashboard.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make table from brushed region — make_the_table","text":"","code":"make_the_table(p)"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Make table from brushed region — make_the_table","text":"p ggplot output, see also ggplot2::ggplot_build.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_table.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Make table from brushed region — make_the_table","text":"function simply aims collect position information brushed area network plot returns annotation table corresponding features. takes plot object produce object information table.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/mass_produce_lm__.html","id":null,"dir":"Reference","previous_headings":"","what":"Mass produce linear model — mass_produce_lm__","title":"Mass produce linear model — mass_produce_lm__","text":"Take column dependent vairable time, produce n linear models n columns dataset.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/mass_produce_lm__.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mass produce linear model — mass_produce_lm__","text":"","code":"mass_produce_lm__(data)"},{"path":"https://kaiyanm.github.io/MolPad/reference/mass_produce_lm__.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mass produce linear model — mass_produce_lm__","text":"data dataframe.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/mass_produce_lm__.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Mass produce linear model — mass_produce_lm__","text":"internal function designed automatically generate list functions regression. column considered response variable columns. resulting functions can utilized inputs random forest regression gNetwork().","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/match.color__.html","id":null,"dir":"Reference","previous_headings":"","what":"Match color — match.color__","title":"Match color — match.color__","text":"Match vector finite list colors.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/match.color__.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Match color — match.color__","text":"","code":"match.color__(valist, mycolors, extendby = 5)"},{"path":"https://kaiyanm.github.io/MolPad/reference/match.color__.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Match color — match.color__","text":"valist vector want specify colors element. mycolors vector colors. extendby number select 1, 2, 3, 4, 5, representing distinct auto-fill schemes.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/match.color__.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Match color — match.color__","text":"","code":"my_vector <- paste0(\"N_\",1:10) match.color__(my_vector,c(\"red\",\"yellow\",\"blue\")) #> N_1 N_2 N_3 N_4 N_5 #> \"red\" \"yellow\" \"blue\" \"white\" \"antiquewhite3\" #> N_6 N_7 N_8 N_9 N_10 #> \"aquamarine3\" \"azure3\" \"bisque2\" \"blue\" \"blueviolet\""},{"path":"https://kaiyanm.github.io/MolPad/reference/match_database.html","id":null,"dir":"Reference","previous_headings":"","what":"Match database — match_database","title":"Match database — match_database","text":"matches selected columns online database names.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/match_database.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Match database — match_database","text":"","code":"match_database(data, id_colname, id_type)"},{"path":"https://kaiyanm.github.io/MolPad/reference/match_database.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Match database — match_database","text":"match_database() internal function matches selected columns dataset corresponding databases. Currently MolPad support two types annotation database: GO KEGG. want custom URL platform, download modify paste_URL() function.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/match_database.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Match database — match_database","text":"","code":"data(test_data) head(test_annotations_processed) #> ID #> 1 1 #> 2 2 #> 3 3 #> 4 4 #> 5 5 #> 6 6 #> GO_ID #> 1 #> 2 #> 3 GO:0003674,GO:0003824,GO:0005575,GO:0006629,GO:0006644,GO:0006650,GO:0006655,GO:0006793,GO:0006796,GO:0008150,GO:0008152,GO:0008610,GO:0008654,GO:0008808,GO:0009058,GO:0009987,GO:0016020,GO:0016740,GO:0016772,GO:0016780,GO:0019637,GO:0030572,GO:0032048,GO:0032049,GO:0044237,GO:0044238,GO:0044249,GO:0044255,GO:0045017,GO:0046471,GO:0046474,GO:0046486,GO:0071704,GO:0090407,GO:1901576 #> 4 GO:0003674,GO:0003676,GO:0003723,GO:0003729,GO:0003824,GO:0004654,GO:0005488,GO:0005575,GO:0005622,GO:0005623,GO:0005737,GO:0006139,GO:0006401,GO:0006402,GO:0006725,GO:0006807,GO:0008150,GO:0008152,GO:0009056,GO:0009057,GO:0009892,GO:0009987,GO:0010468,GO:0010605,GO:0010629,GO:0016070,GO:0016071,GO:0016740,GO:0016772,GO:0016779,GO:0019222,GO:0019439,GO:0034641,GO:0034655,GO:0043170,GO:0044237,GO:0044238,GO:0044248,GO:0044260,GO:0044265,GO:0044270,GO:0044424,GO:0044464,GO:0046483,GO:0046700,GO:0048519,GO:0050789,GO:0060255,GO:0065007,GO:0071704,GO:0090304,GO:0097159,GO:1901360,GO:1901361,GO:1901363,GO:1901575 #> 5 #> 6 #> KEGG_ID Pathway taxonomic.scope #> 1 K07124 Integumentary System hormonal proteins #> 2 Skeletal System structural proteins #> 3 K06131 Muscular System enzymes #> 4 K00962 Nervous System contractile proteins #> 5 K02083 Endocrine System contractile proteins #> 6 Cardiovascular System structural proteins head(match_database(test_annotations_processed,id_colname = c(\"GO_ID\",\"KEGG_ID\"),id_type = c(\"GO\",\"KEGG\"))) #> ID #> 1 1 #> 2 2 #> 3 3 #> 4 4 #> 5 5 #> 6 6 #> GO_ID #> 1 #> 2 #> 3 GO:0003674<\/a>
GO:0003824<\/a>
GO:0005575<\/a>
GO:0006629<\/a>
GO:0006644<\/a>
GO:0006650<\/a>
GO:0006655<\/a>
GO:0006793<\/a>
GO:0006796<\/a>
GO:0008150<\/a>
GO:0008152<\/a>
GO:0008610<\/a>
GO:0008654<\/a>
GO:0008808<\/a>
GO:0009058<\/a>
GO:0009987<\/a>
GO:0016020<\/a>
GO:0016740<\/a>
GO:0016772<\/a>
GO:0016780<\/a>
GO:0019637<\/a>
GO:0030572<\/a>
GO:0032048<\/a>
GO:0032049<\/a>
GO:0044237<\/a>
GO:0044238<\/a>
GO:0044249<\/a>
GO:0044255<\/a>
GO:0045017<\/a>
GO:0046471<\/a>
GO:0046474<\/a>
GO:0046486<\/a>
GO:0071704<\/a>
GO:0090407<\/a>
GO:1901576<\/a> #> 4 GO:0003674<\/a>
GO:0003676<\/a>
GO:0003723<\/a>
GO:0003729<\/a>
GO:0003824<\/a>
GO:0004654<\/a>
GO:0005488<\/a>
GO:0005575<\/a>
GO:0005622<\/a>
GO:0005623<\/a>
GO:0005737<\/a>
GO:0006139<\/a>
GO:0006401<\/a>
GO:0006402<\/a>
GO:0006725<\/a>
GO:0006807<\/a>
GO:0008150<\/a>
GO:0008152<\/a>
GO:0009056<\/a>
GO:0009057<\/a>
GO:0009892<\/a>
GO:0009987<\/a>
GO:0010468<\/a>
GO:0010605<\/a>
GO:0010629<\/a>
GO:0016070<\/a>
GO:0016071<\/a>
GO:0016740<\/a>
GO:0016772<\/a>
GO:0016779<\/a>
GO:0019222<\/a>
GO:0019439<\/a>
GO:0034641<\/a>
GO:0034655<\/a>
GO:0043170<\/a>
GO:0044237<\/a>
GO:0044238<\/a>
GO:0044248<\/a>
GO:0044260<\/a>
GO:0044265<\/a>
GO:0044270<\/a>
GO:0044424<\/a>
GO:0044464<\/a>
GO:0046483<\/a>
GO:0046700<\/a>
GO:0048519<\/a>
GO:0050789<\/a>
GO:0060255<\/a>
GO:0065007<\/a>
GO:0071704<\/a>
GO:0090304<\/a>
GO:0097159<\/a>
GO:1901360<\/a>
GO:1901361<\/a>
GO:1901363<\/a>
GO:1901575<\/a> #> 5 #> 6 #> KEGG_ID #> 1 K07124<\/a> #> 2 #> 3 K06131<\/a> #> 4 K00962<\/a> #> 5 K02083<\/a> #> 6 #> Pathway taxonomic.scope #> 1 Integumentary System hormonal proteins #> 2 Skeletal System structural proteins #> 3 Muscular System enzymes #> 4 Nervous System contractile proteins #> 5 Endocrine System contractile proteins #> 6 Cardiovascular System structural proteins"},{"path":"https://kaiyanm.github.io/MolPad/reference/paste_URL.html","id":null,"dir":"Reference","previous_headings":"","what":"Paste URL — paste_URL","title":"Paste URL — paste_URL","text":"Retrieve database IDs associate respective URLs.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/paste_URL.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Paste URL — paste_URL","text":"","code":"paste_URL(x, id_type)"},{"path":"https://kaiyanm.github.io/MolPad/reference/paste_URL.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Paste URL — paste_URL","text":"","code":"data(test_data) paste_URL(test_annotations$GO_ID[1:4], id_type = \"GO\") #> [1] NA #> [2] NA #> [3] \"GO:0003674<\/a>
GO:0003824<\/a>
GO:0005575<\/a>
GO:0006629<\/a>
GO:0006644<\/a>
GO:0006650<\/a>
GO:0006655<\/a>
GO:0006793<\/a>
GO:0006796<\/a>
GO:0008150<\/a>
GO:0008152<\/a>
GO:0008610<\/a>
GO:0008654<\/a>
GO:0008808<\/a>
GO:0009058<\/a>
GO:0009987<\/a>
GO:0016020<\/a>
GO:0016740<\/a>
GO:0016772<\/a>
GO:0016780<\/a>
GO:0019637<\/a>
GO:0030572<\/a>
GO:0032048<\/a>
GO:0032049<\/a>
GO:0044237<\/a>
GO:0044238<\/a>
GO:0044249<\/a>
GO:0044255<\/a>
GO:0045017<\/a>
GO:0046471<\/a>
GO:0046474<\/a>
GO:0046486<\/a>
GO:0071704<\/a>
GO:0090407<\/a>
GO:1901576<\/a>\" #> [4] \"GO:0003674<\/a>
GO:0003676<\/a>
GO:0003723<\/a>
GO:0003729<\/a>
GO:0003824<\/a>
GO:0004654<\/a>
GO:0005488<\/a>
GO:0005575<\/a>
GO:0005622<\/a>
GO:0005623<\/a>
GO:0005737<\/a>
GO:0006139<\/a>
GO:0006401<\/a>
GO:0006402<\/a>
GO:0006725<\/a>
GO:0006807<\/a>
GO:0008150<\/a>
GO:0008152<\/a>
GO:0009056<\/a>
GO:0009057<\/a>
GO:0009892<\/a>
GO:0009987<\/a>
GO:0010468<\/a>
GO:0010605<\/a>
GO:0010629<\/a>
GO:0016070<\/a>
GO:0016071<\/a>
GO:0016740<\/a>
GO:0016772<\/a>
GO:0016779<\/a>
GO:0019222<\/a>
GO:0019439<\/a>
GO:0034641<\/a>
GO:0034655<\/a>
GO:0043170<\/a>
GO:0044237<\/a>
GO:0044238<\/a>
GO:0044248<\/a>
GO:0044260<\/a>
GO:0044265<\/a>
GO:0044270<\/a>
GO:0044424<\/a>
GO:0044464<\/a>
GO:0046483<\/a>
GO:0046700<\/a>
GO:0048519<\/a>
GO:0050789<\/a>
GO:0060255<\/a>
GO:0065007<\/a>
GO:0071704<\/a>
GO:0090304<\/a>
GO:0097159<\/a>
GO:1901360<\/a>
GO:1901361<\/a>
GO:1901363<\/a>
GO:1901575<\/a>\""},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":null,"dir":"Reference","previous_headings":"","what":"Pre-processing datasets — pre_process","title":"Pre-processing datasets — pre_process","text":"pre_process() function aids processing data inputs automatically establishes standardized format future use. allows two types data input: list datasets different sources long dataset containing specified last column type.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pre-processing datasets — pre_process","text":"","code":"pre_process( data, typenameList = NULL, replaceNA = TRUE, scale = TRUE, autoColName = \"Sec_\" )"},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pre-processing datasets — pre_process","text":"data data.frame describe feature one row. data contain variables ID value time_1, ..., value time_k, type extracting patterns across time. Note initial last column must exactly ID type. multiple data.frame format needs analyzed, also put list data.frame argument. case, variable type required generated next argument typenameList. typenameList vector strings. parameter used clarify source names data.frame, applicable input data list data.frame. default, set \"Dataset_1\", \"Dataset_2\", ..., etc. scale Logical; scale TRUE (default), standardize data.frame row base::scale. converts original value z-score. See also scale_by_row__(). autoColName string; autoColName -NULL (default), automatically set uniform column names data.frames. parameter applicable input data list data.frame. replaceNa Logical; replaceNa TRUE (default), replace NA 0.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pre-processing datasets — pre_process","text":"function returns long data.frame columns ID, value time_1, ..., value time_k, type.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Pre-processing datasets — pre_process","text":"consider two distinct scenarios application: one scenario, individuals collect several datasets various aspects instruments objects. example, might separately detecting lipids, metabolites, peptides specific soil sample. scenario, data uniform quality, can categorized larger groups exhibit significant differences. cases, pre_process() function serves valuable versatile tool. Yet, function optional generating dashboard. Users can perform processing long format matches required output. However, mindful number samples (timepoints) must greater 5 avoid potential errors subsequent prediction section.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pre-processing datasets — pre_process","text":"","code":"data(test_data) head(test_data, 10) #> ID T1 T2 T3 T4 T5 T6 T7 T8 T9 T10 type #> 1 1 1 0 0 1 1 0 0 1 6 6 type_A #> 2 2 6 0 0 0 0 3 1 0 2 1 type_A #> 3 3 1 0 0 0 2 0 0 2 2 1 type_A #> 4 4 4 5 3 3 7 2 1 1 0 0 type_A #> 5 5 4 3 NA 2 5 5 0 0 0 0 type_A #> 6 6 4 1 0 1 3 1 3 5 11 14 type_A #> 7 7 1 0 0 0 1 3 3 1 1 1 type_A #> 8 8 4 2 1 1 1 1 0 0 0 0 type_A #> 9 9 1 1 1 19 22 1 2 1 1 2 type_A #> 10 10 1 1 3 5 8 2 2 2 5 2 type_A a <- pre_process(test_data) head(a, 10) #> ID T1 T2 T3 T4 T5 T6 #> 1 1 -0.25354628 -0.6761234 -0.67612340 -0.25354628 -0.25354628 -0.6761234 #> 2 2 2.41458180 -0.6678631 -0.66786305 -0.66786305 -0.66786305 0.8733594 #> 3 3 0.21764288 -0.8705715 -0.87057150 -0.87057150 1.30585725 -0.8705715 #> 4 4 0.61658123 1.0569964 0.17616607 0.17616607 1.93782672 -0.2642491 #> 5 5 0.96186009 0.5038315 -0.87025436 0.04580286 1.41988870 1.4198887 #> 6 6 -0.06459959 -0.7105955 -0.92592741 -0.71059546 -0.27993154 -0.7105955 #> 7 7 -0.09086738 -0.9995412 -0.99954118 -0.99954118 -0.09086738 1.7264802 #> 8 8 2.40535118 0.8017837 0.00000000 0.00000000 0.00000000 0.0000000 #> 9 9 -0.50260633 -0.5026063 -0.50260633 1.70395805 2.07171878 -0.5026063 #> 10 10 -0.94019379 -0.9401938 -0.04477113 0.85065153 2.19378551 -0.4924825 #> T7 T8 T9 T10 type #> 1 -0.6761234 -0.25354628 1.85933936 1.85933936 type_A #> 2 -0.1541222 -0.66786305 0.35961857 -0.15412224 type_A #> 3 -0.8705715 1.30585725 1.30585725 0.21764288 type_A #> 4 -0.7046643 -0.70466426 -1.14507943 -1.14507943 type_A #> 5 -0.8702544 -0.87025436 -0.87025436 -0.87025436 type_A #> 6 -0.2799315 0.15073237 1.44272411 2.08871998 type_A #> 7 1.7264802 -0.09086738 -0.09086738 -0.09086738 type_A #> 8 -0.8017837 -0.80178373 -0.80178373 -0.80178373 type_A #> 9 -0.3800194 -0.50260633 -0.50260633 -0.38001942 type_A #> 10 -0.4924825 -0.49248246 0.85065153 -0.49248246 type_A"},{"path":"https://kaiyanm.github.io/MolPad/reference/reshape_for_make_functions.html","id":null,"dir":"Reference","previous_headings":"","what":"Reshape for 'make' functions — reshape_for_make_functions","title":"Reshape for 'make' functions — reshape_for_make_functions","text":"internal function produces three primary datasets dashboard intended \"make\" functions.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/reshape_for_make_functions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reshape for 'make' functions — reshape_for_make_functions","text":"","code":"reshape_for_make_functions(data, cluster, annotation, id_colname, id_type)"},{"path":"https://kaiyanm.github.io/MolPad/reference/reshape_for_make_functions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reshape for 'make' functions — reshape_for_make_functions","text":"data output pre_process() cluster output gClusters() annotation output gPathway() id_colname columns contain database IDs. id_type corresponding database names columns.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/reshape_for_make_functions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reshape for 'make' functions — reshape_for_make_functions","text":"","code":"data(test_data) l <- reshape_for_make_functions(test_data_processed, test_cluster, test_annotations_processed, id_colname = c(\"GO_ID\",\"KEGG_ID\"),id_type = c(\"GO\",\"KEGG\")) head(l[[1]]) #> ID cluster #> 1 1 Group_2 #> 2 2 Group_5 #> 3 3 Group_2 #> 4 4 Group_4 #> 5 5 Group_4 #> 6 6 Group_2 #> GO_ID #> 1 #> 2 #> 3 GO:0003674,GO:0003824,GO:0005575,GO:0006629,GO:0006644,GO:0006650,GO:0006655,GO:0006793,GO:0006796,GO:0008150,GO:0008152,GO:0008610,GO:0008654,GO:0008808,GO:0009058,GO:0009987,GO:0016020,GO:0016740,GO:0016772,GO:0016780,GO:0019637,GO:0030572,GO:0032048,GO:0032049,GO:0044237,GO:0044238,GO:0044249,GO:0044255,GO:0045017,GO:0046471,GO:0046474,GO:0046486,GO:0071704,GO:0090407,GO:1901576 #> 4 GO:0003674,GO:0003676,GO:0003723,GO:0003729,GO:0003824,GO:0004654,GO:0005488,GO:0005575,GO:0005622,GO:0005623,GO:0005737,GO:0006139,GO:0006401,GO:0006402,GO:0006725,GO:0006807,GO:0008150,GO:0008152,GO:0009056,GO:0009057,GO:0009892,GO:0009987,GO:0010468,GO:0010605,GO:0010629,GO:0016070,GO:0016071,GO:0016740,GO:0016772,GO:0016779,GO:0019222,GO:0019439,GO:0034641,GO:0034655,GO:0043170,GO:0044237,GO:0044238,GO:0044248,GO:0044260,GO:0044265,GO:0044270,GO:0044424,GO:0044464,GO:0046483,GO:0046700,GO:0048519,GO:0050789,GO:0060255,GO:0065007,GO:0071704,GO:0090304,GO:0097159,GO:1901360,GO:1901361,GO:1901363,GO:1901575 #> 5 #> 6 #> KEGG_ID Pathway taxonomic.scope #> 1 K07124 Integumentary System hormonal proteins #> 2 Skeletal System structural proteins #> 3 K06131 Muscular System enzymes #> 4 K00962 Nervous System contractile proteins #> 5 K02083 Endocrine System contractile proteins #> 6 Cardiovascular System structural proteins head(l[[2]]) #> # A tibble: 6 × 6 #> ID type cluster day value taxonomic.scope #> #> 1 1 type_A Group_2 T1 -0.254 hormonal proteins #> 2 1 type_A Group_2 T2 -0.676 hormonal proteins #> 3 1 type_A Group_2 T3 -0.676 hormonal proteins #> 4 1 type_A Group_2 T4 -0.254 hormonal proteins #> 5 1 type_A Group_2 T5 -0.254 hormonal proteins #> 6 1 type_A Group_2 T6 -0.676 hormonal proteins head(l[[3]]) #> ID cluster #> 1 1 Group_2 #> 2 2 Group_5 #> 3 3 Group_2 #> 4 4 Group_4 #> 5 5 Group_4 #> 6 6 Group_2 #> GO_ID #> 1 #> 2 #> 3 GO:0003674<\/a>
GO:0003824<\/a>
GO:0005575<\/a>
GO:0006629<\/a>
GO:0006644<\/a>
GO:0006650<\/a>
GO:0006655<\/a>
GO:0006793<\/a>
GO:0006796<\/a>
GO:0008150<\/a>
GO:0008152<\/a>
GO:0008610<\/a>
GO:0008654<\/a>
GO:0008808<\/a>
GO:0009058<\/a>
GO:0009987<\/a>
GO:0016020<\/a>
GO:0016740<\/a>
GO:0016772<\/a>
GO:0016780<\/a>
GO:0019637<\/a>
GO:0030572<\/a>
GO:0032048<\/a>
GO:0032049<\/a>
GO:0044237<\/a>
GO:0044238<\/a>
GO:0044249<\/a>
GO:0044255<\/a>
GO:0045017<\/a>
GO:0046471<\/a>
GO:0046474<\/a>
GO:0046486<\/a>
GO:0071704<\/a>
GO:0090407<\/a>
GO:1901576<\/a> #> 4 GO:0003674<\/a>
GO:0003676<\/a>
GO:0003723<\/a>
GO:0003729<\/a>
GO:0003824<\/a>
GO:0004654<\/a>
GO:0005488<\/a>
GO:0005575<\/a>
GO:0005622<\/a>
GO:0005623<\/a>
GO:0005737<\/a>
GO:0006139<\/a>
GO:0006401<\/a>
GO:0006402<\/a>
GO:0006725<\/a>
GO:0006807<\/a>
GO:0008150<\/a>
GO:0008152<\/a>
GO:0009056<\/a>
GO:0009057<\/a>
GO:0009892<\/a>
GO:0009987<\/a>
GO:0010468<\/a>
GO:0010605<\/a>
GO:0010629<\/a>
GO:0016070<\/a>
GO:0016071<\/a>
GO:0016740<\/a>
GO:0016772<\/a>
GO:0016779<\/a>
GO:0019222<\/a>
GO:0019439<\/a>
GO:0034641<\/a>
GO:0034655<\/a>
GO:0043170<\/a>
GO:0044237<\/a>
GO:0044238<\/a>
GO:0044248<\/a>
GO:0044260<\/a>
GO:0044265<\/a>
GO:0044270<\/a>
GO:0044424<\/a>
GO:0044464<\/a>
GO:0046483<\/a>
GO:0046700<\/a>
GO:0048519<\/a>
GO:0050789<\/a>
GO:0060255<\/a>
GO:0065007<\/a>
GO:0071704<\/a>
GO:0090304<\/a>
GO:0097159<\/a>
GO:1901360<\/a>
GO:1901361<\/a>
GO:1901363<\/a>
GO:1901575<\/a> #> 5 #> 6 #> KEGG_ID #> 1 K07124<\/a> #> 2 #> 3 K06131<\/a> #> 4 K00962<\/a> #> 5 K02083<\/a> #> 6 #> Pathway taxonomic.scope #> 1 Integumentary System hormonal proteins #> 2 Skeletal System structural proteins #> 3 Muscular System enzymes #> 4 Nervous System contractile proteins #> 5 Endocrine System contractile proteins #> 6 Cardiovascular System structural proteins"},{"path":"https://kaiyanm.github.io/MolPad/reference/scale_by_row__.html","id":null,"dir":"Reference","previous_headings":"","what":"Scale by row — scale_by_row__","title":"Scale by row — scale_by_row__","text":"Scales values sample, row independently processed.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/scale_by_row__.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Scale by row — scale_by_row__","text":"","code":"scale_by_row__(data)"},{"path":"https://kaiyanm.github.io/MolPad/reference/scale_by_row__.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Scale by row — scale_by_row__","text":"input expected data frame first column ID following columns containing observations different time points. ID column remains unaltered, columns double (dbl) format scaled.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/scale_by_row__.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Scale by row — scale_by_row__","text":"","code":"data(test_data) scale_by_row__(test_data[1:5,1:10]) #> ID T1 T2 T3 T4 T5 T6 #> 1 1 -0.05847053 -0.5847053 -0.58470535 -0.05847053 -0.05847053 -0.5847053 #> 2 2 2.26366583 -0.6467617 -0.64676167 -0.64676167 -0.64676167 0.8084521 #> 3 3 0.22866478 -0.8003267 -0.80032673 -0.80032673 1.25765629 -0.8003267 #> 4 4 0.50395263 0.9575100 0.05039526 0.05039526 1.86462473 -0.4031621 #> 5 5 0.73869087 0.2841119 NA -0.17046712 1.19326987 1.1932699 #> T7 T8 T9 #> 1 -0.5847053 -0.05847053 2.5727035 #> 2 -0.1616904 -0.64676167 0.3233808 #> 3 -0.8003267 1.25765629 1.2576563 #> 4 -0.8567195 -0.85671947 -1.3102768 #> 5 -1.0796251 -1.07962512 -1.0796251"},{"path":"https://kaiyanm.github.io/MolPad/reference/test_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Test data — test_data","title":"Test data — test_data","text":"synthetically generated dataset created basic testing purposes.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/test_data.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Test data — test_data","text":"two datasets: data annotations","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/test_data.html","id":"data","dir":"Reference","previous_headings":"","what":"data","title":"Test data — test_data","text":"data frame 100 rows 12 variables: ID row ID T1~T10 count value 10 timepoints type type ~D","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/test_data.html","id":"annotations","dir":"Reference","previous_headings":"","what":"annotations","title":"Test data — test_data","text":"data frame 100 rows 5 variables: ID row ID GO_ID go ID KEGG_ID kegg ID system primary lable: 'Integumentary System', 'Skeletal System', 'Muscular System', 'Nervous System', 'Endocrine System', 'Cardiovascular System', 'Lymphatic System', 'Respiratory System', 'Digestive System', 'Urinary System' class secondary label: 'antibodies', 'contractile proteins', 'enzymes', 'hormonal proteins', 'structural proteins', 'storage proteins', 'transport proteins'","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/test_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Test data — test_data","text":"","code":"data(test_data)"},{"path":"https://kaiyanm.github.io/MolPad/reference/transpose_dataframe__.html","id":null,"dir":"Reference","previous_headings":"","what":"Transpose dataframe — transpose_dataframe__","title":"Transpose dataframe — transpose_dataframe__","text":"function transposes provided data frame, using values first column new column names.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/transpose_dataframe__.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transpose dataframe — transpose_dataframe__","text":"","code":"transpose_dataframe__(data)"},{"path":"https://kaiyanm.github.io/MolPad/reference/transpose_dataframe__.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transpose dataframe — transpose_dataframe__","text":"expected input data frame first column serves Time, subsequent columns contain observations various features.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/transpose_dataframe__.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transpose dataframe — transpose_dataframe__","text":"","code":"a <- data.frame(\"Day\"=c(\"Day1\",\"Day2\",\"Day3\"),\"feature_1\" =c(1,2,3),\"feature_2\" =c(0,4,1),\"feature_3\" =c(1,1,0)) a #> Day feature_1 feature_2 feature_3 #> 1 Day1 1 0 1 #> 2 Day2 2 4 1 #> 3 Day3 3 1 0 transpose_dataframe__(a) #> Day1 Day2 Day3 #> 1 1 2 3 #> 2 0 4 1 #> 3 1 1 0"}]
+[{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"CC0 1.0 Universal","title":"CC0 1.0 Universal","text":"CREATIVE COMMONS CORPORATION LAW FIRM PROVIDE LEGAL SERVICES. DISTRIBUTION DOCUMENT CREATE ATTORNEY-CLIENT RELATIONSHIP. CREATIVE COMMONS PROVIDES INFORMATION “-” BASIS. CREATIVE COMMONS MAKES WARRANTIES REGARDING USE DOCUMENT INFORMATION WORKS PROVIDED HEREUNDER, DISCLAIMS LIABILITY DAMAGES RESULTING USE DOCUMENT INFORMATION WORKS PROVIDED HEREUNDER.","code":""},{"path":"https://kaiyanm.github.io/MolPad/LICENSE.html","id":"statement-of-purpose","dir":"","previous_headings":"","what":"Statement of Purpose","title":"CC0 1.0 Universal","text":"laws jurisdictions throughout world automatically confer exclusive Copyright Related Rights (defined ) upon creator subsequent owner(s) (, “owner”) original work authorship /database (, “Work”). 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Affirmer understands acknowledges Creative Commons party document duty obligation respect CC0 use Work.","code":""},{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/articles/FAQ.html","id":"how-can-i-tell-if-my-dataset-is-suitable-for-using-this-dashboard","dir":"Articles","previous_headings":"Common Questions:","what":"How can I tell if my dataset is suitable for using this dashboard?","title":"FAQs","text":"lot longitudinal data least two descriptive columns (like types), match minimal needs starting dashboard. addition, data specific web-ID columns, MolPad make convenient createing links databases automatically.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/FAQ.html","id":"how-can-i-customize-my-dashboard","dir":"Articles","previous_headings":"Common Questions:","what":"How can I customize my dashboard?","title":"FAQs","text":"can edit title within gDashboard() function. additional personalization, adjusting colors sizes figures, can fork repository, create copy, make modifications dashboard.css file.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/FAQ.html","id":"what-should-i-do-if-i-only-have-a-few-time-points5","dir":"Articles","previous_headings":"Common Questions:","what":"What should I do if I only have a few time points(<5)?","title":"FAQs","text":"Firstly, strongly recommend extending length longitudinal samples. can achieve increasing number measurements two days incorporating simulated data. options don’t suit needs, can also consider using “linear” method gNetwork() function.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Case Study: Cheese Communities","text":"vignette provides comprehensive guide using MolPad case study, including data pre-processing, network generation, result analysis.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"washed-rind-cheese-microbial-communities","dir":"Articles","previous_headings":"","what":"Washed-Rind Cheese Microbial Communities","title":"Case Study: Cheese Communities","text":"Cheese making ancient craft involves coagulation milk proteins form curds, separated liquid whey. curds processed, shaped, aged develop desired texture flavor. Various factors, type milk, specific cultures bacteria, aging conditions, contribute unique characteristics cheese. One crucial step process aging practice, regular washing brine solution plays significant role. process producing cheese, regular washing brine solution aging practice can homogenize microbial communities cheese’ surface facilitate intermicrobial interactions. following parts, analyze longitudinal data set three washed-rind cheese communities collected cheese ripening.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"data","dir":"Articles","previous_headings":"","what":"Data","title":"Case Study: Cheese Communities","text":"analysis, use data set contained study Smith et al. (2022) microbial communities cheese. original research investigated successional dynamics occur within cheese rind microbial communities using combination 16S rRNA amplicon, Illumina, PacBio sequencing. functionally taxonomically annotate (using eggNOG (21) MMseqs2 (22)) contigs generated Illumina reads, demonstrate utility MolPad using single-omic. Specifically, focus Cheese Sample Cheese Sample C. detailed information attached data, please check documentation.","code":"data(\"cheese\") str(cheese) #> tibble [106,239 × 18] (S3: tbl_df/tbl/data.frame) #> $ ID : chr [1:106239] \"1\" \"2\" \"3\" \"4\" ... #> $ A_1 : int [1:106239] 38 23 24 3 58 12 1 14 1 3 ... #> $ A_2 : int [1:106239] 23 6 5 2 14 9 1 7 1 1 ... #> $ A_3 : int [1:106239] 27 4 37 4 45 14 0 14 3 5 ... #> $ A_4 : int [1:106239] 5 0 10 2 13 4 1 4 4 0 ... #> $ A_5 : int [1:106239] 11 9 19 16 32 13 0 4 1 1 ... #> $ C_1 : int [1:106239] 13 21 3 56 82 2 4 17 7 2 ... #> $ C_3 : int [1:106239] 1 1 0 1 3 0 0 0 0 1 ... #> $ C_4 : int [1:106239] 3 0 1 7 8 1 0 1 2 2 ... #> $ C_5 : int [1:106239] 0 2 4 17 3 4 0 1 8 6 ... #> $ GO_ID : chr [1:106239] NA NA NA NA ... #> $ KEGG_ID: chr [1:106239] NA NA NA NA ... #> $ domain : chr [1:106239] NA NA NA NA ... #> $ phylum : chr [1:106239] NA NA NA NA ... #> $ class : chr [1:106239] NA NA NA NA ... #> $ order : chr [1:106239] NA NA NA NA ... #> $ family : chr [1:106239] NA NA NA NA ... #> $ genus : chr [1:106239] NA NA NA NA ... str(annotations) #> tibble [86,156 × 9] (S3: tbl_df/tbl/data.frame) #> $ ID : chr [1:86156] \"9\" \"10\" \"11\" \"12\" ... #> $ GO_ID : chr [1:86156] \"-\" \"-\" \"-\" \"-\" ... #> $ KEGG_ID: chr [1:86156] \"-\" \"-\" \"-\" \"-\" ... #> $ domain : chr [1:86156] \"Bacteria\" \"Bacteria\" \"Bacteria\" \"Bacteria\" ... #> $ phylum : chr [1:86156] \"Pseudomonadota\" \"Pseudomonadota\" \"Pseudomonadota\" \"Pseudomonadota\" ... #> $ class : chr [1:86156] \"Alphaproteobacteria\" \"Alphaproteobacteria\" \"Alphaproteobacteria\" \"Alphaproteobacteria\" ... #> $ order : chr [1:86156] \"Caulobacterales\" \"Caulobacterales\" \"Hyphomicrobiales\" \"Hyphomicrobiales\" ... #> $ family : chr [1:86156] \"Caulobacteraceae\" \"Caulobacteraceae\" \"Bartonellaceae\" \"Brucellaceae;-_Brucella/Ochrobactrum group\" ... #> $ genus : chr [1:86156] \"Caulobacter\" \"Caulobacter;-_unclassified Caulobacter\" \"Bartonella\" \"Brucella\" ..."},{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"data-and-annotations","dir":"Articles","previous_headings":"Data > Pre-process","what":"Data and Annotations","title":"Case Study: Cheese Communities","text":"select ‘type’ column phylum describe characteristic cheese data. Also, columns phylum class taken tags elemental composition. section, introduce data preparing steps analysis. annotations dataset contains various columns describe characteristics properties samples. First, select ‘type’ column phylum provide broad categorization microbial communities present cheese surface. categorization helps understanding overall composition diversity microbes high taxonomic level. run pre_process() function clean standardize data. annotate dataset, also use columns phylum class tags elemental composition microbial communities. phylum column represents major taxonomic rank, giving us broad overview microbial distribution. class column provides detailed information, allowing us delve deeper specific types microbes present. pre-processing, two datasets put dashboard look like:","code":"cheesedata <- cheese |> select(ID, A_1:C_5, phylum) |> rename(type=phylum) |> pre_process() pathchee <- gAnnotation(annotations,\"phylum\",\"class\") # data cheesedata[112:115,] #> # A tibble: 4 × 11 #> ID A_1 A_2 A_3 A_4 A_5 C_1 C_3 C_4 C_5 type #> #> 1 112 0.943 -0.471 -0.471 -0.471 -0.471 2.36 -0.471 -0.471 -0.471 Other #> 2 113 0.786 -0.124 0.126 -0.623 -0.637 2.33 -0.667 -0.593 -0.593 Other #> 3 114 1.22 -1.09 -0.430 -0.829 -0.170 1.43 -1.30 0.455 0.715 Other #> 4 115 2.67 -0.333 -0.333 -0.333 -0.333 -0.333 -0.333 -0.333 -0.333 Other # annotation pathchee[112:115,] #> # A tibble: 4 × 9 #> ID GO_ID KEGG_ID domain Pathway taxonomic.scope order family genus #> #> 1 145 - ko:K00004 Bacte… Pseudo… Unknown NA NA NA #> 2 147 - ko:K00004 Bacte… Actino… Actinomycetes NA NA NA #> 3 148 - ko:K00004 Bacte… Bacill… Bacilli Baci… Bacil… Lysi… #> 4 149 - ko:K00004,ko:K0… Bacte… Actino… Actinomycetes NA NA NA"},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"cluster-input","dir":"Articles","previous_headings":"Data","what":"Cluster Input","title":"Case Study: Cheese Communities","text":"section, generate clusters first dataset using gClusters function. function takes cheese dataset (cheesedata) input generates clusters based specified parameters. , set number clusters 10 (ncluster = 10) specify maximum number clusters consider determining optimal number clusters (elbow.max=15).","code":"cluschee <- gClusters(cheesedata,ncluster = 10,elbow.max=15)"},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"network-input","dir":"Articles","previous_headings":"Data","what":"network input","title":"Case Study: Cheese Communities","text":"generating clusters major patterns, proceed obtain network results clusters. Taking cluster centroids nodes, prediction process edges divided individual regression tasks, cluster centroid independentally predicted expression cluster centroids, using random forests. pick top 3 related predictors cluster centroid save network output future use. achieved using gNetwork() function. gain insight network results, can visualize details using gNetwork_view() function, shown .","code":"networkchee <- gNetwork(cluschee,ntop = 3) gNetwork_view(networkchee)"},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"run-dashboard","dir":"Articles","previous_headings":"","what":"Run Dashboard","title":"Case Study: Cheese Communities","text":"clusters network results obtained, can proceed run dashboard. involves declaring annotations executing dashboard using gDashboard() function. , pass cheese dataset (cheesedata), cluster results (cluschee), network results (networkchee), specify column names types annotation identifiers.","code":"gDashboard(cheesedata, cluschee, pathchee, networkchee, id_colname = c(\"GO_ID\",\"KEGG_ID\"), id_type = c(\"GO\",\"KEGG\"))"},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"results","dir":"Articles","previous_headings":"","what":"Results","title":"Case Study: Cheese Communities","text":"example, filter Actinomycetota (Actinobacteria) functional group, see edges connecting group 10 group 3- clusters features associated Actinomycetoa Cheese sample (Fig 1.3.). Fig 1: Dashboard showing Actinomycetota filtered network () enrichment pattern Cheese Sample-(B) Cheese Sample-C (C); Cluster pattern Group 9, also enriched Type IV secretion genes (D). Looking pattern traces groups, (Fig 1.3.B), peak samples A4 (week 9) A5 (week 13), mirrors 16S rRNA results Saak et al. Since two clusters edges connecting groups, suggests Actinomycetoa features found groups follow distinct longitudinal succession patterns independent. looking Actinomycetoa within Cheese Sample C see different pattern. Groups 2 5, features associated Actinomycetoa, highly connected groups (Fig 1.3.). results, can hypothesize though Actinomycetoa features abundant later time points cheese samples, dynamics differentially influenced. authors found Type VI secretion enriched Pseudomonadota bacteria (specifically, Psychrobacter), hypothesized enrichment due importance physical species interactions occur habitat. Using MolPad, searched secretion systems associated genes, understand dynamics within community. Focusing KEGG annotated Type IV secretion genes, found Group 9 contained 12/13 genes. Within group, features cluster ones peak Cheese sample C5 (week 13, Fig 1.3.D). also taxonomically diverse sample. , can hypothesize increased taxonomic diversity also associated increases genes related competitive species interactions.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"installation","dir":"Articles","previous_headings":"","what":"Installation","title":"Get started","text":"can either install MolPad devtools, clone repository local computer (example, onto ./Github) installing: , load package:","code":"# Install the package in R: install.packages(\"devtools\") library(devtools) install_github(\"KaiyanM/MolPad\") # Download through Github and install the package in R: setwd(\"./GitHub\") install(\"MolPad\") library(MolPad)"},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"before-input","dir":"Articles","previous_headings":"","what":"Before Input","title":"Get started","text":"recommend carefully reviewing data considering applying normalization (standardization, quantile normalization, log transformation, …) imputation (KNN, PMM, Mean/Median/Mode, …) address issues related library size missing data beforehead, otherwise pre_processing() function default converting NAs 0s.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"tips-for-normalization-and-imputation","dir":"Articles","previous_headings":"Before Input","what":"Tips for normalization and imputation","title":"Get started","text":"choose normalization method, first consider data distribution. Use Z-score normalization data normally distributed, log transformation robust scaling ’s skewed. analysis requires data within specific range, like [0, 1], opt Min-Max normalization. datasets outliers, robust scaling rank normalization better choices. selecting imputation method, assess amount pattern missing data. small amount missing data, simple methods like mean, median, mode imputation effective. missing data related variables, consider sophisticated approaches like Predictive Mean Matching k-NN imputation. computational resources concern, simpler imputation methods might practical, resources allow, advanced methods can improve data quality. ’s hard decide, can always experiment different methods use cross-validation compare effectiveness. Let choice guided domain knowledge specific requirements analysis increase quality following steps.","code":""},{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"multi-omics-yes-or-no","dir":"Articles","previous_headings":"Data Format","what":"Multi-omics: Yes or No","title":"Get started","text":"visualization pipeline starts pre-processing module includes built-functions reshape data inputs standard format streamline data preparation. Depending nature datasets, two primary conditions consider: research object includes multi-omics measurements, assume provide list dataframes, collected different omics type. example, might datasets peptides, metabolites, lipids. combine together standard long dataset row represents feature, simply run pre_process() function, conduct data preparation yield standard input format demonstrated . Datasets multi-omics, can still utilize dashboard long ensuring data inputs reformatted standard longitudinal format. situation, can manually assign type column category label describe major groups data, input data pre_processing() module data preparation . case study, utilized “Kingdom” type label column cheese data. datasets multi-omics, can simply apply dashboard reformatting data standard longitudinal format. case, manually assign category label type column represent major groups. can still input data pre_processing() module cleaning preparation. case study, used “Kingdom” type label cheese datasets.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"data-format-and-pre-process","dir":"Articles","previous_headings":"Data Format","what":"Data Format and Pre-process","title":"Get started","text":"figure, , B, C can represent datasets different sources different aspects measurements. two examples input data might look like transform required format pre_process() modules.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"if-your-input-is-a-list-of-datasets","dir":"Articles","previous_headings":"Data Format > Data Format and Pre-process","what":"If your input is a list of datasets:","title":"Get started","text":"list datasets, following format: ID column identical column names. pre_process():","code":"str(fungal_dlist) #> List of 3 #> $ :'data.frame': 149786 obs. of 12 variables: #> ..$ ID : chr [1:149786] \"grp1_1\" \"grp1_2\" \"grp1_6\" \"grp1_7\" ... #> ..$ D0_01 : num [1:149786] -0.33 -0.33 1.591 1.106 0.659 ... #> ..$ D1_02 : num [1:149786] 0.628 0.628 3.087 3.618 2.843 ... #> ..$ D2_03 : num [1:149786] -1.223 -1.223 0.846 1.178 1.435 ... #> ..$ D3_04 : num [1:149786] -2.411 -1.808 1.239 1.753 0.258 ... #> ..$ D4_05 : num [1:149786] -1.41 -1.18 2.05 1.64 1.21 ... #> ..$ D5_06 : num [1:149786] -2.245 -2.309 0.83 1.64 0.395 ... #> ..$ D6_07 : num [1:149786] -0.784 -1.125 2.758 2.888 2.516 ... #> ..$ D7_08 : num [1:149786] -2.61197 -2.68724 0.42236 0.92846 0.00892 ... #> ..$ D8_09 : num [1:149786] -0.951 -1.3 2.417 2.454 1.418 ... #> ..$ D9_10 : num [1:149786] -1.91 -2.61 1.33 1.8 2.25 ... #> ..$ D10_11: num [1:149786] -2.871 -3.417 1.101 0.619 1.275 ... #> $ :'data.frame': 150 obs. of 12 variables: #> ..$ ID : chr [1:150] \"grp2_1\" \"grp2_2\" \"grp2_3\" \"grp2_4\" ... #> ..$ D0_01 : num [1:150] 0.947 1.012 0.156 0.31 0.138 ... #> ..$ D1_02 : num [1:150] -1.616 -0.917 -1.581 -2.717 -2.578 ... #> ..$ D2_03 : num [1:150] -2.4 -1.67 -2.31 -3.02 -3.66 ... #> ..$ D3_04 : num [1:150] -0.5734 0.0336 -0.351 -1.3052 -1.737 ... #> ..$ D4_05 : num [1:150] 0.662 0.854 1.002 0.12 -0.371 ... #> ..$ D5_06 : num [1:150] -1.09 -1.28 -1.04 -1.82 -3.15 ... #> ..$ D6_07 : num [1:150] -0.212 0.718 0.189 -0.627 -1.081 ... #> ..$ D7_08 : num [1:150] 0.969 2.117 1.462 0.339 -0.038 ... #> ..$ D8_09 : num [1:150] -0.712 0.43 -0.281 -1.297 -1.822 ... #> ..$ D9_10 : num [1:150] -0.649 0.45 -0.124 -1.77 -1.962 ... #> ..$ D10_11: num [1:150] -1.05 -0.85 -1.58 -1.91 -4.05 ... #> $ :'data.frame': 369 obs. of 12 variables: #> ..$ ID : chr [1:369] \"grp3_1\" \"grp3_2\" \"grp3_3\" \"grp3_4\" ... #> ..$ D0_01 : num [1:369] -0.918 -2.112 -1.372 -0.4 -1.75 ... #> ..$ D1_02 : num [1:369] -1.219 -3.502 -1.504 -0.615 -2.11 ... #> ..$ D2_03 : num [1:369] -0.542 -2.324 -0.592 0.161 -1.342 ... #> ..$ D3_04 : num [1:369] -0.101 -1.495 0.1 0.603 -1.058 ... #> ..$ D4_05 : num [1:369] 1.715 1.557 1.89 2.296 0.653 ... #> ..$ D5_06 : num [1:369] -1.452 -2.37 -1.182 -0.846 -2.531 ... #> ..$ D6_07 : num [1:369] 0.0879 -0.9982 0.4382 0.6215 -0.9464 ... #> ..$ D7_08 : num [1:369] 0.0502 0.4668 0.5378 0.8353 -0.9213 ... #> ..$ D8_09 : num [1:369] -0.0268 -0.4226 0.4528 0.6328 -1.0998 ... #> ..$ D9_10 : num [1:369] -0.5807 -1.7755 -0.1956 0.0866 -1.5876 ... #> ..$ D10_11: num [1:369] 0.118 -0.16 0.687 0.572 -1.037 ... #> ID D0_01 D1_02 D2_03 D3_04 D4_05 D5_06 #> 1 grp1_1 -0.3297741 0.6284682 -1.2232968 -2.411339 -1.413062 -2.2445487 #> 2 grp1_2 -0.3297741 0.6284682 -1.2232968 -1.807533 -1.183498 -2.3088022 #> 3 grp1_6 1.5908054 3.0871284 0.8460835 1.239411 2.054109 0.8300876 #> 4 grp1_7 1.1061795 3.6176250 1.1781012 1.753078 1.643661 1.6400102 #> D6_07 D7_08 D8_09 D9_10 D10_11 #> 1 -0.7839412 -2.6119663 -0.9511467 -1.914273 -2.8711810 #> 2 -1.1251434 -2.6872417 -1.2996614 -2.610647 -3.4165861 #> 3 2.7576288 0.4223618 2.4165602 1.333387 1.1012825 #> 4 2.8877343 0.9284640 2.4538561 1.799019 0.6187648 #> ID D0_01 D1_02 D2_03 D3_04 D4_05 D5_06 #> 1 grp2_1 0.9467431 -1.6162900 -2.400394 -0.57337845 0.6616711 -1.090914 #> 2 grp2_2 1.0120048 -0.9165994 -1.674330 0.03361378 0.8540950 -1.283964 #> 3 grp2_3 0.1561394 -1.5805490 -2.307281 -0.35095093 1.0018741 -1.035030 #> 4 grp2_4 0.3104578 -2.7173471 -3.022886 -1.30521496 0.1198075 -1.816677 #> D6_07 D7_08 D8_09 D9_10 D10_11 #> 1 -0.2116778 0.9688582 -0.7123773 -0.6489204 -1.0504845 #> 2 0.7181970 2.1174460 0.4302981 0.4504066 -0.8503481 #> 3 0.1885422 1.4619677 -0.2805926 -0.1235090 -1.5757556 #> 4 -0.6271954 0.3393109 -1.2967938 -1.7697248 -1.9073101 #> ID D0_01 D1_02 D2_03 D3_04 D4_05 D5_06 #> 1 grp3_1 -0.9178218 -1.2193128 -0.5415780 -0.1007493 1.715495 -1.4515238 #> 2 grp3_2 -2.1120636 -3.5015326 -2.3236475 -1.4949366 1.557463 -2.3698578 #> 3 grp3_3 -1.3724446 -1.5043101 -0.5922380 0.1004978 1.889573 -1.1815469 #> 4 grp3_4 -0.4002445 -0.6145252 0.1613003 0.6031744 2.295751 -0.8464012 #> D6_07 D7_08 D8_09 D9_10 D10_11 #> 1 0.08792489 0.05020749 -0.02681783 -0.58065562 0.1177374 #> 2 -0.99822332 0.46681389 -0.42255125 -1.77554842 -0.1603138 #> 3 0.43822354 0.53780990 0.45276561 -0.19558682 0.6870272 #> 4 0.62145080 0.83528878 0.63275758 0.08663959 0.5720591 data <- pre_process(fungal_dlist, typenameList = c('DataSource_A','DataSource_B','DataSource_C'), replaceNA = TRUE, scale = TRUE, autoColName = \"Section_\") #> [1] \"Reformat a list of datasets:\" head(data,10) #> ID D0_01 D1_02 D2_03 D3_04 D4_05 D5_06 #> 1 grp1_1 1.06146675 1.956656 0.2267383 -0.88312967 0.04945980 -0.7273146 #> 2 grp1_2 1.08465667 1.916983 0.3085454 -0.19892034 0.34311497 -0.6343214 #> 3 grp1_6 -0.01899274 1.717871 -0.8834318 -0.42687491 0.51878884 -0.9019992 #> 4 grp1_7 -0.75869657 2.051470 -0.6782202 -0.03485388 -0.15728499 -0.1613700 #> 5 grp1_8 -0.68132827 1.648062 0.1465161 -1.10875822 -0.09101615 -0.9629427 #> 6 grp1_11 0.27709905 1.981042 -0.5786717 0.02888008 0.54647685 -0.7013406 #> 7 grp1_12 0.90487968 2.180079 -0.6439891 -0.14841544 -0.69040101 -0.3834938 #> 8 grp1_13 0.46335031 1.695812 -1.0938459 -0.76868293 0.74415950 -0.6861036 #> 9 grp1_14 0.72014702 1.708604 -0.9344900 -0.35796963 0.63730277 -0.9872778 #> 10 grp1_15 0.54526798 1.784716 -0.8846753 -0.49538649 0.62916563 -0.9742293 #> D6_07 D7_08 D8_09 D9_10 D10_11 type #> 1 0.6371841 -1.0705560 0.4809808 -0.41877137 -1.31271417 DataSource_A #> 2 0.3938012 -0.9630327 0.2422154 -0.89650312 -1.59653914 DataSource_A #> 3 1.3354026 -1.3752687 0.9395058 -0.31779210 -0.58720856 DataSource_A #> 4 1.2347631 -0.9575502 0.7492778 0.01655139 -1.30408627 DataSource_A #> 5 1.2995756 -1.3741657 0.1285139 1.01932257 -0.02377948 DataSource_A #> 6 1.1406352 -1.6339536 0.2041455 -0.45793129 -0.80638109 DataSource_A #> 7 0.4794959 -1.6340692 0.5360766 -0.31430867 -0.28585374 DataSource_A #> 8 1.2235141 -1.2241931 0.6790383 -0.28487806 -0.74817082 DataSource_A #> 9 1.1466328 -1.4265286 0.5357111 -0.51799361 -0.52413804 DataSource_A #> 10 1.1813273 -1.2944121 0.6417621 -0.46906150 -0.66447461 DataSource_A"},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"if-your-input-is-a-single-dataset","dir":"Articles","previous_headings":"Data Format > Data Format and Pre-process","what":"If your input is a single dataset:","title":"Get started","text":"dataset already includes labeled ‘type’ column: pre_process():","code":"#> ID Day_1 Day_2 Day_3 Day_4 Day_5 Day_6 #> 1 1 NA NA -0.82919412 0.08237778 1.30438699 0.4875914 #> 2 2 NA NA -0.41606256 1.42523475 -0.44792066 1.1425154 #> 3 3 1.0354708 NA 0.78841026 -1.49585251 -0.41361867 -0.3271995 #> 4 4 -0.3005323 0.6998150 -1.86018783 1.14167433 -1.04204735 -2.4930409 #> 5 5 -0.1307903 0.7839983 0.25834857 0.62421147 -0.07202124 -0.9214203 #> 6 6 -0.9910321 1.8059465 -1.25395397 -0.45260649 -0.57303842 0.9990113 #> 7 7 -0.9160734 1.4475673 -3.10452531 -0.06560360 -1.27374744 -0.5703149 #> 8 8 -0.7381729 -0.4844302 1.10281675 0.12049188 0.20892638 -0.3115689 #> 9 9 -0.2581716 -0.5906591 0.09794986 2.44249606 1.13135657 0.4886958 #> 10 10 1.8831483 -1.2490571 0.55981559 -2.31731640 0.97323395 -0.7994677 #> Day_7 Day_8 type #> 1 -1.4632070 0.05832042 peptide #> 2 -0.4292379 -0.79047610 peptide #> 3 -0.3508094 -0.34995898 peptide #> 4 -0.3011731 0.67869535 peptide #> 5 1.1761504 -0.86303628 peptide #> 6 -0.8993647 -0.44801227 peptide #> 7 1.1593812 -0.90149427 peptide #> 8 0.5463595 -1.51687039 lipid #> 9 -0.5016034 -0.08241911 lipid #> 10 -0.9442708 NA metabolite x1 <- pre_process(x, replaceNA = TRUE,scale = TRUE) head(x1,10) #> ID Day_1 Day_2 Day_3 Day_4 Day_5 Day_6 #> 1 1 0.05451890 0.05451890 -0.9508450 0.1543986 1.6360347 0.64570353 #> 2 2 -0.07554744 -0.07554744 -0.5950354 1.7039742 -0.6348129 1.35097605 #> 3 3 1.49874204 0.17759695 1.1835204 -1.7309438 -0.3501343 -0.23987305 #> 4 4 0.10376561 0.87801613 -1.1033792 1.2200072 -0.4701534 -1.59319589 #> 5 5 -0.31530188 0.89803359 0.2008348 0.6860993 -0.2373532 -1.36395879 #> 6 6 -0.71746086 1.90776216 -0.9642373 -0.2120986 -0.3251351 1.15037885 #> 7 7 -0.27002806 1.37506283 -1.7931878 0.3218978 -0.5189688 -0.02938062 #> 8 8 -0.74469269 -0.43190500 1.5246885 0.3137802 0.4227931 -0.21881953 #> 9 9 -0.58919072 -0.91616395 -0.2389755 2.0666865 0.7772922 0.14528989 #> 10 10 1.56649401 -0.74805373 0.5886154 -1.5374455 0.8941115 -0.41582898 #> Day_7 Day_8 type #> 1 -1.7195596 0.1252300 peptide #> 2 -0.6114859 -1.0625212 peptide #> 3 -0.2699967 -0.2689116 peptide #> 4 0.1032697 0.8616699 peptide #> 5 1.4181669 -1.2865208 peptide #> 6 -0.6314225 -0.2077865 peptide #> 7 1.1744856 -0.2598810 peptide #> 8 0.8387456 -1.7045902 lipid #> 9 -0.8285852 -0.4163531 lipid #> 10 -0.5228315 0.1749388 metabolite"},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"choose-your-annotation","dir":"Articles","previous_headings":"","what":"Choose Your Annotation","title":"Get started","text":"addition specifying data type mentioned , methods support three levels information: functional annotation, taxonomy annotation, feature annotation. annotations matched ID columns annotation data, serving another crucial input generating dashboard. facilitate automatic feature link generation using KeggID GOID, users set corresponding column names beforehand. example annotation formats align data inputs. data two layers labels (like cheese dataset), can specify two layers want focus network using gAnnotation() function.","code":"#> ID taxonomic.scope ko_term Pathway #> 1 grp1_24 Bacteria K02706 00195_Photosynthesis #> 2 grp1_25 Bacteria K02706 00195_Photosynthesis #> 3 grp1_26 Bacteria K02706 00195_Photosynthesis #> 4 grp1_1075 Bacteria K02112 00195_Photosynthesis #> 5 grp1_1076 Bacteria K02112 00195_Photosynthesis #> 6 grp1_1358 Bacteria K02112 00195_Photosynthesis Annotation_path_taxon <- gAnnotation(annotations,\"phylum\",\"class\")"},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"network-generation","dir":"Articles","previous_headings":"","what":"Network Generation","title":"Get started","text":"generate network, first scale cluster trajectories across input molecular features. Trajectories clustered using K-means, built-elbow method used select optimal K. build network clustered trajectories adapting GENIE3 algorithm. Specifically, network learned series regressions, cluster centroid predicted expression patterns cluster centroids using random forests. choose random forests potential model interacting features non-linearity without strong assumptions. determine feature clusters predictive given trajectory, Mean Decrease Accuracy subset top predictors whose expression directly influences expression target cluster taken indication putative link.","code":"cluster_result <- data |> gClusters(ncluster = 5,elbow.max=15) network_result <- cluster_result |> gNetwork(ntop = 3) gNetwork_view(network_result) #> Warning: Removed 1 rows containing missing values (`geom_segment()`). #> Warning: Removed 1 rows containing missing values (`geom_point()`)."},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"dashboard-navigation","dir":"Articles","previous_headings":"","what":"Dashboard Navigation","title":"Get started","text":"workflow inputs related gDashboard function. key function processes data runs dashboard, requiring input previous steps. ’ve launched Shiny dashboard, can zoom make adjustments explore interesting findings within data. effectively navigate dashboard generated MolPad, ’ll follow three main steps:","code":"gDashboard(data, cluster_result, Annotation_path_taxon, network_result, id_colname = \"ko_term\" , id_type = \"KEGG\")"},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"choose-a-primary-functional-annotation-and-adjust-edge-density","dir":"Articles","previous_headings":"Dashboard Navigation","what":"1. Choose a Primary Functional Annotation and Adjust Edge Density","title":"Get started","text":"Start selecting primary functional annotation available options. , fine-tune edge density adjusting threshold value importance score. Nodes turn bright green indicate clusters containing features related chosen functional annotation.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"explore-the-network","dir":"Articles","previous_headings":"Dashboard Navigation","what":"2. Explore the Network","title":"Get started","text":"Brushing network unveils patterns taxonomic composition typical trajectories. can also zoom specific taxonomic annotations applying filters.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"investigate-feature-details-and-related-function-annotations","dir":"Articles","previous_headings":"Dashboard Navigation","what":"3. Investigate Feature Details and Related Function Annotations","title":"Get started","text":"Delve feature table examine specifics features within selected clusters. Explore additional related function annotations using drop-options. Click provided links access online information items interest. interface encourages iterative exploration, enabling conduct multiple steps answer specific questions, comparing pattern distributions two functions identifying functionally important community members metabolizing feature interest.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"related-visualization-outputs","dir":"Articles","previous_headings":"","what":"Related Visualization Outputs","title":"Get started","text":"following visualizations integrated dashboard, ’s need run function plot individually. ’ve linked stacked bar plot ribbon plot network facilitate focus context explorations. setup helps detecting similar longitudinal patterns understanding relationships.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"network-plot","dir":"Articles","previous_headings":"Related Visualization Outputs","what":"Network Plot","title":"Get started","text":"network plot powerful visualization tool displays relationships different groups features within data. MolPad, network plot helps identify clusters features share similar patterns, revealing underlying connections might immediately obvious. visualizing connections, users can gain clearer understanding structure within data, making easier pinpoint significant associations trends.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"stack-bar-plot","dir":"Articles","previous_headings":"Related Visualization Outputs","what":"Stack Bar Plot","title":"Get started","text":"stacked bar plot provides detailed view composition cluster group dataset. stacking different categories top within single bar, plot allows quick comparison relative proportions across multiple groups. particularly useful microbiome experiments understanding distribution taxa across different conditions time points crucial. stacked bar plot makes easy see distributions change experimental conditions, facilitating deeper insights.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"ribbon-plot","dir":"Articles","previous_headings":"Related Visualization Outputs","what":"Ribbon Plot","title":"Get started","text":"ribbon plot designed visualize changes time, making ideal tool tracking longitudinal data. MolPad, ribbon plot illustrates abundance expression levels features vary across different time points conditions. smooth, flowing design ribbons helps emphasize trends patterns, enabling users quickly identify periods significant change stability. plot particularly advantageous comparing multiple groups, clearly shows overlapping trends divergences, providing comprehensive view temporal dynamics data.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/whymolpad.html","id":"why-molpad","dir":"Articles","previous_headings":"","what":"Why MolPad?","title":"Why MolPad?","text":"increasing multi-omics data longitudinal designs integrated microbiome experiments, growing need present network, especially complex variations across biological modalities. Network perspective helps detect underlying co-occurrence among microbiome samples, allowing high-level insights global structure. Yet comes experimental data records time series 100,000 features, network collapse entangled clumps therefore unable read.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/whymolpad.html","id":"improvements","dir":"Articles","previous_headings":"","what":"Improvements","title":"Why MolPad?","text":"aim network interpretation, MolPad shows improvements 3 important aspects: Capture groups features share longitudinal patterns functional properties within network. Annotate network complex experimental datasets, way dig specific components notable features. Streamlines exploration modularized functions interactive dashboard. package especially facilitates exploration longitudinal molecular co-expression microbiome experiments using focus-plus-context approach, empowering researchers uncover global patterns insights longitudinal microbiomics data. Unlike existing tools, MolPad stands rich network interactivity support contextualization. also equipped beginner-friendly interface self-contained data processing functions, making accessible users limited programming experience.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/whymolpad.html","id":"key-features","dir":"Articles","previous_headings":"","what":"Key Features","title":"Why MolPad?","text":"Trajectory clustering organizing data clusters, helps identify patterns, relationships, underlying structures within dataset. essence, clustering detects groups features observations behave similarly, allowing deeper insights effective decision-making. Network extracting build network clustered trajectories adapting GENIE3 algorithm. Specifically, network learned series regressions, cluster centroid predicted expression patterns cluster centroids using random forests. choose random forests potential model interacting features non-linearity without strong assumptions. Interactive Dashboard package provides interactive dashboard designed help users explore data uncover relationships within complex networks. utilizing focus-plus-context visualization, dashboard allows users zoom specific details keeping broader dataset view. approach enables deeper understanding data, making easier identify patterns make informed decisions.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/whymolpad.html","id":"what-does-it-look-like","dir":"Articles","previous_headings":"","what":"What does it look like?","title":"Why MolPad?","text":"Now, let’s see can get dashboard. ’ll start overview demonstrate discover patterns within data.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/whymolpad.html","id":"molpad-dashboard-overview","dir":"Articles","previous_headings":"What does it look like?","what":"MolPad Dashboard Overview","title":"Why MolPad?","text":"overview MolPad Dashboard. explore dashboard effectively, can start following sequence -B-C-D. approach guide cluster-level network, taxonomic-level bar plot, type-level line plot, feature-level table. , can modify detailed settings identify investigate special patterns within data.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/whymolpad.html","id":"discovering-related-patterns","dir":"Articles","previous_headings":"What does it look like?","what":"Discovering related patterns","title":"Why MolPad?","text":"short example discovering related patterns using network plot: shade edges represents proximity nodes. brushed area, Groups 1-7-8 (circled solid black lines) Groups 1-2 (circled blue dashed lines) strongly linked. Groups 1, 7, 8, patterns form W-shape noticeable peak time section. Although Group 1 shows higher volatility compared Group 2, groups display highly overlapping increasing trend. detailed instructions, please visit “Get Started” page relevant function reference page.","code":""},{"path":"https://kaiyanm.github.io/MolPad/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Kaiyan Ma. Author, maintainer.","code":""},{"path":"https://kaiyanm.github.io/MolPad/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Ma K (2024). MolPad: MolPad: R-Shiny Package Cluster Co-Expression Analysis Longitudinal Microbiomics. R package version 0.1.0, https://kaiyanm.github.io/MolPad/.","code":"@Manual{, title = {MolPad: MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics}, author = {Kaiyan Ma}, year = {2024}, note = {R package version 0.1.0}, url = {https://kaiyanm.github.io/MolPad/}, }"},{"path":"https://kaiyanm.github.io/MolPad/index.html","id":"molpad-","dir":"","previous_headings":"","what":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","title":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","text":"R-Shiny Package Cluster Co-Expression Analysis Longitudinal Microbiomics","code":""},{"path":"https://kaiyanm.github.io/MolPad/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","text":"MolPad offers visualization dashboard tool designed enhance understanding molecular co-expression works context microbiome data. approach involves using cluster network provide initial overview relationships across multiple omics, added functionality interactively zoom specific areas interest. facilitate analysis, ’ve developed focus-plus-context strategy connects online curated annotations. Additionally, package simplifies entire pipeline creating dashboard. user-friendly design makes accessible even people limited R programming experience. Check cheese-data demo .","code":""},{"path":"https://kaiyanm.github.io/MolPad/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","text":"can either install devtools, {r, eval = FALSE} # Install package R: install.packages(\"devtools\") library(devtools) install_github(\"KaiyanM/MolPad\") clone repository local computer (example, onto ./Github) installing: {r, eval = FALSE} # Download install package R: setwd(\"./GitHub\") install(\"MolPad\") , load package: {r,eval=FALSE} library(MolPad)","code":""},{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/index.html","id":"molpad-could-help-you-with","dir":"","previous_headings":"Usage","what":"MolPad could help you with:","title":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","text":"Clustering data k-means building group network. Find significant trend patterns datasets. Target interaction groups, taxons, pathways. Visualize distribution features specific pathways group network. Search particular features user-defined labels. Check detailed information feature automatically generated hyperlinks. better overall understanding datasets.","code":""},{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/index.html","id":"getting-help","dir":"","previous_headings":"","what":"Getting Help","title":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","text":"need assistance MolPad, two primary ways seek help: Ask us anything related MolPad! add question, create issue repository. Stack Overflow another excellent resource answering common issues R. Remember ’s particularly effective can provide reproducible example shows specific problem ’re .","code":""},{"path":"https://kaiyanm.github.io/MolPad/index.html","id":"contribution","dir":"","previous_headings":"","what":"Contribution","title":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","text":"contribute project, use following workflow: fork repository –> create local copy –> submit pull request.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":null,"dir":"Reference","previous_headings":"","what":"Cheese data — cheese","title":"Cheese data — cheese","text":"context cheese production, regular application brine solution maturation technique promotes uniformity microbial populations cheese's surface facilitates interactions among microorganisms. investigation involved analysis longitudinal dataset encompassing three washed-rind cheese communities sampled ripening process.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Cheese data — cheese","text":"two datasets: cheese annotations","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Cheese data — cheese","text":"Reference: doi:10.1128/msystems.00701-22","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Cheese data — cheese","text":"study uncovered remarkably consistent microbial progression within cheese. bacterial community, Firmicutes dominate outset, Proteobacteria swiftly assuming dominance end ripening period. Additionally, Cheese Cheese C consistently demonstrate establishment Actinobacteria Bacteroidetes, distinct manner. corroborate findings using MolPad dashboard, conducted analysis two cheeses (C) three production batches, spanning weeks 2 13.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":"cheese-data","dir":"Reference","previous_headings":"","what":"cheese data","title":"Cheese data — cheese","text":"data frame 106239 rows 18 variables: ID sample ID A_1~A_5 Time series data measured cheese . C_1~C_5 Time series data measured cheese C. domain category feature phylum category feature class category feature order category feature family category feature genus category feature","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":"annotation-data","dir":"Reference","previous_headings":"","what":"annotation data","title":"Cheese data — cheese","text":"data frame 86156 rows 9 variables: ID sample ID GO_ID GO IDs, represents link gene product type molecular function KEGG_ID KEGG IDs, linking genomic information higher order functional information. domain category feature phylum category feature class category feature order category feature family category feature genus category feature","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cheese data — cheese","text":"","code":"data(cheese) head(annotations) #> # A tibble: 6 × 9 #> ID GO_ID KEGG_ID domain phylum class order family genus #> #> 1 9 - - Bacteria Pseudomonadota Alphaproteobac… Caul… Caulo… Caul… #> 2 10 - - Bacteria Pseudomonadota Alphaproteobac… Caul… Caulo… Caul… #> 3 11 - - Bacteria Pseudomonadota Alphaproteobac… Hyph… Barto… Bart… #> 4 12 - - Bacteria Pseudomonadota Alphaproteobac… Hyph… Bruce… Bruc… #> 5 13 - - Bacteria Pseudomonadota Alphaproteobac… Hyph… Nitro… Brad… #> 6 14 - - Bacteria Pseudomonadota Alphaproteobac… Hyph… Phyll… Meso… head(cheese) #> # A tibble: 6 × 18 #> ID A_1 A_2 A_3 A_4 A_5 C_1 C_3 C_4 C_5 GO_ID KEGG_ID #> #> 1 1 38 23 27 5 11 13 1 3 0 NA NA #> 2 2 23 6 4 0 9 21 1 0 2 NA NA #> 3 3 24 5 37 10 19 3 0 1 4 NA NA #> 4 4 3 2 4 2 16 56 1 7 17 NA NA #> 5 5 58 14 45 13 32 82 3 8 3 NA NA #> 6 6 12 9 14 4 13 2 0 1 4 NA NA #> # ℹ 6 more variables: domain , phylum , class , order , #> # family , genus "},{"path":"https://kaiyanm.github.io/MolPad/reference/color_palettes__.html","id":null,"dir":"Reference","previous_headings":"","what":"Color palettes — color_palettes__","title":"Color palettes — color_palettes__","text":"internal function built-color palettes.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/color_palettes__.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Color palettes — color_palettes__","text":"","code":"color_palettes__(name)"},{"path":"https://kaiyanm.github.io/MolPad/reference/color_palettes__.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Color palettes — color_palettes__","text":"name string two options: \"graytone\" \"darkwarm\".","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/color_palettes__.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Color palettes — color_palettes__","text":"","code":"color_palettes__(\"graytone\") #> [1] \"#c3c5c7\" \"#30beba\" \"#998a73\" \"#807566\" \"#a5b1c9\" \"#5d5232\" \"#9a2b41\" #> [8] \"#d6b7a2\" \"#882db4\" \"#b47a53\" color_palettes__(\"darkwarm\") #> [1] \"#251305\" \"#C70A80\" \"#FBCB0A\" \"#ff1122\" \"#7D7463\" \"#CECE5A\" \"#FF9B9B\" #> [8] \"#A459D1\" \"#00235B\" \"#7E1717\""},{"path":"https://kaiyanm.github.io/MolPad/reference/convert_range.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert range — convert_range","title":"Convert range — convert_range","text":"internal function range conversion. element x, returns distance minimal values divided range.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/convert_range.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert range — convert_range","text":"","code":"convert_range(x)"},{"path":"https://kaiyanm.github.io/MolPad/reference/convert_range.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert range — convert_range","text":"x vector numbers.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/convert_range.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert range — convert_range","text":"vector calculated (x - min(x)) / (max(x) - min(x))","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/convert_range.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert range — convert_range","text":"","code":"convert_range(5:10) #> [1] 0.0 0.2 0.4 0.6 0.8 1.0"},{"path":"https://kaiyanm.github.io/MolPad/reference/extend.color__.html","id":null,"dir":"Reference","previous_headings":"","what":"Extend color palettes — extend.color__","title":"Extend color palettes — extend.color__","text":"internal function designed pair vectors color palettes automatically generate colors longer vectors find match.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/extend.color__.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extend color palettes — extend.color__","text":"","code":"extend.color__(n, colors, extendby = 1, alpha = 1, ...)"},{"path":"https://kaiyanm.github.io/MolPad/reference/extend.color__.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extend color palettes — extend.color__","text":"n number. length feature vector want match colors. colors vector colors (finite). extendby number select 1, 2, 3, 4 5, representing distinct auto-fill schemes. alpha number select range 0,1.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gAnnotation.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate processed annotation — gAnnotation","title":"Generate processed annotation — gAnnotation","text":"gAnnotation() provides standard input format dashboard, allowing users select two columns primary factors wish visualize describe data.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gAnnotation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate processed annotation — gAnnotation","text":"","code":"gAnnotation(data, first_order, second_order)"},{"path":"https://kaiyanm.github.io/MolPad/reference/gAnnotation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate processed annotation — gAnnotation","text":"data data.frame containing annotations used describing features measured time point. also required include ID least two categorical variables. first_order string. name one column categorical variable data. second_order string. name another column categorical variable data.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gAnnotation.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate processed annotation — gAnnotation","text":"Guidelines Selecting Annotations: first-order annotation recommended functional, pathway functional system. parameter primarily serve purpose filtering one network time displaying dashboard. second-order annotation utilized illustrate composition first-order annotation using bar plot. Therefore, better set taxon, class label, etc.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gAnnotation.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate processed annotation — gAnnotation","text":"","code":"data(test_data) test_annotations_processed <- gAnnotation(test_annotations,system,class) head(test_annotations_processed) #> ID #> 1 1 #> 2 2 #> 3 3 #> 4 4 #> 5 5 #> 6 6 #> GO_ID #> 1 #> 2 #> 3 GO:0003674,GO:0003824,GO:0005575,GO:0006629,GO:0006644,GO:0006650,GO:0006655,GO:0006793,GO:0006796,GO:0008150,GO:0008152,GO:0008610,GO:0008654,GO:0008808,GO:0009058,GO:0009987,GO:0016020,GO:0016740,GO:0016772,GO:0016780,GO:0019637,GO:0030572,GO:0032048,GO:0032049,GO:0044237,GO:0044238,GO:0044249,GO:0044255,GO:0045017,GO:0046471,GO:0046474,GO:0046486,GO:0071704,GO:0090407,GO:1901576 #> 4 GO:0003674,GO:0003676,GO:0003723,GO:0003729,GO:0003824,GO:0004654,GO:0005488,GO:0005575,GO:0005622,GO:0005623,GO:0005737,GO:0006139,GO:0006401,GO:0006402,GO:0006725,GO:0006807,GO:0008150,GO:0008152,GO:0009056,GO:0009057,GO:0009892,GO:0009987,GO:0010468,GO:0010605,GO:0010629,GO:0016070,GO:0016071,GO:0016740,GO:0016772,GO:0016779,GO:0019222,GO:0019439,GO:0034641,GO:0034655,GO:0043170,GO:0044237,GO:0044238,GO:0044248,GO:0044260,GO:0044265,GO:0044270,GO:0044424,GO:0044464,GO:0046483,GO:0046700,GO:0048519,GO:0050789,GO:0060255,GO:0065007,GO:0071704,GO:0090304,GO:0097159,GO:1901360,GO:1901361,GO:1901363,GO:1901575 #> 5 #> 6 #> KEGG_ID Pathway taxonomic.scope #> 1 K07124 Integumentary System hormonal proteins #> 2 Skeletal System structural proteins #> 3 K06131 Muscular System enzymes #> 4 K00962 Nervous System contractile proteins #> 5 K02083 Endocrine System contractile proteins #> 6 Cardiovascular System structural proteins"},{"path":"https://kaiyanm.github.io/MolPad/reference/gClusters.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate clusters — gClusters","title":"Generate clusters — gClusters","text":"gClusters() returns clusters generated k-means yield elbow plot way finding optimal parameter.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gClusters.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate clusters — gClusters","text":"","code":"gClusters(data, ncluster = 20, elbow.max = 50, ...)"},{"path":"https://kaiyanm.github.io/MolPad/reference/gClusters.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate clusters — gClusters","text":"data scaled data.frame contain variables ID, value time_1, ..., value time_k, type extracting patterns across time. See also pre_process(). ncluster number clusters. related complexity information network: choosing ncluster, suggest thinking many nodes show visualization representative want clustered pattern. elbow.max number maximum value x-axis elbow method plot. larger expected ncluster smaller sample size. iter.max number maximum iterations allowed k-means. See also stats::kmeans. nstart number random attempts generating initial configurations. k-means algorithm choose best one among attempts. larger data, 'nstart' can set lower just set 1. See also stats::kmeans.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gClusters.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate clusters — gClusters","text":"function return list 2 elements: k-means cluster result elbow method plot.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gClusters.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate clusters — gClusters","text":"determine optimal number clusters (ncluster), advised closely examine elbow plot identify point graph substantial change 'elbow' occurs. often indicative suitable cluster count. cases dataset extensive intricate, might consider increasing value elbow.max ensure comprehensive exploration potential cluster counts. can help achieving accurate meaningful results, especially working larger complex datasets. function can executed data parameter outset. However, achieve best clustering results, adjustments recommended. initial run, users expected adjust function's parameters based clustering outcomes elbow plot analysis.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gClusters.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate clusters — gClusters","text":"","code":"data(test_data) reslist <- gClusters(test_data_processed) # k-means result reslist[[1]] #> K-means clustering with 20 clusters of sizes 6, 3, 6, 5, 5, 8, 6, 5, 5, 6, 4, 11, 3, 3, 3, 2, 4, 2, 2, 11 #> #> Cluster means: #> T1 T2 T3 T4 T5 T6 #> 1 2.0919348 -0.33320285 -0.50786563 -0.5579659 -0.5579659 1.5455977 #> 2 0.8277105 1.32036794 -0.09261332 1.2325986 0.4199176 -0.9576634 #> 3 0.5077879 -0.24564283 0.53503708 0.2363183 1.0793829 1.3583848 #> 4 0.2914366 -0.41308350 -0.76541563 -0.9724353 -0.9724353 1.6040887 #> 5 1.2081037 -0.66790409 -0.66790409 -0.9275526 -0.4204221 1.2719890 #> 6 0.1155971 -0.19000668 -0.25629794 -0.1487697 -0.4383348 2.5830148 #> 7 1.2574004 -0.64914997 -0.87502695 0.9353966 0.4760273 1.2681050 #> 8 0.9389212 -0.86278238 0.43887930 1.0009533 1.3733646 -1.0423264 #> 9 -0.8135539 -0.84696982 -0.32573017 1.1218534 2.0955812 -0.5620731 #> 10 -0.3802219 -0.36840199 0.02254271 1.6459653 1.9633618 -0.3246455 #> 11 -0.1225506 -0.44139873 -0.19548572 1.0299038 2.3840629 -0.2147528 #> 12 2.5784883 -0.02446156 -0.60355041 -0.2547486 -0.5120230 -0.3947043 #> 13 0.9922755 0.32399254 -0.94565021 -0.7125240 -0.5104935 -0.9456502 #> 14 0.2699801 -0.08094677 -0.28926312 -0.7813127 -0.6065051 -0.1494523 #> 15 1.7947637 -0.47746004 -0.52783931 -0.7635958 -0.7635958 -0.6438957 #> 16 0.1789247 1.26157166 0.82115649 0.3899368 1.2707671 -0.2614905 #> 17 1.4517382 0.26236463 1.31600581 -0.2531012 0.2004547 -1.1238942 #> 18 -0.2784769 -0.82258408 -0.82258408 -0.8225841 1.2338761 -0.8225841 #> 19 2.1235865 1.05868538 -0.39467611 0.1315587 -0.1315587 0.3946761 #> 20 -0.2262821 -0.73107613 -0.66098092 -0.4323193 -0.4035074 -0.3836848 #> T7 T8 T9 T10 #> 1 -0.425513174 -0.557965933 -0.30571480 -0.391338269 #> 2 -1.160107459 -0.295057401 -0.64757654 -0.647576540 #> 3 -1.006282312 -0.766188425 -0.64803067 -1.050766722 #> 4 0.301678216 -0.371684852 0.66435231 0.633498705 #> 5 1.209152596 -0.095960244 -0.32347149 -0.586030729 #> 6 -0.006327004 -0.327677972 -0.66559889 -0.665598891 #> 7 -0.354972016 -0.601904415 -0.82778139 -0.628094550 #> 8 -1.159101258 -0.345888528 -0.27147562 -0.070544200 #> 9 -0.651292750 -0.498839228 0.42446013 0.056564270 #> 10 -0.647007074 -0.667438225 -0.63229313 -0.611861976 #> 11 -0.400726188 -0.616324716 -0.71136401 -0.711364008 #> 12 -0.526022073 -0.185303954 -0.06989626 -0.007778173 #> 13 -0.945650212 0.121962031 1.19430596 1.427432164 #> 14 -0.763902351 -0.412975440 0.39220455 2.422173070 #> 15 -0.260645101 0.389799714 -0.08327821 1.335746686 #> 16 -0.912917717 -0.481698036 -1.13312530 -1.133125299 #> 17 -0.987867401 -0.340013670 -0.33085684 -0.194830041 #> 18 -0.822584085 1.233876127 1.23387613 0.689768939 #> 19 -0.795567972 -0.795567972 -0.79556797 -0.795567972 #> 20 -0.612869917 -0.006483243 1.70560793 1.751595921 #> #> Clustering vector: #> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 #> 20 1 18 16 3 20 5 19 10 9 8 14 15 12 2 9 6 20 17 12 #> 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 #> 7 20 6 1 12 9 7 12 6 11 5 11 16 18 10 3 8 13 20 2 #> 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 #> 6 1 20 20 7 20 6 3 1 6 15 5 4 13 6 5 4 10 12 10 #> 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 #> 8 3 7 13 3 1 12 7 17 17 14 9 6 19 14 10 2 11 8 7 #> 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 #> 8 20 15 4 4 1 17 12 4 12 10 5 11 12 20 3 9 12 20 12 #> #> Within cluster sum of squares by cluster: #> [1] 3.0260293 5.0357352 13.2343206 13.1118010 10.3695240 8.0412334 #> [7] 11.7251902 8.3255313 3.4617088 2.4693324 1.6353685 13.0645655 #> [13] 2.9416715 3.1842269 5.0413438 2.4669004 9.9418120 0.9921913 #> [19] 0.9833964 12.4042363 #> (between_SS / total_SS = 83.1 %) #> #> Available components: #> #> [1] \"cluster\" \"centers\" \"totss\" \"withinss\" \"tot.withinss\" #> [6] \"betweenss\" \"size\" \"iter\" \"ifault\" # elbow plot reslist[[2]]"},{"path":"https://kaiyanm.github.io/MolPad/reference/gDashboard.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate shiny dashboard — gDashboard","title":"Generate shiny dashboard — gDashboard","text":"outputs g-functions, ready create custom Molpad dashboard. sure specify web ID columns corresponding column names.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gDashboard.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate shiny dashboard — gDashboard","text":"","code":"gDashboard( data, cluster, annotation, networkres, dashboardtitle = \"MolPad Dashboard\", id_colname = NULL, id_type = NULL )"},{"path":"https://kaiyanm.github.io/MolPad/reference/gDashboard.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate shiny dashboard — gDashboard","text":"data output pre_process():scaled data.frame contain variables ID, value time_1, ..., value time_k, type. cluster output gClusters(): list contains result k-means Cluster means, vectors sum squares. annotation output gAnnotation(): data.frame containing annotations describing features. Variables must include ID, Pathway, taxonomic.scope. Note NA permitted 3 variables. networkres output gNetwork(): data.frame 4 variables weight,IncNodePurity,var_names,. dashboardtitle string customized dashboard name. id_colname single string string vector. column names annotation dataset vector contains external database IDs. id_type single string string vector. corresponding database names columns, must chosen \"KEGG\" \"GO\".","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gDashboard.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate shiny dashboard — gDashboard","text":"Please ensure columns containing external database IDs adhere following standards: KEGG ID: Begin 'K' followed 5 digits, example, K05685 K06671. GO ID: Begin 'GO:' followed 7 digits, GO:0003674.\"","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gDashboard.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate shiny dashboard — gDashboard","text":"","code":"if (FALSE) { data(test_data) gDashboard(test_data_processed,test_cluster,test_annotations_processed,test_network,id_colname = c(\"GO_ID\",\"KEGG_ID\"),id_type = c(\"GO\",\"KEGG\")) }"},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate prediction network — gNetwork","title":"Generate prediction network — gNetwork","text":"gNetwork() generates prediction network functional annotation. every feature, features considered independent variables, top predictors selected based %IncMSE.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate prediction network — gNetwork","text":"","code":"gNetwork(clusters, ntop = 10, method = \"randomforest\")"},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate prediction network — gNetwork","text":"clusters list two outputs gClusters(). first element k-means result (See also stats::kmeans), element plot automatically omitted ease directly passing results function. ntop number pick set top n predictors(default: top 10) feature important ones. used construct network among clustered patterns. seed number random seed.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate prediction network — gNetwork","text":"assess relationships clusters using Mean Squared Error (MSE) changes resulting random shuffling. gNetwork()'s output includes edge weights node pairs, essential inputs dashboard.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate prediction network — gNetwork","text":"","code":"data(test_data) networkres <- gNetwork(test_cluster, ntop = 3) head(networkres) #> weight IncNodePurity var_names from #> 1 1.4215243 1.1776012 Group_5 Group_1 #> 2 0.7911266 0.7011049 Group_3 Group_1 #> 3 -1.9891284 0.5811186 Group_2 Group_1 #> 4 5.8098014 1.1107592 Group_4 Group_2 #> 5 5.4000389 0.8744358 Group_5 Group_2 #> 6 1.2492288 1.1816976 Group_1 Group_2 gNetwork_view(networkres)"},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork_view.html","id":null,"dir":"Reference","previous_headings":"","what":"View the feature importance plot — gNetwork_view","title":"View the feature importance plot — gNetwork_view","text":"gNetwork_view() Generate feature importance plot random forest result. plot visually highlights importance individual cluster within dataset, helping identify key factors predictive modeling.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork_view.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"View the feature importance plot — gNetwork_view","text":"","code":"gNetwork_view(data)"},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork_view.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"View the feature importance plot — gNetwork_view","text":"data dataframe; output gNetwork(), including 4 variables: weight, IncNodePurity, var_names . weight edge value, IncNodePurity(%IncMSE) measure predictions result var_names permuted. dependent variable round random forest.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork_view.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"View the feature importance plot — gNetwork_view","text":"","code":"data(test_data) gNetwork_view(test_network) #> Warning: Removed 1 rows containing missing values (`geom_segment()`). #> Warning: Removed 1 rows containing missing values (`geom_point()`)."},{"path":"https://kaiyanm.github.io/MolPad/reference/make_line_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Make line plot — make_line_plot","title":"Make line plot — make_line_plot","text":"Generate line ribbon plot dashboard.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_line_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make line plot — make_line_plot","text":"","code":"make_line_plot(dfgroup_long, selected_groups, selected_taxa)"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_line_plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Make line plot — make_line_plot","text":"function makes ribbon plot every brush action Shiny show min, max, mean value clustered pattern group across time. ribbon plot grouped colored type variable input datasets. See also ggplot2::geom_ribbon.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_line_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Make line plot — make_line_plot","text":"","code":"data(test_data) make_line_plot(test_maindata, \"Group_5\", c(\"hormonal proteins\",\"structural proteins\",\"enzymes\",\"storage proteins\",\"antibodies\",\"transport proteins\"))"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_stackbar_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Make stackbar plot — make_stackbar_plot","title":"Make stackbar plot — make_stackbar_plot","text":"Generate stackbar plot dashboard.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_stackbar_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make stackbar plot — make_stackbar_plot","text":"","code":"make_stackbar_plot(dfgroup_long, selected_groups, selected_taxa)"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_stackbar_plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Make stackbar plot — make_stackbar_plot","text":"function makes bar plot every brush action Shiny show components clustered pattern group. bar plot colored taxonomic.scope variable processed annotation dataset generated gAnnotation(). See also ggplot2::geom_bar.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_stackbar_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Make stackbar plot — make_stackbar_plot","text":"","code":"data(test_data) make_stackbar_plot(test_maindata, \"Group_5\", c(\"hormonal proteins\",\"structural proteins\",\"enzymes\",\"storage proteins\",\"antibodies\",\"transport proteins\"))"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_graph.html","id":null,"dir":"Reference","previous_headings":"","what":"Make graph plot — make_the_graph","title":"Make graph plot — make_the_graph","text":"Generate graph dashboard.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_graph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make graph plot — make_the_graph","text":"","code":"make_the_graph(ptw, network_output, min_weight, s_ptw, graph_layout)"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_graph.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Make graph plot — make_the_graph","text":"function makes network plot every Pathway selection Shiny show relationship among clustered pattern groups. network plot built dataset generated gNetwork() can adjusted layout minimum weights. See also ggraph::ggraph.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_graph.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Make graph plot — make_the_graph","text":"","code":"data(test_data) make_the_graph(test_graphptw, test_network, 0.03, \"Muscular System\",\"kk\")"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_table.html","id":null,"dir":"Reference","previous_headings":"","what":"Make table from brushed region — make_the_table","title":"Make table from brushed region — make_the_table","text":"Generate table selected groups dashboard.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make table from brushed region — make_the_table","text":"","code":"make_the_table(p)"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Make table from brushed region — make_the_table","text":"p ggplot output, see also ggplot2::ggplot_build.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_table.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Make table from brushed region — make_the_table","text":"function simply aims collect position information brushed area network plot returns annotation table corresponding features. takes plot object produce object information table.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/mass_produce_lm__.html","id":null,"dir":"Reference","previous_headings":"","what":"Mass produce linear model — mass_produce_lm__","title":"Mass produce linear model — mass_produce_lm__","text":"Take column dependent vairable time, produce n linear models n columns dataset.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/mass_produce_lm__.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mass produce linear model — mass_produce_lm__","text":"","code":"mass_produce_lm__(data)"},{"path":"https://kaiyanm.github.io/MolPad/reference/mass_produce_lm__.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mass produce linear model — mass_produce_lm__","text":"data dataframe.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/mass_produce_lm__.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Mass produce linear model — mass_produce_lm__","text":"internal function designed automatically generate list functions regression. column considered response variable columns. resulting functions can utilized inputs random forest regression gNetwork().","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/match.color__.html","id":null,"dir":"Reference","previous_headings":"","what":"Match color — match.color__","title":"Match color — match.color__","text":"Match vector finite list colors.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/match.color__.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Match color — match.color__","text":"","code":"match.color__(valist, mycolors, extendby = 5)"},{"path":"https://kaiyanm.github.io/MolPad/reference/match.color__.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Match color — match.color__","text":"valist vector want specify colors element. mycolors vector colors. extendby number select 1, 2, 3, 4, 5, representing distinct auto-fill schemes.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/match.color__.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Match color — match.color__","text":"","code":"my_vector <- paste0(\"N_\",1:10) match.color__(my_vector,c(\"red\",\"yellow\",\"blue\")) #> N_1 N_2 N_3 N_4 N_5 #> \"red\" \"yellow\" \"blue\" \"white\" \"antiquewhite3\" #> N_6 N_7 N_8 N_9 N_10 #> \"aquamarine3\" \"azure3\" \"bisque2\" \"blue\" \"blueviolet\""},{"path":"https://kaiyanm.github.io/MolPad/reference/match_database.html","id":null,"dir":"Reference","previous_headings":"","what":"Match database — match_database","title":"Match database — match_database","text":"matches selected columns online database names.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/match_database.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Match database — match_database","text":"","code":"match_database(data, id_colname, id_type)"},{"path":"https://kaiyanm.github.io/MolPad/reference/match_database.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Match database — match_database","text":"match_database() internal function matches selected columns dataset corresponding databases. Currently MolPad support two types annotation database: GO KEGG. want custom URL platform, download modify paste_URL() function.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/match_database.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Match database — match_database","text":"","code":"data(test_data) head(test_annotations_processed) #> ID #> 1 1 #> 2 2 #> 3 3 #> 4 4 #> 5 5 #> 6 6 #> GO_ID #> 1 #> 2 #> 3 GO:0003674,GO:0003824,GO:0005575,GO:0006629,GO:0006644,GO:0006650,GO:0006655,GO:0006793,GO:0006796,GO:0008150,GO:0008152,GO:0008610,GO:0008654,GO:0008808,GO:0009058,GO:0009987,GO:0016020,GO:0016740,GO:0016772,GO:0016780,GO:0019637,GO:0030572,GO:0032048,GO:0032049,GO:0044237,GO:0044238,GO:0044249,GO:0044255,GO:0045017,GO:0046471,GO:0046474,GO:0046486,GO:0071704,GO:0090407,GO:1901576 #> 4 GO:0003674,GO:0003676,GO:0003723,GO:0003729,GO:0003824,GO:0004654,GO:0005488,GO:0005575,GO:0005622,GO:0005623,GO:0005737,GO:0006139,GO:0006401,GO:0006402,GO:0006725,GO:0006807,GO:0008150,GO:0008152,GO:0009056,GO:0009057,GO:0009892,GO:0009987,GO:0010468,GO:0010605,GO:0010629,GO:0016070,GO:0016071,GO:0016740,GO:0016772,GO:0016779,GO:0019222,GO:0019439,GO:0034641,GO:0034655,GO:0043170,GO:0044237,GO:0044238,GO:0044248,GO:0044260,GO:0044265,GO:0044270,GO:0044424,GO:0044464,GO:0046483,GO:0046700,GO:0048519,GO:0050789,GO:0060255,GO:0065007,GO:0071704,GO:0090304,GO:0097159,GO:1901360,GO:1901361,GO:1901363,GO:1901575 #> 5 #> 6 #> KEGG_ID Pathway taxonomic.scope #> 1 K07124 Integumentary System hormonal proteins #> 2 Skeletal System structural proteins #> 3 K06131 Muscular System enzymes #> 4 K00962 Nervous System contractile proteins #> 5 K02083 Endocrine System contractile proteins #> 6 Cardiovascular System structural proteins head(match_database(test_annotations_processed,id_colname = c(\"GO_ID\",\"KEGG_ID\"),id_type = c(\"GO\",\"KEGG\"))) #> ID #> 1 1 #> 2 2 #> 3 3 #> 4 4 #> 5 5 #> 6 6 #> GO_ID #> 1 #> 2 #> 3 GO:0003674<\/a>
GO:0003824<\/a>
GO:0005575<\/a>
GO:0006629<\/a>
GO:0006644<\/a>
GO:0006650<\/a>
GO:0006655<\/a>
GO:0006793<\/a>
GO:0006796<\/a>
GO:0008150<\/a>
GO:0008152<\/a>
GO:0008610<\/a>
GO:0008654<\/a>
GO:0008808<\/a>
GO:0009058<\/a>
GO:0009987<\/a>
GO:0016020<\/a>
GO:0016740<\/a>
GO:0016772<\/a>
GO:0016780<\/a>
GO:0019637<\/a>
GO:0030572<\/a>
GO:0032048<\/a>
GO:0032049<\/a>
GO:0044237<\/a>
GO:0044238<\/a>
GO:0044249<\/a>
GO:0044255<\/a>
GO:0045017<\/a>
GO:0046471<\/a>
GO:0046474<\/a>
GO:0046486<\/a>
GO:0071704<\/a>
GO:0090407<\/a>
GO:1901576<\/a> #> 4 GO:0003674<\/a>
GO:0003676<\/a>
GO:0003723<\/a>
GO:0003729<\/a>
GO:0003824<\/a>
GO:0004654<\/a>
GO:0005488<\/a>
GO:0005575<\/a>
GO:0005622<\/a>
GO:0005623<\/a>
GO:0005737<\/a>
GO:0006139<\/a>
GO:0006401<\/a>
GO:0006402<\/a>
GO:0006725<\/a>
GO:0006807<\/a>
GO:0008150<\/a>
GO:0008152<\/a>
GO:0009056<\/a>
GO:0009057<\/a>
GO:0009892<\/a>
GO:0009987<\/a>
GO:0010468<\/a>
GO:0010605<\/a>
GO:0010629<\/a>
GO:0016070<\/a>
GO:0016071<\/a>
GO:0016740<\/a>
GO:0016772<\/a>
GO:0016779<\/a>
GO:0019222<\/a>
GO:0019439<\/a>
GO:0034641<\/a>
GO:0034655<\/a>
GO:0043170<\/a>
GO:0044237<\/a>
GO:0044238<\/a>
GO:0044248<\/a>
GO:0044260<\/a>
GO:0044265<\/a>
GO:0044270<\/a>
GO:0044424<\/a>
GO:0044464<\/a>
GO:0046483<\/a>
GO:0046700<\/a>
GO:0048519<\/a>
GO:0050789<\/a>
GO:0060255<\/a>
GO:0065007<\/a>
GO:0071704<\/a>
GO:0090304<\/a>
GO:0097159<\/a>
GO:1901360<\/a>
GO:1901361<\/a>
GO:1901363<\/a>
GO:1901575<\/a> #> 5 #> 6 #> KEGG_ID #> 1 K07124<\/a> #> 2 #> 3 K06131<\/a> #> 4 K00962<\/a> #> 5 K02083<\/a> #> 6 #> Pathway taxonomic.scope #> 1 Integumentary System hormonal proteins #> 2 Skeletal System structural proteins #> 3 Muscular System enzymes #> 4 Nervous System contractile proteins #> 5 Endocrine System contractile proteins #> 6 Cardiovascular System structural proteins"},{"path":"https://kaiyanm.github.io/MolPad/reference/paste_URL.html","id":null,"dir":"Reference","previous_headings":"","what":"Paste URL — paste_URL","title":"Paste URL — paste_URL","text":"Retrieve database IDs associate respective URLs.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/paste_URL.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Paste URL — paste_URL","text":"","code":"paste_URL(x, id_type)"},{"path":"https://kaiyanm.github.io/MolPad/reference/paste_URL.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Paste URL — paste_URL","text":"","code":"data(test_data) paste_URL(test_annotations$GO_ID[1:4], id_type = \"GO\") #> [1] NA #> [2] NA #> [3] \"GO:0003674<\/a>
GO:0003824<\/a>
GO:0005575<\/a>
GO:0006629<\/a>
GO:0006644<\/a>
GO:0006650<\/a>
GO:0006655<\/a>
GO:0006793<\/a>
GO:0006796<\/a>
GO:0008150<\/a>
GO:0008152<\/a>
GO:0008610<\/a>
GO:0008654<\/a>
GO:0008808<\/a>
GO:0009058<\/a>
GO:0009987<\/a>
GO:0016020<\/a>
GO:0016740<\/a>
GO:0016772<\/a>
GO:0016780<\/a>
GO:0019637<\/a>
GO:0030572<\/a>
GO:0032048<\/a>
GO:0032049<\/a>
GO:0044237<\/a>
GO:0044238<\/a>
GO:0044249<\/a>
GO:0044255<\/a>
GO:0045017<\/a>
GO:0046471<\/a>
GO:0046474<\/a>
GO:0046486<\/a>
GO:0071704<\/a>
GO:0090407<\/a>
GO:1901576<\/a>\" #> [4] \"GO:0003674<\/a>
GO:0003676<\/a>
GO:0003723<\/a>
GO:0003729<\/a>
GO:0003824<\/a>
GO:0004654<\/a>
GO:0005488<\/a>
GO:0005575<\/a>
GO:0005622<\/a>
GO:0005623<\/a>
GO:0005737<\/a>
GO:0006139<\/a>
GO:0006401<\/a>
GO:0006402<\/a>
GO:0006725<\/a>
GO:0006807<\/a>
GO:0008150<\/a>
GO:0008152<\/a>
GO:0009056<\/a>
GO:0009057<\/a>
GO:0009892<\/a>
GO:0009987<\/a>
GO:0010468<\/a>
GO:0010605<\/a>
GO:0010629<\/a>
GO:0016070<\/a>
GO:0016071<\/a>
GO:0016740<\/a>
GO:0016772<\/a>
GO:0016779<\/a>
GO:0019222<\/a>
GO:0019439<\/a>
GO:0034641<\/a>
GO:0034655<\/a>
GO:0043170<\/a>
GO:0044237<\/a>
GO:0044238<\/a>
GO:0044248<\/a>
GO:0044260<\/a>
GO:0044265<\/a>
GO:0044270<\/a>
GO:0044424<\/a>
GO:0044464<\/a>
GO:0046483<\/a>
GO:0046700<\/a>
GO:0048519<\/a>
GO:0050789<\/a>
GO:0060255<\/a>
GO:0065007<\/a>
GO:0071704<\/a>
GO:0090304<\/a>
GO:0097159<\/a>
GO:1901360<\/a>
GO:1901361<\/a>
GO:1901363<\/a>
GO:1901575<\/a>\""},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":null,"dir":"Reference","previous_headings":"","what":"Pre-processing datasets — pre_process","title":"Pre-processing datasets — pre_process","text":"pre_process() function aids processing data inputs automatically establishes standardized format future use. allows two types data input: list datasets different sources long dataset containing specified last column type.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pre-processing datasets — pre_process","text":"","code":"pre_process( data, typenameList = NULL, replaceNA = TRUE, scale = TRUE, autoColName = \"Sec_\" )"},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pre-processing datasets — pre_process","text":"data data.frame describe feature one row. data contain variables ID value time_1, ..., value time_k, type extracting patterns across time. Note initial last column must exactly ID type. multiple data.frame format needs analyzed, also put list data.frame argument. case, variable type required generated next argument typenameList. typenameList vector strings. parameter used clarify source names data.frame, applicable input data list data.frame. default, set \"Dataset_1\", \"Dataset_2\", ..., etc. scale Logical; scale TRUE (default), standardize data.frame row base::scale. converts original value z-score. See also scale_by_row__(). autoColName string; autoColName -NULL (default), automatically set uniform column names data.frames. parameter applicable input data list data.frame. replaceNa Logical; replaceNa TRUE (default), replace NA 0.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pre-processing datasets — pre_process","text":"function returns long data.frame columns ID, value time_1, ..., value time_k, type.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Pre-processing datasets — pre_process","text":"consider two distinct scenarios application: one scenario, individuals collect several datasets various aspects instruments objects. example, might separately detecting lipids, metabolites, peptides specific soil sample. scenario, data uniform quality, can categorized larger groups exhibit significant differences. cases, pre_process() function serves valuable versatile tool. Yet, function optional generating dashboard. Users can perform processing long format matches required output. However, mindful number samples (timepoints) must greater 5 avoid potential errors subsequent prediction section.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pre-processing datasets — pre_process","text":"","code":"data(test_data) head(test_data, 10) #> ID T1 T2 T3 T4 T5 T6 T7 T8 T9 T10 type #> 1 1 1 0 0 1 1 0 0 1 6 6 type_A #> 2 2 6 0 0 0 0 3 1 0 2 1 type_A #> 3 3 1 0 0 0 2 0 0 2 2 1 type_A #> 4 4 4 5 3 3 7 2 1 1 0 0 type_A #> 5 5 4 3 NA 2 5 5 0 0 0 0 type_A #> 6 6 4 1 0 1 3 1 3 5 11 14 type_A #> 7 7 1 0 0 0 1 3 3 1 1 1 type_A #> 8 8 4 2 1 1 1 1 0 0 0 0 type_A #> 9 9 1 1 1 19 22 1 2 1 1 2 type_A #> 10 10 1 1 3 5 8 2 2 2 5 2 type_A a <- pre_process(test_data) head(a, 10) #> ID T1 T2 T3 T4 T5 T6 #> 1 1 -0.25354628 -0.6761234 -0.67612340 -0.25354628 -0.25354628 -0.6761234 #> 2 2 2.41458180 -0.6678631 -0.66786305 -0.66786305 -0.66786305 0.8733594 #> 3 3 0.21764288 -0.8705715 -0.87057150 -0.87057150 1.30585725 -0.8705715 #> 4 4 0.61658123 1.0569964 0.17616607 0.17616607 1.93782672 -0.2642491 #> 5 5 0.96186009 0.5038315 -0.87025436 0.04580286 1.41988870 1.4198887 #> 6 6 -0.06459959 -0.7105955 -0.92592741 -0.71059546 -0.27993154 -0.7105955 #> 7 7 -0.09086738 -0.9995412 -0.99954118 -0.99954118 -0.09086738 1.7264802 #> 8 8 2.40535118 0.8017837 0.00000000 0.00000000 0.00000000 0.0000000 #> 9 9 -0.50260633 -0.5026063 -0.50260633 1.70395805 2.07171878 -0.5026063 #> 10 10 -0.94019379 -0.9401938 -0.04477113 0.85065153 2.19378551 -0.4924825 #> T7 T8 T9 T10 type #> 1 -0.6761234 -0.25354628 1.85933936 1.85933936 type_A #> 2 -0.1541222 -0.66786305 0.35961857 -0.15412224 type_A #> 3 -0.8705715 1.30585725 1.30585725 0.21764288 type_A #> 4 -0.7046643 -0.70466426 -1.14507943 -1.14507943 type_A #> 5 -0.8702544 -0.87025436 -0.87025436 -0.87025436 type_A #> 6 -0.2799315 0.15073237 1.44272411 2.08871998 type_A #> 7 1.7264802 -0.09086738 -0.09086738 -0.09086738 type_A #> 8 -0.8017837 -0.80178373 -0.80178373 -0.80178373 type_A #> 9 -0.3800194 -0.50260633 -0.50260633 -0.38001942 type_A #> 10 -0.4924825 -0.49248246 0.85065153 -0.49248246 type_A"},{"path":"https://kaiyanm.github.io/MolPad/reference/reshape_for_make_functions.html","id":null,"dir":"Reference","previous_headings":"","what":"Reshape for 'make' functions — reshape_for_make_functions","title":"Reshape for 'make' functions — reshape_for_make_functions","text":"internal function produces three primary datasets dashboard intended \"make\" functions.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/reshape_for_make_functions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reshape for 'make' functions — reshape_for_make_functions","text":"","code":"reshape_for_make_functions(data, cluster, annotation, id_colname, id_type)"},{"path":"https://kaiyanm.github.io/MolPad/reference/reshape_for_make_functions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reshape for 'make' functions — reshape_for_make_functions","text":"data output pre_process() cluster output gClusters() annotation output gPathway() id_colname columns contain database IDs. id_type corresponding database names columns.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/reshape_for_make_functions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reshape for 'make' functions — reshape_for_make_functions","text":"","code":"data(test_data) l <- reshape_for_make_functions(test_data_processed, test_cluster, test_annotations_processed, id_colname = c(\"GO_ID\",\"KEGG_ID\"),id_type = c(\"GO\",\"KEGG\")) head(l[[1]]) #> ID cluster #> 1 1 Group_2 #> 2 2 Group_5 #> 3 3 Group_2 #> 4 4 Group_4 #> 5 5 Group_4 #> 6 6 Group_2 #> GO_ID #> 1 #> 2 #> 3 GO:0003674,GO:0003824,GO:0005575,GO:0006629,GO:0006644,GO:0006650,GO:0006655,GO:0006793,GO:0006796,GO:0008150,GO:0008152,GO:0008610,GO:0008654,GO:0008808,GO:0009058,GO:0009987,GO:0016020,GO:0016740,GO:0016772,GO:0016780,GO:0019637,GO:0030572,GO:0032048,GO:0032049,GO:0044237,GO:0044238,GO:0044249,GO:0044255,GO:0045017,GO:0046471,GO:0046474,GO:0046486,GO:0071704,GO:0090407,GO:1901576 #> 4 GO:0003674,GO:0003676,GO:0003723,GO:0003729,GO:0003824,GO:0004654,GO:0005488,GO:0005575,GO:0005622,GO:0005623,GO:0005737,GO:0006139,GO:0006401,GO:0006402,GO:0006725,GO:0006807,GO:0008150,GO:0008152,GO:0009056,GO:0009057,GO:0009892,GO:0009987,GO:0010468,GO:0010605,GO:0010629,GO:0016070,GO:0016071,GO:0016740,GO:0016772,GO:0016779,GO:0019222,GO:0019439,GO:0034641,GO:0034655,GO:0043170,GO:0044237,GO:0044238,GO:0044248,GO:0044260,GO:0044265,GO:0044270,GO:0044424,GO:0044464,GO:0046483,GO:0046700,GO:0048519,GO:0050789,GO:0060255,GO:0065007,GO:0071704,GO:0090304,GO:0097159,GO:1901360,GO:1901361,GO:1901363,GO:1901575 #> 5 #> 6 #> KEGG_ID Pathway taxonomic.scope #> 1 K07124 Integumentary System hormonal proteins #> 2 Skeletal System structural proteins #> 3 K06131 Muscular System enzymes #> 4 K00962 Nervous System contractile proteins #> 5 K02083 Endocrine System contractile proteins #> 6 Cardiovascular System structural proteins head(l[[2]]) #> # A tibble: 6 × 6 #> ID type cluster day value taxonomic.scope #> #> 1 1 type_A Group_2 T1 -0.254 hormonal proteins #> 2 1 type_A Group_2 T2 -0.676 hormonal proteins #> 3 1 type_A Group_2 T3 -0.676 hormonal proteins #> 4 1 type_A Group_2 T4 -0.254 hormonal proteins #> 5 1 type_A Group_2 T5 -0.254 hormonal proteins #> 6 1 type_A Group_2 T6 -0.676 hormonal proteins head(l[[3]]) #> ID cluster #> 1 1 Group_2 #> 2 2 Group_5 #> 3 3 Group_2 #> 4 4 Group_4 #> 5 5 Group_4 #> 6 6 Group_2 #> GO_ID #> 1 #> 2 #> 3 GO:0003674<\/a>
GO:0003824<\/a>
GO:0005575<\/a>
GO:0006629<\/a>
GO:0006644<\/a>
GO:0006650<\/a>
GO:0006655<\/a>
GO:0006793<\/a>
GO:0006796<\/a>
GO:0008150<\/a>
GO:0008152<\/a>
GO:0008610<\/a>
GO:0008654<\/a>
GO:0008808<\/a>
GO:0009058<\/a>
GO:0009987<\/a>
GO:0016020<\/a>
GO:0016740<\/a>
GO:0016772<\/a>
GO:0016780<\/a>
GO:0019637<\/a>
GO:0030572<\/a>
GO:0032048<\/a>
GO:0032049<\/a>
GO:0044237<\/a>
GO:0044238<\/a>
GO:0044249<\/a>
GO:0044255<\/a>
GO:0045017<\/a>
GO:0046471<\/a>
GO:0046474<\/a>
GO:0046486<\/a>
GO:0071704<\/a>
GO:0090407<\/a>
GO:1901576<\/a> #> 4 GO:0003674<\/a>
GO:0003676<\/a>
GO:0003723<\/a>
GO:0003729<\/a>
GO:0003824<\/a>
GO:0004654<\/a>
GO:0005488<\/a>
GO:0005575<\/a>
GO:0005622<\/a>
GO:0005623<\/a>
GO:0005737<\/a>
GO:0006139<\/a>
GO:0006401<\/a>
GO:0006402<\/a>
GO:0006725<\/a>
GO:0006807<\/a>
GO:0008150<\/a>
GO:0008152<\/a>
GO:0009056<\/a>
GO:0009057<\/a>
GO:0009892<\/a>
GO:0009987<\/a>
GO:0010468<\/a>
GO:0010605<\/a>
GO:0010629<\/a>
GO:0016070<\/a>
GO:0016071<\/a>
GO:0016740<\/a>
GO:0016772<\/a>
GO:0016779<\/a>
GO:0019222<\/a>
GO:0019439<\/a>
GO:0034641<\/a>
GO:0034655<\/a>
GO:0043170<\/a>
GO:0044237<\/a>
GO:0044238<\/a>
GO:0044248<\/a>
GO:0044260<\/a>
GO:0044265<\/a>
GO:0044270<\/a>
GO:0044424<\/a>
GO:0044464<\/a>
GO:0046483<\/a>
GO:0046700<\/a>
GO:0048519<\/a>
GO:0050789<\/a>
GO:0060255<\/a>
GO:0065007<\/a>
GO:0071704<\/a>
GO:0090304<\/a>
GO:0097159<\/a>
GO:1901360<\/a>
GO:1901361<\/a>
GO:1901363<\/a>
GO:1901575<\/a> #> 5 #> 6 #> KEGG_ID #> 1 K07124<\/a> #> 2 #> 3 K06131<\/a> #> 4 K00962<\/a> #> 5 K02083<\/a> #> 6 #> Pathway taxonomic.scope #> 1 Integumentary System hormonal proteins #> 2 Skeletal System structural proteins #> 3 Muscular System enzymes #> 4 Nervous System contractile proteins #> 5 Endocrine System contractile proteins #> 6 Cardiovascular System structural proteins"},{"path":"https://kaiyanm.github.io/MolPad/reference/scale_by_row__.html","id":null,"dir":"Reference","previous_headings":"","what":"Scale by row — scale_by_row__","title":"Scale by row — scale_by_row__","text":"Scales values sample, row independently processed.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/scale_by_row__.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Scale by row — scale_by_row__","text":"","code":"scale_by_row__(data)"},{"path":"https://kaiyanm.github.io/MolPad/reference/scale_by_row__.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Scale by row — scale_by_row__","text":"input expected data frame first column ID following columns containing observations different time points. ID column remains unaltered, columns double (dbl) format scaled.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/scale_by_row__.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Scale by row — scale_by_row__","text":"","code":"data(test_data) scale_by_row__(test_data[1:5,1:10]) #> ID T1 T2 T3 T4 T5 T6 #> 1 1 -0.05847053 -0.5847053 -0.58470535 -0.05847053 -0.05847053 -0.5847053 #> 2 2 2.26366583 -0.6467617 -0.64676167 -0.64676167 -0.64676167 0.8084521 #> 3 3 0.22866478 -0.8003267 -0.80032673 -0.80032673 1.25765629 -0.8003267 #> 4 4 0.50395263 0.9575100 0.05039526 0.05039526 1.86462473 -0.4031621 #> 5 5 0.73869087 0.2841119 NA -0.17046712 1.19326987 1.1932699 #> T7 T8 T9 #> 1 -0.5847053 -0.05847053 2.5727035 #> 2 -0.1616904 -0.64676167 0.3233808 #> 3 -0.8003267 1.25765629 1.2576563 #> 4 -0.8567195 -0.85671947 -1.3102768 #> 5 -1.0796251 -1.07962512 -1.0796251"},{"path":"https://kaiyanm.github.io/MolPad/reference/test_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Test data — test_data","title":"Test data — test_data","text":"synthetically generated dataset created basic testing purposes.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/test_data.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Test data — test_data","text":"two datasets: data annotations","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/test_data.html","id":"data","dir":"Reference","previous_headings":"","what":"data","title":"Test data — test_data","text":"data frame 100 rows 12 variables: ID row ID T1~T10 count value 10 timepoints type type ~D","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/test_data.html","id":"annotations","dir":"Reference","previous_headings":"","what":"annotations","title":"Test data — test_data","text":"data frame 100 rows 5 variables: ID row ID GO_ID go ID KEGG_ID kegg ID system primary lable: 'Integumentary System', 'Skeletal System', 'Muscular System', 'Nervous System', 'Endocrine System', 'Cardiovascular System', 'Lymphatic System', 'Respiratory System', 'Digestive System', 'Urinary System' class secondary label: 'antibodies', 'contractile proteins', 'enzymes', 'hormonal proteins', 'structural proteins', 'storage proteins', 'transport proteins'","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/test_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Test data — test_data","text":"","code":"data(test_data)"},{"path":"https://kaiyanm.github.io/MolPad/reference/transpose_dataframe__.html","id":null,"dir":"Reference","previous_headings":"","what":"Transpose dataframe — transpose_dataframe__","title":"Transpose dataframe — transpose_dataframe__","text":"function transposes provided data frame, using values first column new column names.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/transpose_dataframe__.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transpose dataframe — transpose_dataframe__","text":"","code":"transpose_dataframe__(data)"},{"path":"https://kaiyanm.github.io/MolPad/reference/transpose_dataframe__.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transpose dataframe — transpose_dataframe__","text":"expected input data frame first column serves Time, subsequent columns contain observations various features.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/transpose_dataframe__.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transpose dataframe — transpose_dataframe__","text":"","code":"a <- data.frame(\"Day\"=c(\"Day1\",\"Day2\",\"Day3\"),\"feature_1\" =c(1,2,3),\"feature_2\" =c(0,4,1),\"feature_3\" =c(1,1,0)) a #> Day feature_1 feature_2 feature_3 #> 1 Day1 1 0 1 #> 2 Day2 2 4 1 #> 3 Day3 3 1 0 transpose_dataframe__(a) #> Day1 Day2 Day3 #> 1 1 2 3 #> 2 0 4 1 #> 3 1 1 0"}]
diff --git a/man/figures/.DS_Store b/man/figures/.DS_Store
index bbe3881..862bd8d 100644
Binary files a/man/figures/.DS_Store and b/man/figures/.DS_Store differ
diff --git a/man/figures/cheesecase.png b/man/figures/cheesecase.png
deleted file mode 100644
index d91f265..0000000
Binary files a/man/figures/cheesecase.png and /dev/null differ
diff --git a/man/figures/cheesecase1.png b/man/figures/cheesecase1.png
new file mode 100644
index 0000000..6d4d8aa
Binary files /dev/null and b/man/figures/cheesecase1.png differ
diff --git a/vignettes/cheese.Rmd b/vignettes/cheese.Rmd
index a26acd4..18967b8 100644
--- a/vignettes/cheese.Rmd
+++ b/vignettes/cheese.Rmd
@@ -98,3 +98,18 @@ gDashboard(cheesedata,
id_colname = c("GO_ID","KEGG_ID"),
id_type = c("GO","KEGG"))
```
+
+# Results
+
+For example, when we filter for Actinomycetota (Actinobacteria) as the functional group, we see that there are no edges connecting to group 10 and group 3- the clusters that have the most features associated with Actinomycetoa for Cheese sample A (Fig 1.3.A).
+
+
+
+ { width=100% }
+ *Fig 1: Dashboard showing Actinomycetota filtered network (A) with enrichment pattern for Cheese Sample-A (B) and Cheese Sample-C (C); Cluster pattern for Group 9, which also is enriched for Type IV secretion genes (D).*
+
+
+
+Looking at the pattern traces of these groups, (Fig 1.3.B), there is a peak in samples A4 (week 9) and A5 (week 13), which mirrors the 16S rRNA results of Saak et al. Since these two clusters do not have edges connecting them to other groups, this suggests that the Actinomycetoa features found in these groups follow distinct longitudinal succession patterns that are independent. When looking at Actinomycetoa within Cheese Sample C we see a different pattern. Groups 2 and 5, have the most features associated with Actinomycetoa, but they are highly connected to the other groups (Fig 1.3.A). From these results, we can hypothesize that though Actinomycetoa features are more abundant in later time points for both cheese samples, their dynamics are differentially influenced.
+
+The authors found that Type VI secretion was enriched in Pseudomonadota bacteria (specifically, Psychrobacter), and hypothesized this enrichment was due to the importance of physical species interactions that occur with this habitat. Using MolPad, we searched for other secretion systems associated genes, to understand their dynamics within the community. Focusing on KEGG annotated Type IV secretion genes, we found that Group 9 contained 12/13 of these genes. Within this group, features that cluster are ones that peak in Cheese sample C5 (week 13, Fig 1.3.D). This is also the most taxonomically diverse sample. From this, we can hypothesize that increased taxonomic diversity is also associated with increases in genes that are related to competitive species interactions.
\ No newline at end of file
diff --git a/vignettes/getstarted.Rmd b/vignettes/getstarted.Rmd
index b9ec236..5724477 100644
--- a/vignettes/getstarted.Rmd
+++ b/vignettes/getstarted.Rmd
@@ -45,6 +45,8 @@ library(MolPad)
load("/Users/hazelma/Documents/GitHub/MolPad_Data/multiomics_simulate.RData")
```
+
+
# Before Input
We recommend carefully reviewing your data and considering applying normalization (standardization, quantile normalization, log transformation, ...) and imputation (KNN, PMM, Mean/Median/Mode, ...) to address issues related to library size and missing data beforehead, otherwise the `pre_processing()` function will default to converting NAs to 0s.
@@ -57,6 +59,7 @@ We recommend carefully reviewing your data and considering applying normalizatio
* If it's hard to decide, you can always experiment with different methods and use cross-validation to compare their effectiveness. Let your choice be guided by domain knowledge and the specific requirements of your analysis to increase the quality of the following steps.
+
# Data Format
@@ -72,14 +75,17 @@ If your datasets are not multi-omics, you can simply apply it to our dashboard b
### Data Format and Pre-process
+
{ width=70% }
-
+
In the figure, A, B, and C can represent datasets from different sources or different aspects of measurements. Below are two examples of what the input data might look like and how to transform it into the required format through `pre_process()` modules.
+
+
#### If your input is a list of datasets:
Here is a list of datasets, each following the same format: they all have an ID column and identical column names.
@@ -105,6 +111,8 @@ data <- pre_process(fungal_dlist, typenameList = c('DataSource_A','DataSource_B'
head(data,10)
```
+
+
#### If your input is a single dataset:
Here is a dataset that already includes a labeled 'type' column:
@@ -121,6 +129,7 @@ x1 <- pre_process(x, replaceNA = TRUE,scale = TRUE)
head(x1,10)
```
+
# Choose Your Annotation
@@ -137,15 +146,18 @@ If your data has more than two layers of labels (like the cheese dataset), you c
Annotation_path_taxon <- gAnnotation(annotations,"phylum","class")
```
+
+
# Network Generation
To generate the network, we first scale and cluster the trajectories across the input molecular features. Trajectories are clustered using K-means, and a built-in elbow method is used to select the optimal K. We build network between clustered trajectories by adapting the GENIE3 algorithm. Specifically, the network is learned through a series of regressions, where each cluster centroid is predicted from the expression patterns of all the other cluster centroids using random forests. We choose random forests because of their potential to model interacting features and non-linearity without strong assumptions.
+
{ width=50% }
-
+
To determine which feature clusters are predictive of a given trajectory, the Mean Decrease Accuracy of a subset of top predictors whose expression directly influences the expression of the target cluster is taken as an indication of a putative link.
@@ -159,6 +171,8 @@ network_result <- cluster_result |>
gNetwork_view(network_result)
```
+
+
# Dashboard Navigation
@@ -173,46 +187,68 @@ gDashboard(data,
id_type = "KEGG")
```
+
{ width=70% }
+
Once you've launched the Shiny dashboard, you can zoom in or make adjustments to explore interesting findings within your data. To effectively navigate the dashboard generated by MolPad, you'll follow three main steps:
-### Choose a Primary Functional Annotation and Adjust Edge Density
+### 1. Choose a Primary Functional Annotation and Adjust Edge Density
Start by selecting a primary functional annotation from the available options. Then, fine-tune the edge density by adjusting the threshold value on the importance score. Nodes that turn bright green indicate clusters containing the most features related to the chosen functional annotation.
-### Explore the Network
+### 2. Explore the Network
Brushing on the network unveils patterns of taxonomic composition and typical trajectories. You can also zoom into specific taxonomic annotations by applying filters.
-### Investigate Feature Details and Related Function Annotations
+### 3. Investigate Feature Details and Related Function Annotations
Delve into the feature table to examine the specifics of the features within the selected clusters. Explore additional related function annotations using the drop-down options. Click on the provided links to access online information about the items of interest. The interface encourages iterative exploration, enabling you to conduct multiple steps to answer specific questions, such as comparing pattern distributions between two functions or identifying functionally important community members metabolizing a feature of interest.
+
+
+
{ width=100% }
+
# Related Visualization Outputs
The following visualizations are integrated into the dashboard, so there's no need to run function for each plot individually. We've linked a stacked bar plot and a ribbon plot with the network to facilitate focus and context explorations. This setup helps in detecting similar longitudinal patterns and understanding their relationships.
+
+
+
### Network Plot
+
+The network plot is a powerful visualization tool that displays the relationships between different groups or features within your data. In MolPad, the network plot helps to identify clusters of features that share similar patterns, revealing underlying connections that might not be immediately obvious. By visualizing these connections, users can gain a clearer understanding of the structure within their data, making it easier to pinpoint significant associations and trends.
+
```{r,echo=FALSE}
data("test_data")
make_the_graph(test_graphptw, test_network, 0.03, "Muscular System","nicely")
```
+
+
### Stack Bar Plot
+
+The stacked bar plot provides a detailed view of the composition of each cluster or group in your dataset. By stacking different categories on top of each other within a single bar, this plot allows for a quick comparison of relative proportions across multiple groups. This is particularly useful in microbiome experiments where understanding the distribution of taxa across different conditions or time points is crucial. The stacked bar plot makes it easy to see how these distributions change between experimental conditions, facilitating deeper insights.
+
```{r,echo=FALSE}
make_stackbar_plot(test_maindata, c("Group_1","Group_2","Group_3","Group_4"), c("hormonal proteins","structural proteins","enzymes","storage proteins","antibodies","transport proteins"))
```
+
+
+
### Ribbon Plot
+The ribbon plot is designed to visualize changes over time, making it an ideal tool for tracking longitudinal data. In MolPad, the ribbon plot illustrates how the abundance or expression levels of features vary across different time points or conditions. The smooth, flowing design of the ribbons helps to emphasize trends and patterns, enabling users to quickly identify periods of significant change or stability. This plot is particularly advantageous when comparing multiple groups, as it clearly shows overlapping trends and divergences, providing a comprehensive view of temporal dynamics in the data.
+
```{r,echo=FALSE}
make_line_plot(test_maindata, c("Group_1","Group_2","Group_3","Group_4"), c("hormonal proteins","structural proteins","enzymes","storage proteins","antibodies","transport proteins"))
diff --git a/vignettes/whymolpad.Rmd b/vignettes/whymolpad.Rmd
index c308dba..38cb484 100644
--- a/vignettes/whymolpad.Rmd
+++ b/vignettes/whymolpad.Rmd
@@ -24,11 +24,17 @@ library(dplyr)
# Why MolPad?
With the increasing multi-omics data and longitudinal designs integrated into microbiome experiments, there is a growing need to present the network, especially with complex variations across biological modalities. Network perspective helps detect the underlying co-occurrence among microbiome samples, allowing for high-level insights into the global structure. Yet when it comes to experimental data that records time series for 100,000 features, the network will collapse into some entangled clumps and therefore unable to read.
+
+
{ width=50% }
+
+
+# Improvements
+
For the aim of network interpretation, MolPad shows improvements in 3 important aspects:
* Capture groups of features that share longitudinal patterns and functional properties within the network.
@@ -39,6 +45,7 @@ This package especially facilitates the exploration of longitudinal molecular co
Unlike existing tools, MolPad stands out for its rich network interactivity and support for contextualization. It is also equipped with a beginner-friendly interface and self-contained data processing functions, making it accessible to users with limited programming experience.
+
# Key Features
@@ -54,25 +61,31 @@ We build network between clustered trajectories by adapting the GENIE3 algorithm
This package provides an interactive dashboard designed to help users explore data and uncover relationships within complex networks. By utilizing focus-plus-context visualization, the dashboard allows users to zoom in on specific details while keeping the broader dataset in view. This approach enables a deeper understanding of the data, making it easier to identify patterns and make informed decisions.
+
+
# What does it look like?
+Now, let’s see what you can get from the dashboard. We’ll start with an overview and then demonstrate how to discover patterns within your data.
+
+
### MolPad Dashboard Overview
+
{ width=100% }
-
+
Above is the overview of the MolPad Dashboard. To explore the dashboard effectively, you can start by following the sequence A-B-C-D. This approach will guide you through the cluster-level network, taxonomic-level bar plot, type-level line plot, and feature-level table. From there, you can modify detailed settings to identify and investigate special patterns within your data.
-
### Discovering related patterns
{ width=100% }
+
Here is a short example of discovering related patterns using the network plot: The shade of the edges represents the proximity of nodes. In the brushed area, Groups 1-7-8 (circled by solid black lines) and Groups 1-2 (circled by blue dashed lines) are strongly linked. For Groups 1, 7, and 8, the patterns form a W-shape with a noticeable peak at the same time section. Although Group 1 shows higher volatility compared to Group 2, both groups display a highly overlapping increasing trend.
Examplesnetworkres <- gNetwork(test_cluster, ntop = 3)
head(networkres)
#> weight IncNodePurity var_names from
-#> 1 1.6429045 0.7371260 Group_3 Group_1
-#> 2 0.4588961 0.9310697 Group_5 Group_1
-#> 3 -0.7833169 0.6761286 Group_2 Group_1
-#> 4 5.0076415 0.8821582 Group_5 Group_2
-#> 5 4.1618215 1.1976058 Group_4 Group_2
-#> 6 2.5714583 1.2820391 Group_1 Group_2
+#> 1 1.4215243 1.1776012 Group_5 Group_1
+#> 2 0.7911266 0.7011049 Group_3 Group_1
+#> 3 -1.9891284 0.5811186 Group_2 Group_1
+#> 4 5.8098014 1.1107592 Group_4 Group_2
+#> 5 5.4000389 0.8744358 Group_5 Group_2
+#> 6 1.2492288 1.1816976 Group_1 Group_2
gNetwork_view(networkres)
diff --git a/docs/reference/make_the_graph-1.png b/docs/reference/make_the_graph-1.png
index 061c537..76d9b9c 100644
Binary files a/docs/reference/make_the_graph-1.png and b/docs/reference/make_the_graph-1.png differ
diff --git a/docs/search.json b/docs/search.json
index 782a610..33c4d18 100644
--- a/docs/search.json
+++ b/docs/search.json
@@ -1 +1 @@
-[{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"CC0 1.0 Universal","title":"CC0 1.0 Universal","text":"CREATIVE COMMONS CORPORATION LAW FIRM PROVIDE LEGAL SERVICES. DISTRIBUTION DOCUMENT CREATE ATTORNEY-CLIENT RELATIONSHIP. CREATIVE COMMONS PROVIDES INFORMATION “-” BASIS. CREATIVE COMMONS MAKES WARRANTIES REGARDING USE DOCUMENT INFORMATION WORKS PROVIDED HEREUNDER, DISCLAIMS LIABILITY DAMAGES RESULTING USE DOCUMENT INFORMATION WORKS PROVIDED HEREUNDER.","code":""},{"path":"https://kaiyanm.github.io/MolPad/LICENSE.html","id":"statement-of-purpose","dir":"","previous_headings":"","what":"Statement of Purpose","title":"CC0 1.0 Universal","text":"laws jurisdictions throughout world automatically confer exclusive Copyright Related Rights (defined ) upon creator subsequent owner(s) (, “owner”) original work authorship /database (, “Work”). 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Affirmer understands acknowledges Creative Commons party document duty obligation respect CC0 use Work.","code":""},{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/articles/FAQ.html","id":"how-can-i-tell-if-my-dataset-is-suitable-for-using-this-dashboard","dir":"Articles","previous_headings":"Common Questions:","what":"How can I tell if my dataset is suitable for using this dashboard?","title":"FAQs","text":"lot longitudinal data least two descriptive columns (like types), match minimal needs starting dashboard. addition, data specific web-ID columns, MolPad make convenient createing links databases automatically.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/FAQ.html","id":"how-can-i-customize-my-dashboard","dir":"Articles","previous_headings":"Common Questions:","what":"How can I customize my dashboard?","title":"FAQs","text":"can edit title within gDashboard() function. additional personalization, adjusting colors sizes figures, can fork repository, create copy, make modifications dashboard.css file.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/FAQ.html","id":"what-should-i-do-if-i-only-have-a-few-time-points5","dir":"Articles","previous_headings":"Common Questions:","what":"What should I do if I only have a few time points(<5)?","title":"FAQs","text":"Firstly, strongly recommend extending length longitudinal samples. can achieve increasing number measurements two days incorporating simulated data. options don’t suit needs, can also consider using “linear” method gNetwork() function.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Case Study: Cheese Communities","text":"vignette provides comprehensive guide using MolPad case study, including data pre-processing, network generation, result analysis.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"washed-rind-cheese-microbial-communities","dir":"Articles","previous_headings":"","what":"Washed-Rind Cheese Microbial Communities","title":"Case Study: Cheese Communities","text":"Cheese making ancient craft involves coagulation milk proteins form curds, separated liquid whey. curds processed, shaped, aged develop desired texture flavor. Various factors, type milk, specific cultures bacteria, aging conditions, contribute unique characteristics cheese. One crucial step process aging practice, regular washing brine solution plays significant role. process producing cheese, regular washing brine solution aging practice can homogenize microbial communities cheese’ surface facilitate intermicrobial interactions. following parts, analyze longitudinal data set three washed-rind cheese communities collected cheese ripening.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"data","dir":"Articles","previous_headings":"","what":"Data","title":"Case Study: Cheese Communities","text":"analysis, use data set contained study Smith et al. (2022) microbial communities cheese. original research investigated successional dynamics occur within cheese rind microbial communities using combination 16S rRNA amplicon, Illumina, PacBio sequencing. functionally taxonomically annotate (using eggNOG (21) MMseqs2 (22)) contigs generated Illumina reads, demonstrate utility MolPad using single-omic. Specifically, focus Cheese Sample Cheese Sample C. detailed information attached data, please check documentation.","code":"data(\"cheese\") str(cheese) #> tibble [106,239 × 18] (S3: tbl_df/tbl/data.frame) #> $ ID : chr [1:106239] \"1\" \"2\" \"3\" \"4\" ... #> $ A_1 : int [1:106239] 38 23 24 3 58 12 1 14 1 3 ... #> $ A_2 : int [1:106239] 23 6 5 2 14 9 1 7 1 1 ... #> $ A_3 : int [1:106239] 27 4 37 4 45 14 0 14 3 5 ... #> $ A_4 : int [1:106239] 5 0 10 2 13 4 1 4 4 0 ... #> $ A_5 : int [1:106239] 11 9 19 16 32 13 0 4 1 1 ... #> $ C_1 : int [1:106239] 13 21 3 56 82 2 4 17 7 2 ... #> $ C_3 : int [1:106239] 1 1 0 1 3 0 0 0 0 1 ... #> $ C_4 : int [1:106239] 3 0 1 7 8 1 0 1 2 2 ... #> $ C_5 : int [1:106239] 0 2 4 17 3 4 0 1 8 6 ... #> $ GO_ID : chr [1:106239] NA NA NA NA ... #> $ KEGG_ID: chr [1:106239] NA NA NA NA ... #> $ domain : chr [1:106239] NA NA NA NA ... #> $ phylum : chr [1:106239] NA NA NA NA ... #> $ class : chr [1:106239] NA NA NA NA ... #> $ order : chr [1:106239] NA NA NA NA ... #> $ family : chr [1:106239] NA NA NA NA ... #> $ genus : chr [1:106239] NA NA NA NA ... str(annotations) #> tibble [86,156 × 9] (S3: tbl_df/tbl/data.frame) #> $ ID : chr [1:86156] \"9\" \"10\" \"11\" \"12\" ... #> $ GO_ID : chr [1:86156] \"-\" \"-\" \"-\" \"-\" ... #> $ KEGG_ID: chr [1:86156] \"-\" \"-\" \"-\" \"-\" ... #> $ domain : chr [1:86156] \"Bacteria\" \"Bacteria\" \"Bacteria\" \"Bacteria\" ... #> $ phylum : chr [1:86156] \"Pseudomonadota\" \"Pseudomonadota\" \"Pseudomonadota\" \"Pseudomonadota\" ... #> $ class : chr [1:86156] \"Alphaproteobacteria\" \"Alphaproteobacteria\" \"Alphaproteobacteria\" \"Alphaproteobacteria\" ... #> $ order : chr [1:86156] \"Caulobacterales\" \"Caulobacterales\" \"Hyphomicrobiales\" \"Hyphomicrobiales\" ... #> $ family : chr [1:86156] \"Caulobacteraceae\" \"Caulobacteraceae\" \"Bartonellaceae\" \"Brucellaceae;-_Brucella/Ochrobactrum group\" ... #> $ genus : chr [1:86156] \"Caulobacter\" \"Caulobacter;-_unclassified Caulobacter\" \"Bartonella\" \"Brucella\" ..."},{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"data-and-annotations","dir":"Articles","previous_headings":"Data > Pre-process","what":"Data and Annotations","title":"Case Study: Cheese Communities","text":"select ‘type’ column phylum describe characteristic cheese data. Also, columns phylum class taken tags elemental composition. section, introduce data preparing steps analysis. annotations dataset contains various columns describe characteristics properties samples. First, select ‘type’ column phylum provide broad categorization microbial communities present cheese surface. categorization helps understanding overall composition diversity microbes high taxonomic level. run pre_process() function clean standardize data. annotate dataset, also use columns phylum class tags elemental composition microbial communities. phylum column represents major taxonomic rank, giving us broad overview microbial distribution. class column provides detailed information, allowing us delve deeper specific types microbes present. pre-processing, two datasets put dashboard look like:","code":"cheesedata <- cheese |> select(ID, A_1:C_5, phylum) |> rename(type=phylum) |> pre_process() pathchee <- gAnnotation(annotations,\"phylum\",\"class\") # data cheesedata[112:115,] #> # A tibble: 4 × 11 #> ID A_1 A_2 A_3 A_4 A_5 C_1 C_3 C_4 C_5 type #> #> 1 112 0.943 -0.471 -0.471 -0.471 -0.471 2.36 -0.471 -0.471 -0.471 Other #> 2 113 0.786 -0.124 0.126 -0.623 -0.637 2.33 -0.667 -0.593 -0.593 Other #> 3 114 1.22 -1.09 -0.430 -0.829 -0.170 1.43 -1.30 0.455 0.715 Other #> 4 115 2.67 -0.333 -0.333 -0.333 -0.333 -0.333 -0.333 -0.333 -0.333 Other # annotation pathchee[112:115,] #> # A tibble: 4 × 9 #> ID GO_ID KEGG_ID domain Pathway taxonomic.scope order family genus #> #> 1 145 - ko:K00004 Bacte… Pseudo… Unknown NA NA NA #> 2 147 - ko:K00004 Bacte… Actino… Actinomycetes NA NA NA #> 3 148 - ko:K00004 Bacte… Bacill… Bacilli Baci… Bacil… Lysi… #> 4 149 - ko:K00004,ko:K0… Bacte… Actino… Actinomycetes NA NA NA"},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"cluster-input","dir":"Articles","previous_headings":"Data","what":"Cluster Input","title":"Case Study: Cheese Communities","text":"section, generate clusters first dataset using gClusters function. function takes cheese dataset (cheesedata) input generates clusters based specified parameters. , set number clusters 10 (ncluster = 10) specify maximum number clusters consider determining optimal number clusters (elbow.max=15).","code":"cluschee <- gClusters(cheesedata,ncluster = 10,elbow.max=15)"},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"network-input","dir":"Articles","previous_headings":"Data","what":"network input","title":"Case Study: Cheese Communities","text":"generating clusters major patterns, proceed obtain network results clusters. Taking cluster centroids nodes, prediction process edges divided individual regression tasks, cluster centroid independentally predicted expression cluster centroids, using random forests. pick top 3 related predictors cluster centroid save network output future use. achieved using gNetwork() function. gain insight network results, can visualize details using gNetwork_view() function, shown .","code":"networkchee <- gNetwork(cluschee,ntop = 3) gNetwork_view(networkchee)"},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"run-dashboard","dir":"Articles","previous_headings":"","what":"Run Dashboard","title":"Case Study: Cheese Communities","text":"clusters network results obtained, can proceed run dashboard. involves declaring annotations executing dashboard using gDashboard() function. , pass cheese dataset (cheesedata), cluster results (cluschee), network results (networkchee), specify column names types annotation identifiers.","code":"gDashboard(cheesedata, cluschee, pathchee, networkchee, id_colname = c(\"GO_ID\",\"KEGG_ID\"), id_type = c(\"GO\",\"KEGG\"))"},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"installation","dir":"Articles","previous_headings":"","what":"Installation","title":"Get started","text":"can either install MolPad devtools, clone repository local computer (example, onto ./Github) installing: , load package:","code":"# Install the package in R: install.packages(\"devtools\") library(devtools) install_github(\"KaiyanM/MolPad\") # Download through Github and install the package in R: setwd(\"./GitHub\") install(\"MolPad\") library(MolPad)"},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"before-input","dir":"Articles","previous_headings":"","what":"Before Input","title":"Get started","text":"recommend carefully reviewing data considering applying normalization (standardization, quantile normalization, log transformation, …) imputation (KNN, PMM, Mean/Median/Mode, …) address issues related library size missing data beforehead, otherwise pre_processing() function default converting NAs 0s.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"tips-for-normalization-and-imputation","dir":"Articles","previous_headings":"Before Input","what":"Tips for normalization and imputation","title":"Get started","text":"choose normalization method, first consider data distribution. Use Z-score normalization data normally distributed, log transformation robust scaling ’s skewed. analysis requires data within specific range, like [0, 1], opt Min-Max normalization. datasets outliers, robust scaling rank normalization better choices. selecting imputation method, assess amount pattern missing data. small amount missing data, simple methods like mean, median, mode imputation effective. missing data related variables, consider sophisticated approaches like Predictive Mean Matching k-NN imputation. computational resources concern, simpler imputation methods might practical, resources allow, advanced methods can improve data quality. ’s hard decide, can always experiment different methods use cross-validation compare effectiveness. Let choice guided domain knowledge specific requirements analysis increase quality following steps.","code":""},{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"multi-omics-yes-or-no","dir":"Articles","previous_headings":"Data Format","what":"Multi-omics: Yes or No","title":"Get started","text":"visualization pipeline starts pre-processing module includes built-functions reshape data inputs standard format streamline data preparation. Depending nature datasets, two primary conditions consider: research object includes multi-omics measurements, assume provide list dataframes, collected different omics type. example, might datasets peptides, metabolites, lipids. combine together standard long dataset row represents feature, simply run pre_process() function, conduct data preparation yield standard input format demonstrated . Datasets multi-omics, can still utilize dashboard long ensuring data inputs reformatted standard longitudinal format. situation, can manually assign type column category label describe major groups data, input data pre_processing() module data preparation . case study, utilized “Kingdom” type label column cheese data. datasets multi-omics, can simply apply dashboard reformatting data standard longitudinal format. case, manually assign category label type column represent major groups. can still input data pre_processing() module cleaning preparation. case study, used “Kingdom” type label cheese datasets.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"data-format-and-pre-process","dir":"Articles","previous_headings":"Data Format","what":"Data Format and Pre-process","title":"Get started","text":"figure, , B, C can represent datasets different sources different aspects measurements. two examples input data might look like transform required format pre_process() modules.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"if-your-input-is-a-list-of-datasets","dir":"Articles","previous_headings":"Data Format > Data Format and Pre-process","what":"If your input is a list of datasets:","title":"Get started","text":"list datasets, following format: ID column identical column names. pre_process():","code":"str(fungal_dlist) #> List of 3 #> $ :'data.frame': 149786 obs. of 12 variables: #> ..$ ID : chr [1:149786] \"grp1_1\" \"grp1_2\" \"grp1_6\" \"grp1_7\" ... #> ..$ D0_01 : num [1:149786] -0.33 -0.33 1.591 1.106 0.659 ... #> ..$ D1_02 : num [1:149786] 0.628 0.628 3.087 3.618 2.843 ... #> ..$ D2_03 : num [1:149786] -1.223 -1.223 0.846 1.178 1.435 ... #> ..$ D3_04 : num [1:149786] -2.411 -1.808 1.239 1.753 0.258 ... #> ..$ D4_05 : num [1:149786] -1.41 -1.18 2.05 1.64 1.21 ... #> ..$ D5_06 : num [1:149786] -2.245 -2.309 0.83 1.64 0.395 ... #> ..$ D6_07 : num [1:149786] -0.784 -1.125 2.758 2.888 2.516 ... #> ..$ D7_08 : num [1:149786] -2.61197 -2.68724 0.42236 0.92846 0.00892 ... #> ..$ D8_09 : num [1:149786] -0.951 -1.3 2.417 2.454 1.418 ... #> ..$ D9_10 : num [1:149786] -1.91 -2.61 1.33 1.8 2.25 ... #> ..$ D10_11: num [1:149786] -2.871 -3.417 1.101 0.619 1.275 ... #> $ :'data.frame': 150 obs. of 12 variables: #> ..$ ID : chr [1:150] \"grp2_1\" \"grp2_2\" \"grp2_3\" \"grp2_4\" ... #> ..$ D0_01 : num [1:150] 0.947 1.012 0.156 0.31 0.138 ... #> ..$ D1_02 : num [1:150] -1.616 -0.917 -1.581 -2.717 -2.578 ... #> ..$ D2_03 : num [1:150] -2.4 -1.67 -2.31 -3.02 -3.66 ... #> ..$ D3_04 : num [1:150] -0.5734 0.0336 -0.351 -1.3052 -1.737 ... #> ..$ D4_05 : num [1:150] 0.662 0.854 1.002 0.12 -0.371 ... #> ..$ D5_06 : num [1:150] -1.09 -1.28 -1.04 -1.82 -3.15 ... #> ..$ D6_07 : num [1:150] -0.212 0.718 0.189 -0.627 -1.081 ... #> ..$ D7_08 : num [1:150] 0.969 2.117 1.462 0.339 -0.038 ... #> ..$ D8_09 : num [1:150] -0.712 0.43 -0.281 -1.297 -1.822 ... #> ..$ D9_10 : num [1:150] -0.649 0.45 -0.124 -1.77 -1.962 ... #> ..$ D10_11: num [1:150] -1.05 -0.85 -1.58 -1.91 -4.05 ... #> $ :'data.frame': 369 obs. of 12 variables: #> ..$ ID : chr [1:369] \"grp3_1\" \"grp3_2\" \"grp3_3\" \"grp3_4\" ... #> ..$ D0_01 : num [1:369] -0.918 -2.112 -1.372 -0.4 -1.75 ... #> ..$ D1_02 : num [1:369] -1.219 -3.502 -1.504 -0.615 -2.11 ... #> ..$ D2_03 : num [1:369] -0.542 -2.324 -0.592 0.161 -1.342 ... #> ..$ D3_04 : num [1:369] -0.101 -1.495 0.1 0.603 -1.058 ... #> ..$ D4_05 : num [1:369] 1.715 1.557 1.89 2.296 0.653 ... #> ..$ D5_06 : num [1:369] -1.452 -2.37 -1.182 -0.846 -2.531 ... #> ..$ D6_07 : num [1:369] 0.0879 -0.9982 0.4382 0.6215 -0.9464 ... #> ..$ D7_08 : num [1:369] 0.0502 0.4668 0.5378 0.8353 -0.9213 ... #> ..$ D8_09 : num [1:369] -0.0268 -0.4226 0.4528 0.6328 -1.0998 ... #> ..$ D9_10 : num [1:369] -0.5807 -1.7755 -0.1956 0.0866 -1.5876 ... #> ..$ D10_11: num [1:369] 0.118 -0.16 0.687 0.572 -1.037 ... #> ID D0_01 D1_02 D2_03 D3_04 D4_05 D5_06 #> 1 grp1_1 -0.3297741 0.6284682 -1.2232968 -2.411339 -1.413062 -2.2445487 #> 2 grp1_2 -0.3297741 0.6284682 -1.2232968 -1.807533 -1.183498 -2.3088022 #> 3 grp1_6 1.5908054 3.0871284 0.8460835 1.239411 2.054109 0.8300876 #> 4 grp1_7 1.1061795 3.6176250 1.1781012 1.753078 1.643661 1.6400102 #> D6_07 D7_08 D8_09 D9_10 D10_11 #> 1 -0.7839412 -2.6119663 -0.9511467 -1.914273 -2.8711810 #> 2 -1.1251434 -2.6872417 -1.2996614 -2.610647 -3.4165861 #> 3 2.7576288 0.4223618 2.4165602 1.333387 1.1012825 #> 4 2.8877343 0.9284640 2.4538561 1.799019 0.6187648 #> ID D0_01 D1_02 D2_03 D3_04 D4_05 D5_06 #> 1 grp2_1 0.9467431 -1.6162900 -2.400394 -0.57337845 0.6616711 -1.090914 #> 2 grp2_2 1.0120048 -0.9165994 -1.674330 0.03361378 0.8540950 -1.283964 #> 3 grp2_3 0.1561394 -1.5805490 -2.307281 -0.35095093 1.0018741 -1.035030 #> 4 grp2_4 0.3104578 -2.7173471 -3.022886 -1.30521496 0.1198075 -1.816677 #> D6_07 D7_08 D8_09 D9_10 D10_11 #> 1 -0.2116778 0.9688582 -0.7123773 -0.6489204 -1.0504845 #> 2 0.7181970 2.1174460 0.4302981 0.4504066 -0.8503481 #> 3 0.1885422 1.4619677 -0.2805926 -0.1235090 -1.5757556 #> 4 -0.6271954 0.3393109 -1.2967938 -1.7697248 -1.9073101 #> ID D0_01 D1_02 D2_03 D3_04 D4_05 D5_06 #> 1 grp3_1 -0.9178218 -1.2193128 -0.5415780 -0.1007493 1.715495 -1.4515238 #> 2 grp3_2 -2.1120636 -3.5015326 -2.3236475 -1.4949366 1.557463 -2.3698578 #> 3 grp3_3 -1.3724446 -1.5043101 -0.5922380 0.1004978 1.889573 -1.1815469 #> 4 grp3_4 -0.4002445 -0.6145252 0.1613003 0.6031744 2.295751 -0.8464012 #> D6_07 D7_08 D8_09 D9_10 D10_11 #> 1 0.08792489 0.05020749 -0.02681783 -0.58065562 0.1177374 #> 2 -0.99822332 0.46681389 -0.42255125 -1.77554842 -0.1603138 #> 3 0.43822354 0.53780990 0.45276561 -0.19558682 0.6870272 #> 4 0.62145080 0.83528878 0.63275758 0.08663959 0.5720591 data <- pre_process(fungal_dlist, typenameList = c('DataSource_A','DataSource_B','DataSource_C'), replaceNA = TRUE, scale = TRUE, autoColName = \"Section_\") #> [1] \"Reformat a list of datasets:\" head(data,10) #> ID D0_01 D1_02 D2_03 D3_04 D4_05 D5_06 #> 1 grp1_1 1.06146675 1.956656 0.2267383 -0.88312967 0.04945980 -0.7273146 #> 2 grp1_2 1.08465667 1.916983 0.3085454 -0.19892034 0.34311497 -0.6343214 #> 3 grp1_6 -0.01899274 1.717871 -0.8834318 -0.42687491 0.51878884 -0.9019992 #> 4 grp1_7 -0.75869657 2.051470 -0.6782202 -0.03485388 -0.15728499 -0.1613700 #> 5 grp1_8 -0.68132827 1.648062 0.1465161 -1.10875822 -0.09101615 -0.9629427 #> 6 grp1_11 0.27709905 1.981042 -0.5786717 0.02888008 0.54647685 -0.7013406 #> 7 grp1_12 0.90487968 2.180079 -0.6439891 -0.14841544 -0.69040101 -0.3834938 #> 8 grp1_13 0.46335031 1.695812 -1.0938459 -0.76868293 0.74415950 -0.6861036 #> 9 grp1_14 0.72014702 1.708604 -0.9344900 -0.35796963 0.63730277 -0.9872778 #> 10 grp1_15 0.54526798 1.784716 -0.8846753 -0.49538649 0.62916563 -0.9742293 #> D6_07 D7_08 D8_09 D9_10 D10_11 type #> 1 0.6371841 -1.0705560 0.4809808 -0.41877137 -1.31271417 DataSource_A #> 2 0.3938012 -0.9630327 0.2422154 -0.89650312 -1.59653914 DataSource_A #> 3 1.3354026 -1.3752687 0.9395058 -0.31779210 -0.58720856 DataSource_A #> 4 1.2347631 -0.9575502 0.7492778 0.01655139 -1.30408627 DataSource_A #> 5 1.2995756 -1.3741657 0.1285139 1.01932257 -0.02377948 DataSource_A #> 6 1.1406352 -1.6339536 0.2041455 -0.45793129 -0.80638109 DataSource_A #> 7 0.4794959 -1.6340692 0.5360766 -0.31430867 -0.28585374 DataSource_A #> 8 1.2235141 -1.2241931 0.6790383 -0.28487806 -0.74817082 DataSource_A #> 9 1.1466328 -1.4265286 0.5357111 -0.51799361 -0.52413804 DataSource_A #> 10 1.1813273 -1.2944121 0.6417621 -0.46906150 -0.66447461 DataSource_A"},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"if-your-input-is-a-single-dataset","dir":"Articles","previous_headings":"Data Format > Data Format and Pre-process","what":"If your input is a single dataset:","title":"Get started","text":"dataset already includes labeled ‘type’ column: pre_process():","code":"#> ID Day_1 Day_2 Day_3 Day_4 Day_5 Day_6 #> 1 1 NA NA 0.104083550 -1.07999900 1.3416214 -1.733564471 #> 2 2 NA NA 0.882578509 0.08440181 0.7165712 0.004811372 #> 3 3 0.40203917 NA 0.675076590 0.97017740 -0.8210872 0.659073443 #> 4 4 1.02859068 0.30178418 0.412617771 0.39306149 -0.3427640 0.837009714 #> 5 5 -2.14036218 -0.06315709 0.656982200 -0.18301850 -1.3967562 -0.123327229 #> 6 6 -2.04354192 1.30960484 1.371349945 -1.67403067 -1.2946150 0.760315504 #> 7 7 0.31164367 2.26615374 0.972843085 -0.03956293 1.0190965 1.180680430 #> 8 8 -0.79692858 -0.86038791 -3.417872877 0.62118867 -2.1983197 -0.104229763 #> 9 9 -0.12033042 1.17585682 -0.001075426 0.01122359 0.3430992 -0.189234532 #> 10 10 0.02592915 0.48551867 0.841254390 -0.46430744 0.6891800 0.751766756 #> Day_7 Day_8 type #> 1 0.4638261 2.1318716 peptide #> 2 -0.2949926 -0.9310538 peptide #> 3 -0.2520195 0.7809977 peptide #> 4 -1.0120371 -0.1162361 peptide #> 5 -2.0085970 -0.8959521 peptide #> 6 0.7874222 -0.5106016 peptide #> 7 -0.7719437 -1.4686258 peptide #> 8 0.3541200 0.8224521 lipid #> 9 0.5674059 -0.9313882 lipid #> 10 -0.4097464 NA metabolite x1 <- pre_process(x, replaceNA = TRUE,scale = TRUE) head(x1,10) #> ID Day_1 Day_2 Day_3 Day_4 Day_5 Day_6 #> 1 1 -0.12499488 -0.1249949 -0.04022866 -1.00455207 0.9676290 -1.53681943 #> 2 2 -0.10246242 -0.1024624 1.46237249 0.04718422 1.1680372 -0.09393173 #> 3 3 0.16412393 -0.4940273 0.61109508 1.09418472 -1.8381738 0.58489741 #> 4 4 1.27432720 0.1728189 0.34079228 0.31115385 -0.8040231 0.98397755 #> 5 5 -1.35954882 0.7001734 1.41425156 0.58132077 -0.6222015 0.64050965 #> 6 6 -1.35505505 1.0595199 1.10398207 -1.08897287 -0.8157586 0.66398092 #> 7 7 -0.10291149 1.5438734 0.45418630 -0.39882285 0.4931574 0.62930098 #> 8 8 -0.06748422 -0.1105542 -1.84632390 0.89499455 -1.0186109 0.40265173 #> 9 9 -0.36947382 1.7376966 -0.17560476 -0.15561064 0.3839091 -0.48148903 #> 10 10 -0.41003145 0.4704748 1.15201231 -1.34925296 0.8606601 0.98056711 #> Day_7 Day_8 type #> 1 0.2527477 1.6112132 peptide #> 2 -0.6254920 -1.7532453 peptide #> 3 -0.9065915 0.7844915 peptide #> 4 -1.8183370 -0.4607098 peptide #> 5 -1.2288927 -0.1256124 peptide #> 6 0.6835002 -0.2511966 peptide #> 7 -1.0158949 -1.6028889 peptide #> 8 0.7137346 1.0315924 lipid #> 9 0.7485573 -1.6879846 lipid #> 10 -1.2447220 -0.4597079 metabolite"},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"choose-your-annotation","dir":"Articles","previous_headings":"","what":"Choose Your Annotation","title":"Get started","text":"addition specifying data type mentioned , methods support three levels information: functional annotation, taxonomy annotation, feature annotation. annotations matched ID columns annotation data, serving another crucial input generating dashboard. facilitate automatic feature link generation using KeggID GOID, users set corresponding column names beforehand. example annotation formats align data inputs. data two layers labels (like cheese dataset), can specify two layers want focus network using gAnnotation() function.","code":"#> ID taxonomic.scope ko_term Pathway #> 1 grp1_24 Bacteria K02706 00195_Photosynthesis #> 2 grp1_25 Bacteria K02706 00195_Photosynthesis #> 3 grp1_26 Bacteria K02706 00195_Photosynthesis #> 4 grp1_1075 Bacteria K02112 00195_Photosynthesis #> 5 grp1_1076 Bacteria K02112 00195_Photosynthesis #> 6 grp1_1358 Bacteria K02112 00195_Photosynthesis Annotation_path_taxon <- gAnnotation(annotations,\"phylum\",\"class\")"},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"network-generation","dir":"Articles","previous_headings":"","what":"Network Generation","title":"Get started","text":"generate network, first scale cluster trajectories across input molecular features. Trajectories clustered using K-means, built-elbow method used select optimal K. build network clustered trajectories adapting GENIE3 algorithm. Specifically, network learned series regressions, cluster centroid predicted expression patterns cluster centroids using random forests. choose random forests potential model interacting features non-linearity without strong assumptions. determine feature clusters predictive given trajectory, Mean Decrease Accuracy subset top predictors whose expression directly influences expression target cluster taken indication putative link.","code":"cluster_result <- data |> gClusters(ncluster = 5,elbow.max=15) network_result <- cluster_result |> gNetwork(ntop = 3) gNetwork_view(network_result)"},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"dashboard-navigation","dir":"Articles","previous_headings":"","what":"Dashboard Navigation","title":"Get started","text":"workflow inputs related gDashboard function. key function processes data runs dashboard, requiring input previous steps. ’ve launched Shiny dashboard, can zoom make adjustments explore interesting findings within data. effectively navigate dashboard generated MolPad, ’ll follow three main steps:","code":"gDashboard(data, cluster_result, Annotation_path_taxon, network_result, id_colname = \"ko_term\" , id_type = \"KEGG\")"},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"choose-a-primary-functional-annotation-and-adjust-edge-density","dir":"Articles","previous_headings":"Dashboard Navigation","what":"Choose a Primary Functional Annotation and Adjust Edge Density","title":"Get started","text":"Start selecting primary functional annotation available options. , fine-tune edge density adjusting threshold value importance score. Nodes turn bright green indicate clusters containing features related chosen functional annotation.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"explore-the-network","dir":"Articles","previous_headings":"Dashboard Navigation","what":"Explore the Network","title":"Get started","text":"Brushing network unveils patterns taxonomic composition typical trajectories. can also zoom specific taxonomic annotations applying filters.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"investigate-feature-details-and-related-function-annotations","dir":"Articles","previous_headings":"Dashboard Navigation","what":"Investigate Feature Details and Related Function Annotations","title":"Get started","text":"Delve feature table examine specifics features within selected clusters. Explore additional related function annotations using drop-options. Click provided links access online information items interest. interface encourages iterative exploration, enabling conduct multiple steps answer specific questions, comparing pattern distributions two functions identifying functionally important community members metabolizing feature interest.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"related-visualization-outputs","dir":"Articles","previous_headings":"","what":"Related Visualization Outputs","title":"Get started","text":"following visualizations integrated dashboard, ’s need run function plot individually. ’ve linked stacked bar plot ribbon plot network facilitate focus context explorations. setup helps detecting similar longitudinal patterns understanding relationships.","code":""},{"path":[]},{"path":[]},{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/articles/whymolpad.html","id":"why-molpad","dir":"Articles","previous_headings":"","what":"Why MolPad?","title":"Why MolPad?","text":"aim network interpretation, MolPad shows improvements 3 important aspects: Capture groups features share longitudinal patterns functional properties within network. Annotate network complex experimental datasets, way dig specific components notable features. Streamlines exploration modularized functions interactive dashboard. package especially facilitates exploration longitudinal molecular co-expression microbiome experiments using focus-plus-context approach, empowering researchers uncover global patterns insights longitudinal microbiomics data. Unlike existing tools, MolPad stands rich network interactivity support contextualization. also equipped beginner-friendly interface self-contained data processing functions, making accessible users limited programming experience.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/whymolpad.html","id":"key-features","dir":"Articles","previous_headings":"","what":"Key Features","title":"Why MolPad?","text":"Trajectory clustering organizing data clusters, helps identify patterns, relationships, underlying structures within dataset. essence, clustering detects groups features observations behave similarly, allowing deeper insights effective decision-making. Network extracting build network clustered trajectories adapting GENIE3 algorithm. Specifically, network learned series regressions, cluster centroid predicted expression patterns cluster centroids using random forests. choose random forests potential model interacting features non-linearity without strong assumptions. Interactive Dashboard package provides interactive dashboard designed help users explore data uncover relationships within complex networks. utilizing focus-plus-context visualization, dashboard allows users zoom specific details keeping broader dataset view. approach enables deeper understanding data, making easier identify patterns make informed decisions.","code":""},{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/articles/whymolpad.html","id":"molpad-dashboard-overview","dir":"Articles","previous_headings":"What does it look like?","what":"MolPad Dashboard Overview","title":"Why MolPad?","text":"overview MolPad Dashboard. explore dashboard effectively, can start following sequence -B-C-D. approach guide cluster-level network, taxonomic-level bar plot, type-level line plot, feature-level table. , can modify detailed settings identify investigate special patterns within data.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/whymolpad.html","id":"discovering-related-patterns","dir":"Articles","previous_headings":"What does it look like?","what":"Discovering related patterns","title":"Why MolPad?","text":"short example discovering related patterns using network plot: shade edges represents proximity nodes. brushed area, Groups 1-7-8 (circled solid black lines) Groups 1-2 (circled blue dashed lines) strongly linked. Groups 1, 7, 8, patterns form W-shape noticeable peak time section. Although Group 1 shows higher volatility compared Group 2, groups display highly overlapping increasing trend. detailed instructions, please visit “Get Started” page relevant function reference page.","code":""},{"path":"https://kaiyanm.github.io/MolPad/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Kaiyan Ma. Author, maintainer.","code":""},{"path":"https://kaiyanm.github.io/MolPad/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Ma K (2024). MolPad: MolPad: R-Shiny Package Cluster Co-Expression Analysis Longitudinal Microbiomics. R package version 0.1.0, https://kaiyanm.github.io/MolPad/.","code":"@Manual{, title = {MolPad: MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics}, author = {Kaiyan Ma}, year = {2024}, note = {R package version 0.1.0}, url = {https://kaiyanm.github.io/MolPad/}, }"},{"path":"https://kaiyanm.github.io/MolPad/index.html","id":"molpad-","dir":"","previous_headings":"","what":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","title":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","text":"R-Shiny Package Cluster Co-Expression Analysis Longitudinal Microbiomics","code":""},{"path":"https://kaiyanm.github.io/MolPad/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","text":"MolPad offers visualization dashboard tool designed enhance understanding molecular co-expression works context microbiome data. approach involves using cluster network provide initial overview relationships across multiple omics, added functionality interactively zoom specific areas interest. facilitate analysis, ’ve developed focus-plus-context strategy connects online curated annotations. Additionally, package simplifies entire pipeline creating dashboard. user-friendly design makes accessible even people limited R programming experience. Check cheese-data demo .","code":""},{"path":"https://kaiyanm.github.io/MolPad/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","text":"can either install devtools, {r, eval = FALSE} # Install package R: install.packages(\"devtools\") library(devtools) install_github(\"KaiyanM/MolPad\") clone repository local computer (example, onto ./Github) installing: {r, eval = FALSE} # Download install package R: setwd(\"./GitHub\") install(\"MolPad\") , load package: {r,eval=FALSE} library(MolPad)","code":""},{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/index.html","id":"molpad-could-help-you-with","dir":"","previous_headings":"Usage","what":"MolPad could help you with:","title":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","text":"Clustering data k-means building group network. Find significant trend patterns datasets. Target interaction groups, taxons, pathways. Visualize distribution features specific pathways group network. Search particular features user-defined labels. Check detailed information feature automatically generated hyperlinks. better overall understanding datasets.","code":""},{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/index.html","id":"getting-help","dir":"","previous_headings":"","what":"Getting Help","title":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","text":"need assistance MolPad, two primary ways seek help: Ask us anything related MolPad! add question, create issue repository. Stack Overflow another excellent resource answering common issues R. Remember ’s particularly effective can provide reproducible example shows specific problem ’re .","code":""},{"path":"https://kaiyanm.github.io/MolPad/index.html","id":"contribution","dir":"","previous_headings":"","what":"Contribution","title":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","text":"contribute project, use following workflow: fork repository –> create local copy –> submit pull request.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":null,"dir":"Reference","previous_headings":"","what":"Cheese data — cheese","title":"Cheese data — cheese","text":"context cheese production, regular application brine solution maturation technique promotes uniformity microbial populations cheese's surface facilitates interactions among microorganisms. investigation involved analysis longitudinal dataset encompassing three washed-rind cheese communities sampled ripening process.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Cheese data — cheese","text":"two datasets: cheese annotations","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Cheese data — cheese","text":"Reference: doi:10.1128/msystems.00701-22","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Cheese data — cheese","text":"study uncovered remarkably consistent microbial progression within cheese. bacterial community, Firmicutes dominate outset, Proteobacteria swiftly assuming dominance end ripening period. Additionally, Cheese Cheese C consistently demonstrate establishment Actinobacteria Bacteroidetes, distinct manner. corroborate findings using MolPad dashboard, conducted analysis two cheeses (C) three production batches, spanning weeks 2 13.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":"cheese-data","dir":"Reference","previous_headings":"","what":"cheese data","title":"Cheese data — cheese","text":"data frame 106239 rows 18 variables: ID sample ID A_1~A_5 Time series data measured cheese . C_1~C_5 Time series data measured cheese C. domain category feature phylum category feature class category feature order category feature family category feature genus category feature","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":"annotation-data","dir":"Reference","previous_headings":"","what":"annotation data","title":"Cheese data — cheese","text":"data frame 86156 rows 9 variables: ID sample ID GO_ID GO IDs, represents link gene product type molecular function KEGG_ID KEGG IDs, linking genomic information higher order functional information. domain category feature phylum category feature class category feature order category feature family category feature genus category feature","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cheese data — cheese","text":"","code":"data(cheese) head(annotations) #> # A tibble: 6 × 9 #> ID GO_ID KEGG_ID domain phylum class order family genus #> #> 1 9 - - Bacteria Pseudomonadota Alphaproteobac… Caul… Caulo… Caul… #> 2 10 - - Bacteria Pseudomonadota Alphaproteobac… Caul… Caulo… Caul… #> 3 11 - - Bacteria Pseudomonadota Alphaproteobac… Hyph… Barto… Bart… #> 4 12 - - Bacteria Pseudomonadota Alphaproteobac… Hyph… Bruce… Bruc… #> 5 13 - - Bacteria Pseudomonadota Alphaproteobac… Hyph… Nitro… Brad… #> 6 14 - - Bacteria Pseudomonadota Alphaproteobac… Hyph… Phyll… Meso… head(cheese) #> # A tibble: 6 × 18 #> ID A_1 A_2 A_3 A_4 A_5 C_1 C_3 C_4 C_5 GO_ID KEGG_ID #> #> 1 1 38 23 27 5 11 13 1 3 0 NA NA #> 2 2 23 6 4 0 9 21 1 0 2 NA NA #> 3 3 24 5 37 10 19 3 0 1 4 NA NA #> 4 4 3 2 4 2 16 56 1 7 17 NA NA #> 5 5 58 14 45 13 32 82 3 8 3 NA NA #> 6 6 12 9 14 4 13 2 0 1 4 NA NA #> # ℹ 6 more variables: domain , phylum , class , order , #> # family , genus "},{"path":"https://kaiyanm.github.io/MolPad/reference/color_palettes__.html","id":null,"dir":"Reference","previous_headings":"","what":"Color palettes — color_palettes__","title":"Color palettes — color_palettes__","text":"internal function built-color palettes.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/color_palettes__.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Color palettes — color_palettes__","text":"","code":"color_palettes__(name)"},{"path":"https://kaiyanm.github.io/MolPad/reference/color_palettes__.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Color palettes — color_palettes__","text":"name string two options: \"graytone\" \"darkwarm\".","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/color_palettes__.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Color palettes — color_palettes__","text":"","code":"color_palettes__(\"graytone\") #> [1] \"#c3c5c7\" \"#30beba\" \"#998a73\" \"#807566\" \"#a5b1c9\" \"#5d5232\" \"#9a2b41\" #> [8] \"#d6b7a2\" \"#882db4\" \"#b47a53\" color_palettes__(\"darkwarm\") #> [1] \"#251305\" \"#C70A80\" \"#FBCB0A\" \"#ff1122\" \"#7D7463\" \"#CECE5A\" \"#FF9B9B\" #> [8] \"#A459D1\" \"#00235B\" \"#7E1717\""},{"path":"https://kaiyanm.github.io/MolPad/reference/convert_range.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert range — convert_range","title":"Convert range — convert_range","text":"internal function range conversion. element x, returns distance minimal values divided range.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/convert_range.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert range — convert_range","text":"","code":"convert_range(x)"},{"path":"https://kaiyanm.github.io/MolPad/reference/convert_range.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert range — convert_range","text":"x vector numbers.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/convert_range.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert range — convert_range","text":"vector calculated (x - min(x)) / (max(x) - min(x))","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/convert_range.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert range — convert_range","text":"","code":"convert_range(5:10) #> [1] 0.0 0.2 0.4 0.6 0.8 1.0"},{"path":"https://kaiyanm.github.io/MolPad/reference/extend.color__.html","id":null,"dir":"Reference","previous_headings":"","what":"Extend color palettes — extend.color__","title":"Extend color palettes — extend.color__","text":"internal function designed pair vectors color palettes automatically generate colors longer vectors find match.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/extend.color__.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extend color palettes — extend.color__","text":"","code":"extend.color__(n, colors, extendby = 1, alpha = 1, ...)"},{"path":"https://kaiyanm.github.io/MolPad/reference/extend.color__.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extend color palettes — extend.color__","text":"n number. length feature vector want match colors. colors vector colors (finite). extendby number select 1, 2, 3, 4 5, representing distinct auto-fill schemes. alpha number select range 0,1.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gAnnotation.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate processed annotation — gAnnotation","title":"Generate processed annotation — gAnnotation","text":"gAnnotation() provides standard input format dashboard, allowing users select two columns primary factors wish visualize describe data.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gAnnotation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate processed annotation — gAnnotation","text":"","code":"gAnnotation(data, first_order, second_order)"},{"path":"https://kaiyanm.github.io/MolPad/reference/gAnnotation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate processed annotation — gAnnotation","text":"data data.frame containing annotations used describing features measured time point. also required include ID least two categorical variables. first_order string. name one column categorical variable data. second_order string. name another column categorical variable data.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gAnnotation.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate processed annotation — gAnnotation","text":"Guidelines Selecting Annotations: first-order annotation recommended functional, pathway functional system. parameter primarily serve purpose filtering one network time displaying dashboard. second-order annotation utilized illustrate composition first-order annotation using bar plot. Therefore, better set taxon, class label, etc.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gAnnotation.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate processed annotation — gAnnotation","text":"","code":"data(test_data) test_annotations_processed <- gAnnotation(test_annotations,system,class) head(test_annotations_processed) #> ID #> 1 1 #> 2 2 #> 3 3 #> 4 4 #> 5 5 #> 6 6 #> GO_ID #> 1 #> 2 #> 3 GO:0003674,GO:0003824,GO:0005575,GO:0006629,GO:0006644,GO:0006650,GO:0006655,GO:0006793,GO:0006796,GO:0008150,GO:0008152,GO:0008610,GO:0008654,GO:0008808,GO:0009058,GO:0009987,GO:0016020,GO:0016740,GO:0016772,GO:0016780,GO:0019637,GO:0030572,GO:0032048,GO:0032049,GO:0044237,GO:0044238,GO:0044249,GO:0044255,GO:0045017,GO:0046471,GO:0046474,GO:0046486,GO:0071704,GO:0090407,GO:1901576 #> 4 GO:0003674,GO:0003676,GO:0003723,GO:0003729,GO:0003824,GO:0004654,GO:0005488,GO:0005575,GO:0005622,GO:0005623,GO:0005737,GO:0006139,GO:0006401,GO:0006402,GO:0006725,GO:0006807,GO:0008150,GO:0008152,GO:0009056,GO:0009057,GO:0009892,GO:0009987,GO:0010468,GO:0010605,GO:0010629,GO:0016070,GO:0016071,GO:0016740,GO:0016772,GO:0016779,GO:0019222,GO:0019439,GO:0034641,GO:0034655,GO:0043170,GO:0044237,GO:0044238,GO:0044248,GO:0044260,GO:0044265,GO:0044270,GO:0044424,GO:0044464,GO:0046483,GO:0046700,GO:0048519,GO:0050789,GO:0060255,GO:0065007,GO:0071704,GO:0090304,GO:0097159,GO:1901360,GO:1901361,GO:1901363,GO:1901575 #> 5 #> 6 #> KEGG_ID Pathway taxonomic.scope #> 1 K07124 Integumentary System hormonal proteins #> 2 Skeletal System structural proteins #> 3 K06131 Muscular System enzymes #> 4 K00962 Nervous System contractile proteins #> 5 K02083 Endocrine System contractile proteins #> 6 Cardiovascular System structural proteins"},{"path":"https://kaiyanm.github.io/MolPad/reference/gClusters.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate clusters — gClusters","title":"Generate clusters — gClusters","text":"gClusters() returns clusters generated k-means yield elbow plot way finding optimal parameter.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gClusters.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate clusters — gClusters","text":"","code":"gClusters(data, ncluster = 20, elbow.max = 50, ...)"},{"path":"https://kaiyanm.github.io/MolPad/reference/gClusters.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate clusters — gClusters","text":"data scaled data.frame contain variables ID, value time_1, ..., value time_k, type extracting patterns across time. See also pre_process(). ncluster number clusters. related complexity information network: choosing ncluster, suggest thinking many nodes show visualization representative want clustered pattern. elbow.max number maximum value x-axis elbow method plot. larger expected ncluster smaller sample size. iter.max number maximum iterations allowed k-means. See also stats::kmeans. nstart number random attempts generating initial configurations. k-means algorithm choose best one among attempts. larger data, 'nstart' can set lower just set 1. See also stats::kmeans.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gClusters.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate clusters — gClusters","text":"function return list 2 elements: k-means cluster result elbow method plot.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gClusters.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate clusters — gClusters","text":"determine optimal number clusters (ncluster), advised closely examine elbow plot identify point graph substantial change 'elbow' occurs. often indicative suitable cluster count. cases dataset extensive intricate, might consider increasing value elbow.max ensure comprehensive exploration potential cluster counts. can help achieving accurate meaningful results, especially working larger complex datasets. function can executed data parameter outset. However, achieve best clustering results, adjustments recommended. initial run, users expected adjust function's parameters based clustering outcomes elbow plot analysis.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gClusters.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate clusters — gClusters","text":"","code":"data(test_data) reslist <- gClusters(test_data_processed) # k-means result reslist[[1]] #> K-means clustering with 20 clusters of sizes 7, 6, 7, 10, 8, 2, 3, 5, 4, 6, 10, 3, 3, 4, 4, 6, 4, 3, 2, 3 #> #> Cluster means: #> T1 T2 T3 T4 T5 T6 #> 1 2.22789830 0.02950883 -0.75836275 -0.2619747 -0.5953255 -0.70974025 #> 2 2.77349939 -0.21724464 -0.35972402 -0.4172516 -0.5000122 -0.11678355 #> 3 0.93534606 -0.39771771 0.31238452 1.1531782 1.1995301 -1.05203742 #> 4 -0.27715339 -0.39760069 -0.06466866 1.3995407 2.1316423 -0.28068841 #> 5 0.11559709 -0.19000668 -0.25629794 -0.1487697 -0.4383348 2.58301481 #> 6 -0.27847690 -0.82258408 -0.82258408 -0.8225841 1.2338761 -0.82258408 #> 7 0.26998015 -0.08094677 -0.28926312 -0.7813127 -0.6065051 -0.14945230 #> 8 -0.81355388 -0.84696982 -0.32573017 1.1218534 2.0955812 -0.56207309 #> 9 1.45173820 0.26236463 1.31600581 -0.2531012 0.2004547 -1.12389420 #> 10 0.34700978 0.13673201 0.82464908 0.2571569 1.2656031 0.63541302 #> 11 -0.19020083 -0.69654961 -0.66836949 -0.4657663 -0.3851487 -0.55904213 #> 12 1.62582942 1.51619556 -0.35316244 0.2978109 -0.1777508 0.02299732 #> 13 -0.09187847 -0.87257293 -0.87257293 -0.8725729 -0.5696817 2.05668333 #> 14 0.85679806 -0.37993358 -0.46099690 0.3934155 0.5857582 1.85027957 #> 15 1.12205113 0.09185659 -0.89815993 -0.7233153 -0.5717924 -0.86037548 #> 16 2.09193480 -0.33320285 -0.50786563 -0.5579659 -0.5579659 1.54559773 #> 17 1.56599593 -0.49119311 -0.83000857 -0.6160199 -0.2092753 1.19151568 #> 18 0.89463785 0.01698143 -0.57023879 -1.1815834 -1.1815834 1.18824796 #> 19 -0.13543375 -0.90710305 -0.66248003 -0.4178570 -0.6624800 1.37010464 #> 20 1.31631350 -0.76059916 -0.76059916 1.5193444 0.6006058 0.84385791 #> T7 T8 T9 T10 #> 1 -0.567081109 0.132060988 0.03929621 0.46372005 #> 2 -0.357532815 -0.298895212 -0.29119673 -0.21485863 #> 3 -1.222209771 -0.334925678 -0.36853535 -0.22501291 #> 4 -0.548494719 -0.646992821 -0.66392148 -0.65166279 #> 5 -0.006327004 -0.327677972 -0.66559889 -0.66559889 #> 6 -0.822584085 1.233876127 1.23387613 0.68976894 #> 7 -0.763902351 -0.412975440 0.39220455 2.42217307 #> 8 -0.651292750 -0.498839228 0.42446013 0.05656427 #> 9 -0.987867401 -0.340013670 -0.33085684 -0.19483004 #> 10 -1.004339966 -0.620506186 -0.71949086 -1.12222690 #> 11 -0.566522777 0.002653353 1.73917982 1.78976662 #> 12 -0.770498738 -0.620423681 -0.77049874 -0.77049874 #> 13 0.882337606 0.086536293 0.12686085 0.12686085 #> 14 -0.526130916 -0.896529515 -0.89652951 -0.52613092 #> 15 -0.822591022 0.091471523 1.00908283 1.56177203 #> 16 -0.425513174 -0.557965933 -0.30571480 -0.39133827 #> 17 0.774154665 0.196302112 -0.62663627 -0.95483532 #> 18 0.894637851 -0.836550610 0.41343688 0.36201421 #> 19 -0.907103054 -0.417856999 1.37010464 1.37010464 #> 20 -0.666108041 -0.666108041 -0.66610804 -0.76059916 #> #> Clustering vector: #> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 #> 11 16 6 10 10 11 13 12 4 8 3 7 15 2 3 8 5 11 9 1 #> 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 #> 17 11 5 16 2 8 20 1 5 4 17 4 10 6 4 10 3 15 11 12 #> 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 #> 5 16 11 11 14 11 5 14 16 5 1 17 18 15 5 17 13 4 1 4 #> 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 #> 3 14 20 15 10 16 2 14 9 9 7 8 5 12 7 4 3 4 3 20 #> 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 #> 3 19 1 13 19 16 9 2 18 2 4 18 4 1 11 10 8 2 11 1 #> #> Within cluster sum of squares by cluster: #> [1] 13.8467580 2.0713631 15.4102772 5.9461649 8.0412334 0.9921913 #> [7] 3.1842269 3.4617088 9.9418120 17.6305488 8.0515257 4.9390800 #> [13] 4.0041862 7.3057948 4.7723673 3.0260293 7.3290085 5.6033175 #> [19] 0.9549143 2.4583646 #> (between_SS / total_SS = 83.4 %) #> #> Available components: #> #> [1] \"cluster\" \"centers\" \"totss\" \"withinss\" \"tot.withinss\" #> [6] \"betweenss\" \"size\" \"iter\" \"ifault\" # elbow plot reslist[[2]]"},{"path":"https://kaiyanm.github.io/MolPad/reference/gDashboard.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate shiny dashboard — gDashboard","title":"Generate shiny dashboard — gDashboard","text":"outputs g-functions, ready create custom Molpad dashboard. sure specify web ID columns corresponding column names.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gDashboard.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate shiny dashboard — gDashboard","text":"","code":"gDashboard( data, cluster, annotation, networkres, dashboardtitle = \"MolPad Dashboard\", id_colname = NULL, id_type = NULL )"},{"path":"https://kaiyanm.github.io/MolPad/reference/gDashboard.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate shiny dashboard — gDashboard","text":"data output pre_process():scaled data.frame contain variables ID, value time_1, ..., value time_k, type. cluster output gClusters(): list contains result k-means Cluster means, vectors sum squares. annotation output gAnnotation(): data.frame containing annotations describing features. Variables must include ID, Pathway, taxonomic.scope. Note NA permitted 3 variables. networkres output gNetwork(): data.frame 4 variables weight,IncNodePurity,var_names,. dashboardtitle string customized dashboard name. id_colname single string string vector. column names annotation dataset vector contains external database IDs. id_type single string string vector. corresponding database names columns, must chosen \"KEGG\" \"GO\".","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gDashboard.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate shiny dashboard — gDashboard","text":"Please ensure columns containing external database IDs adhere following standards: KEGG ID: Begin 'K' followed 5 digits, example, K05685 K06671. GO ID: Begin 'GO:' followed 7 digits, GO:0003674.\"","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gDashboard.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate shiny dashboard — gDashboard","text":"","code":"if (FALSE) { data(test_data) gDashboard(test_data_processed,test_cluster,test_annotations_processed,test_network,id_colname = c(\"GO_ID\",\"KEGG_ID\"),id_type = c(\"GO\",\"KEGG\")) }"},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate prediction network — gNetwork","title":"Generate prediction network — gNetwork","text":"gNetwork() generates prediction network functional annotation. every feature, features considered independent variables, top predictors selected based %IncMSE.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate prediction network — gNetwork","text":"","code":"gNetwork(clusters, ntop = 10, method = \"randomforest\")"},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate prediction network — gNetwork","text":"clusters list two outputs gClusters(). first element k-means result (See also stats::kmeans), element plot automatically omitted ease directly passing results function. ntop number pick set top n predictors(default: top 10) feature important ones. used construct network among clustered patterns. seed number random seed.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate prediction network — gNetwork","text":"assess relationships clusters using Mean Squared Error (MSE) changes resulting random shuffling. gNetwork()'s output includes edge weights node pairs, essential inputs dashboard.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate prediction network — gNetwork","text":"","code":"data(test_data) networkres <- gNetwork(test_cluster, ntop = 3) head(networkres) #> weight IncNodePurity var_names from #> 1 1.6429045 0.7371260 Group_3 Group_1 #> 2 0.4588961 0.9310697 Group_5 Group_1 #> 3 -0.7833169 0.6761286 Group_2 Group_1 #> 4 5.0076415 0.8821582 Group_5 Group_2 #> 5 4.1618215 1.1976058 Group_4 Group_2 #> 6 2.5714583 1.2820391 Group_1 Group_2 gNetwork_view(networkres)"},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork_view.html","id":null,"dir":"Reference","previous_headings":"","what":"View the feature importance plot — gNetwork_view","title":"View the feature importance plot — gNetwork_view","text":"gNetwork_view() Generate feature importance plot random forest result. plot visually highlights importance individual cluster within dataset, helping identify key factors predictive modeling.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork_view.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"View the feature importance plot — gNetwork_view","text":"","code":"gNetwork_view(data)"},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork_view.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"View the feature importance plot — gNetwork_view","text":"data dataframe; output gNetwork(), including 4 variables: weight, IncNodePurity, var_names . weight edge value, IncNodePurity(%IncMSE) measure predictions result var_names permuted. dependent variable round random forest.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork_view.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"View the feature importance plot — gNetwork_view","text":"","code":"data(test_data) gNetwork_view(test_network) #> Warning: Removed 1 rows containing missing values (`geom_segment()`). #> Warning: Removed 1 rows containing missing values (`geom_point()`)."},{"path":"https://kaiyanm.github.io/MolPad/reference/make_line_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Make line plot — make_line_plot","title":"Make line plot — make_line_plot","text":"Generate line ribbon plot dashboard.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_line_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make line plot — make_line_plot","text":"","code":"make_line_plot(dfgroup_long, selected_groups, selected_taxa)"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_line_plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Make line plot — make_line_plot","text":"function makes ribbon plot every brush action Shiny show min, max, mean value clustered pattern group across time. ribbon plot grouped colored type variable input datasets. See also ggplot2::geom_ribbon.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_line_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Make line plot — make_line_plot","text":"","code":"data(test_data) make_line_plot(test_maindata, \"Group_5\", c(\"hormonal proteins\",\"structural proteins\",\"enzymes\",\"storage proteins\",\"antibodies\",\"transport proteins\"))"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_stackbar_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Make stackbar plot — make_stackbar_plot","title":"Make stackbar plot — make_stackbar_plot","text":"Generate stackbar plot dashboard.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_stackbar_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make stackbar plot — make_stackbar_plot","text":"","code":"make_stackbar_plot(dfgroup_long, selected_groups, selected_taxa)"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_stackbar_plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Make stackbar plot — make_stackbar_plot","text":"function makes bar plot every brush action Shiny show components clustered pattern group. bar plot colored taxonomic.scope variable processed annotation dataset generated gAnnotation(). See also ggplot2::geom_bar.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_stackbar_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Make stackbar plot — make_stackbar_plot","text":"","code":"data(test_data) make_stackbar_plot(test_maindata, \"Group_5\", c(\"hormonal proteins\",\"structural proteins\",\"enzymes\",\"storage proteins\",\"antibodies\",\"transport proteins\"))"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_graph.html","id":null,"dir":"Reference","previous_headings":"","what":"Make graph plot — make_the_graph","title":"Make graph plot — make_the_graph","text":"Generate graph dashboard.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_graph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make graph plot — make_the_graph","text":"","code":"make_the_graph(ptw, network_output, min_weight, s_ptw, graph_layout)"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_graph.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Make graph plot — make_the_graph","text":"function makes network plot every Pathway selection Shiny show relationship among clustered pattern groups. network plot built dataset generated gNetwork() can adjusted layout minimum weights. See also ggraph::ggraph.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_graph.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Make graph plot — make_the_graph","text":"","code":"data(test_data) make_the_graph(test_graphptw, test_network, 0.03, \"Muscular System\",\"kk\")"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_table.html","id":null,"dir":"Reference","previous_headings":"","what":"Make table from brushed region — make_the_table","title":"Make table from brushed region — make_the_table","text":"Generate table selected groups dashboard.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make table from brushed region — make_the_table","text":"","code":"make_the_table(p)"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Make table from brushed region — make_the_table","text":"p ggplot output, see also ggplot2::ggplot_build.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_table.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Make table from brushed region — make_the_table","text":"function simply aims collect position information brushed area network plot returns annotation table corresponding features. takes plot object produce object information table.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/mass_produce_lm__.html","id":null,"dir":"Reference","previous_headings":"","what":"Mass produce linear model — mass_produce_lm__","title":"Mass produce linear model — mass_produce_lm__","text":"Take column dependent vairable time, produce n linear models n columns dataset.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/mass_produce_lm__.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mass produce linear model — mass_produce_lm__","text":"","code":"mass_produce_lm__(data)"},{"path":"https://kaiyanm.github.io/MolPad/reference/mass_produce_lm__.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mass produce linear model — mass_produce_lm__","text":"data dataframe.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/mass_produce_lm__.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Mass produce linear model — mass_produce_lm__","text":"internal function designed automatically generate list functions regression. column considered response variable columns. resulting functions can utilized inputs random forest regression gNetwork().","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/match.color__.html","id":null,"dir":"Reference","previous_headings":"","what":"Match color — match.color__","title":"Match color — match.color__","text":"Match vector finite list colors.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/match.color__.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Match color — match.color__","text":"","code":"match.color__(valist, mycolors, extendby = 5)"},{"path":"https://kaiyanm.github.io/MolPad/reference/match.color__.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Match color — match.color__","text":"valist vector want specify colors element. mycolors vector colors. extendby number select 1, 2, 3, 4, 5, representing distinct auto-fill schemes.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/match.color__.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Match color — match.color__","text":"","code":"my_vector <- paste0(\"N_\",1:10) match.color__(my_vector,c(\"red\",\"yellow\",\"blue\")) #> N_1 N_2 N_3 N_4 N_5 #> \"red\" \"yellow\" \"blue\" \"white\" \"antiquewhite3\" #> N_6 N_7 N_8 N_9 N_10 #> \"aquamarine3\" \"azure3\" \"bisque2\" \"blue\" \"blueviolet\""},{"path":"https://kaiyanm.github.io/MolPad/reference/match_database.html","id":null,"dir":"Reference","previous_headings":"","what":"Match database — match_database","title":"Match database — match_database","text":"matches selected columns online database names.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/match_database.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Match database — match_database","text":"","code":"match_database(data, id_colname, id_type)"},{"path":"https://kaiyanm.github.io/MolPad/reference/match_database.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Match database — match_database","text":"match_database() internal function matches selected columns dataset corresponding databases. Currently MolPad support two types annotation database: GO KEGG. want custom URL platform, download modify paste_URL() function.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/match_database.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Match database — match_database","text":"","code":"data(test_data) head(test_annotations_processed) #> ID #> 1 1 #> 2 2 #> 3 3 #> 4 4 #> 5 5 #> 6 6 #> GO_ID #> 1 #> 2 #> 3 GO:0003674,GO:0003824,GO:0005575,GO:0006629,GO:0006644,GO:0006650,GO:0006655,GO:0006793,GO:0006796,GO:0008150,GO:0008152,GO:0008610,GO:0008654,GO:0008808,GO:0009058,GO:0009987,GO:0016020,GO:0016740,GO:0016772,GO:0016780,GO:0019637,GO:0030572,GO:0032048,GO:0032049,GO:0044237,GO:0044238,GO:0044249,GO:0044255,GO:0045017,GO:0046471,GO:0046474,GO:0046486,GO:0071704,GO:0090407,GO:1901576 #> 4 GO:0003674,GO:0003676,GO:0003723,GO:0003729,GO:0003824,GO:0004654,GO:0005488,GO:0005575,GO:0005622,GO:0005623,GO:0005737,GO:0006139,GO:0006401,GO:0006402,GO:0006725,GO:0006807,GO:0008150,GO:0008152,GO:0009056,GO:0009057,GO:0009892,GO:0009987,GO:0010468,GO:0010605,GO:0010629,GO:0016070,GO:0016071,GO:0016740,GO:0016772,GO:0016779,GO:0019222,GO:0019439,GO:0034641,GO:0034655,GO:0043170,GO:0044237,GO:0044238,GO:0044248,GO:0044260,GO:0044265,GO:0044270,GO:0044424,GO:0044464,GO:0046483,GO:0046700,GO:0048519,GO:0050789,GO:0060255,GO:0065007,GO:0071704,GO:0090304,GO:0097159,GO:1901360,GO:1901361,GO:1901363,GO:1901575 #> 5 #> 6 #> KEGG_ID Pathway taxonomic.scope #> 1 K07124 Integumentary System hormonal proteins #> 2 Skeletal System structural proteins #> 3 K06131 Muscular System enzymes #> 4 K00962 Nervous System contractile proteins #> 5 K02083 Endocrine System contractile proteins #> 6 Cardiovascular System structural proteins head(match_database(test_annotations_processed,id_colname = c(\"GO_ID\",\"KEGG_ID\"),id_type = c(\"GO\",\"KEGG\"))) #> ID #> 1 1 #> 2 2 #> 3 3 #> 4 4 #> 5 5 #> 6 6 #> GO_ID #> 1 #> 2 #> 3 GO:0003674<\/a>
GO:0003824<\/a>
GO:0005575<\/a>
GO:0006629<\/a>
GO:0006644<\/a>
GO:0006650<\/a>
GO:0006655<\/a>
GO:0006793<\/a>
GO:0006796<\/a>
GO:0008150<\/a>
GO:0008152<\/a>
GO:0008610<\/a>
GO:0008654<\/a>
GO:0008808<\/a>
GO:0009058<\/a>
GO:0009987<\/a>
GO:0016020<\/a>
GO:0016740<\/a>
GO:0016772<\/a>
GO:0016780<\/a>
GO:0019637<\/a>
GO:0030572<\/a>
GO:0032048<\/a>
GO:0032049<\/a>
GO:0044237<\/a>
GO:0044238<\/a>
GO:0044249<\/a>
GO:0044255<\/a>
GO:0045017<\/a>
GO:0046471<\/a>
GO:0046474<\/a>
GO:0046486<\/a>
GO:0071704<\/a>
GO:0090407<\/a>
GO:1901576<\/a> #> 4 GO:0003674<\/a>
GO:0003676<\/a>
GO:0003723<\/a>
GO:0003729<\/a>
GO:0003824<\/a>
GO:0004654<\/a>
GO:0005488<\/a>
GO:0005575<\/a>
GO:0005622<\/a>
GO:0005623<\/a>
GO:0005737<\/a>
GO:0006139<\/a>
GO:0006401<\/a>
GO:0006402<\/a>
GO:0006725<\/a>
GO:0006807<\/a>
GO:0008150<\/a>
GO:0008152<\/a>
GO:0009056<\/a>
GO:0009057<\/a>
GO:0009892<\/a>
GO:0009987<\/a>
GO:0010468<\/a>
GO:0010605<\/a>
GO:0010629<\/a>
GO:0016070<\/a>
GO:0016071<\/a>
GO:0016740<\/a>
GO:0016772<\/a>
GO:0016779<\/a>
GO:0019222<\/a>
GO:0019439<\/a>
GO:0034641<\/a>
GO:0034655<\/a>
GO:0043170<\/a>
GO:0044237<\/a>
GO:0044238<\/a>
GO:0044248<\/a>
GO:0044260<\/a>
GO:0044265<\/a>
GO:0044270<\/a>
GO:0044424<\/a>
GO:0044464<\/a>
GO:0046483<\/a>
GO:0046700<\/a>
GO:0048519<\/a>
GO:0050789<\/a>
GO:0060255<\/a>
GO:0065007<\/a>
GO:0071704<\/a>
GO:0090304<\/a>
GO:0097159<\/a>
GO:1901360<\/a>
GO:1901361<\/a>
GO:1901363<\/a>
GO:1901575<\/a> #> 5 #> 6 #> KEGG_ID #> 1 K07124<\/a> #> 2 #> 3 K06131<\/a> #> 4 K00962<\/a> #> 5 K02083<\/a> #> 6 #> Pathway taxonomic.scope #> 1 Integumentary System hormonal proteins #> 2 Skeletal System structural proteins #> 3 Muscular System enzymes #> 4 Nervous System contractile proteins #> 5 Endocrine System contractile proteins #> 6 Cardiovascular System structural proteins"},{"path":"https://kaiyanm.github.io/MolPad/reference/paste_URL.html","id":null,"dir":"Reference","previous_headings":"","what":"Paste URL — paste_URL","title":"Paste URL — paste_URL","text":"Retrieve database IDs associate respective URLs.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/paste_URL.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Paste URL — paste_URL","text":"","code":"paste_URL(x, id_type)"},{"path":"https://kaiyanm.github.io/MolPad/reference/paste_URL.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Paste URL — paste_URL","text":"","code":"data(test_data) paste_URL(test_annotations$GO_ID[1:4], id_type = \"GO\") #> [1] NA #> [2] NA #> [3] \"GO:0003674<\/a>
GO:0003824<\/a>
GO:0005575<\/a>
GO:0006629<\/a>
GO:0006644<\/a>
GO:0006650<\/a>
GO:0006655<\/a>
GO:0006793<\/a>
GO:0006796<\/a>
GO:0008150<\/a>
GO:0008152<\/a>
GO:0008610<\/a>
GO:0008654<\/a>
GO:0008808<\/a>
GO:0009058<\/a>
GO:0009987<\/a>
GO:0016020<\/a>
GO:0016740<\/a>
GO:0016772<\/a>
GO:0016780<\/a>
GO:0019637<\/a>
GO:0030572<\/a>
GO:0032048<\/a>
GO:0032049<\/a>
GO:0044237<\/a>
GO:0044238<\/a>
GO:0044249<\/a>
GO:0044255<\/a>
GO:0045017<\/a>
GO:0046471<\/a>
GO:0046474<\/a>
GO:0046486<\/a>
GO:0071704<\/a>
GO:0090407<\/a>
GO:1901576<\/a>\" #> [4] \"GO:0003674<\/a>
GO:0003676<\/a>
GO:0003723<\/a>
GO:0003729<\/a>
GO:0003824<\/a>
GO:0004654<\/a>
GO:0005488<\/a>
GO:0005575<\/a>
GO:0005622<\/a>
GO:0005623<\/a>
GO:0005737<\/a>
GO:0006139<\/a>
GO:0006401<\/a>
GO:0006402<\/a>
GO:0006725<\/a>
GO:0006807<\/a>
GO:0008150<\/a>
GO:0008152<\/a>
GO:0009056<\/a>
GO:0009057<\/a>
GO:0009892<\/a>
GO:0009987<\/a>
GO:0010468<\/a>
GO:0010605<\/a>
GO:0010629<\/a>
GO:0016070<\/a>
GO:0016071<\/a>
GO:0016740<\/a>
GO:0016772<\/a>
GO:0016779<\/a>
GO:0019222<\/a>
GO:0019439<\/a>
GO:0034641<\/a>
GO:0034655<\/a>
GO:0043170<\/a>
GO:0044237<\/a>
GO:0044238<\/a>
GO:0044248<\/a>
GO:0044260<\/a>
GO:0044265<\/a>
GO:0044270<\/a>
GO:0044424<\/a>
GO:0044464<\/a>
GO:0046483<\/a>
GO:0046700<\/a>
GO:0048519<\/a>
GO:0050789<\/a>
GO:0060255<\/a>
GO:0065007<\/a>
GO:0071704<\/a>
GO:0090304<\/a>
GO:0097159<\/a>
GO:1901360<\/a>
GO:1901361<\/a>
GO:1901363<\/a>
GO:1901575<\/a>\""},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":null,"dir":"Reference","previous_headings":"","what":"Pre-processing datasets — pre_process","title":"Pre-processing datasets — pre_process","text":"pre_process() function aids processing data inputs automatically establishes standardized format future use. allows two types data input: list datasets different sources long dataset containing specified last column type.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pre-processing datasets — pre_process","text":"","code":"pre_process( data, typenameList = NULL, replaceNA = TRUE, scale = TRUE, autoColName = \"Sec_\" )"},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pre-processing datasets — pre_process","text":"data data.frame describe feature one row. data contain variables ID value time_1, ..., value time_k, type extracting patterns across time. Note initial last column must exactly ID type. multiple data.frame format needs analyzed, also put list data.frame argument. case, variable type required generated next argument typenameList. typenameList vector strings. parameter used clarify source names data.frame, applicable input data list data.frame. default, set \"Dataset_1\", \"Dataset_2\", ..., etc. scale Logical; scale TRUE (default), standardize data.frame row base::scale. converts original value z-score. See also scale_by_row__(). autoColName string; autoColName -NULL (default), automatically set uniform column names data.frames. parameter applicable input data list data.frame. replaceNa Logical; replaceNa TRUE (default), replace NA 0.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pre-processing datasets — pre_process","text":"function returns long data.frame columns ID, value time_1, ..., value time_k, type.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Pre-processing datasets — pre_process","text":"consider two distinct scenarios application: one scenario, individuals collect several datasets various aspects instruments objects. example, might separately detecting lipids, metabolites, peptides specific soil sample. scenario, data uniform quality, can categorized larger groups exhibit significant differences. cases, pre_process() function serves valuable versatile tool. Yet, function optional generating dashboard. Users can perform processing long format matches required output. However, mindful number samples (timepoints) must greater 5 avoid potential errors subsequent prediction section.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pre-processing datasets — pre_process","text":"","code":"data(test_data) head(test_data, 10) #> ID T1 T2 T3 T4 T5 T6 T7 T8 T9 T10 type #> 1 1 1 0 0 1 1 0 0 1 6 6 type_A #> 2 2 6 0 0 0 0 3 1 0 2 1 type_A #> 3 3 1 0 0 0 2 0 0 2 2 1 type_A #> 4 4 4 5 3 3 7 2 1 1 0 0 type_A #> 5 5 4 3 NA 2 5 5 0 0 0 0 type_A #> 6 6 4 1 0 1 3 1 3 5 11 14 type_A #> 7 7 1 0 0 0 1 3 3 1 1 1 type_A #> 8 8 4 2 1 1 1 1 0 0 0 0 type_A #> 9 9 1 1 1 19 22 1 2 1 1 2 type_A #> 10 10 1 1 3 5 8 2 2 2 5 2 type_A a <- pre_process(test_data) head(a, 10) #> ID T1 T2 T3 T4 T5 T6 #> 1 1 -0.25354628 -0.6761234 -0.67612340 -0.25354628 -0.25354628 -0.6761234 #> 2 2 2.41458180 -0.6678631 -0.66786305 -0.66786305 -0.66786305 0.8733594 #> 3 3 0.21764288 -0.8705715 -0.87057150 -0.87057150 1.30585725 -0.8705715 #> 4 4 0.61658123 1.0569964 0.17616607 0.17616607 1.93782672 -0.2642491 #> 5 5 0.96186009 0.5038315 -0.87025436 0.04580286 1.41988870 1.4198887 #> 6 6 -0.06459959 -0.7105955 -0.92592741 -0.71059546 -0.27993154 -0.7105955 #> 7 7 -0.09086738 -0.9995412 -0.99954118 -0.99954118 -0.09086738 1.7264802 #> 8 8 2.40535118 0.8017837 0.00000000 0.00000000 0.00000000 0.0000000 #> 9 9 -0.50260633 -0.5026063 -0.50260633 1.70395805 2.07171878 -0.5026063 #> 10 10 -0.94019379 -0.9401938 -0.04477113 0.85065153 2.19378551 -0.4924825 #> T7 T8 T9 T10 type #> 1 -0.6761234 -0.25354628 1.85933936 1.85933936 type_A #> 2 -0.1541222 -0.66786305 0.35961857 -0.15412224 type_A #> 3 -0.8705715 1.30585725 1.30585725 0.21764288 type_A #> 4 -0.7046643 -0.70466426 -1.14507943 -1.14507943 type_A #> 5 -0.8702544 -0.87025436 -0.87025436 -0.87025436 type_A #> 6 -0.2799315 0.15073237 1.44272411 2.08871998 type_A #> 7 1.7264802 -0.09086738 -0.09086738 -0.09086738 type_A #> 8 -0.8017837 -0.80178373 -0.80178373 -0.80178373 type_A #> 9 -0.3800194 -0.50260633 -0.50260633 -0.38001942 type_A #> 10 -0.4924825 -0.49248246 0.85065153 -0.49248246 type_A"},{"path":"https://kaiyanm.github.io/MolPad/reference/reshape_for_make_functions.html","id":null,"dir":"Reference","previous_headings":"","what":"Reshape for 'make' functions — reshape_for_make_functions","title":"Reshape for 'make' functions — reshape_for_make_functions","text":"internal function produces three primary datasets dashboard intended \"make\" functions.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/reshape_for_make_functions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reshape for 'make' functions — reshape_for_make_functions","text":"","code":"reshape_for_make_functions(data, cluster, annotation, id_colname, id_type)"},{"path":"https://kaiyanm.github.io/MolPad/reference/reshape_for_make_functions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reshape for 'make' functions — reshape_for_make_functions","text":"data output pre_process() cluster output gClusters() annotation output gPathway() id_colname columns contain database IDs. id_type corresponding database names columns.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/reshape_for_make_functions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reshape for 'make' functions — reshape_for_make_functions","text":"","code":"data(test_data) l <- reshape_for_make_functions(test_data_processed, test_cluster, test_annotations_processed, id_colname = c(\"GO_ID\",\"KEGG_ID\"),id_type = c(\"GO\",\"KEGG\")) head(l[[1]]) #> ID cluster #> 1 1 Group_2 #> 2 2 Group_5 #> 3 3 Group_2 #> 4 4 Group_4 #> 5 5 Group_4 #> 6 6 Group_2 #> GO_ID #> 1 #> 2 #> 3 GO:0003674,GO:0003824,GO:0005575,GO:0006629,GO:0006644,GO:0006650,GO:0006655,GO:0006793,GO:0006796,GO:0008150,GO:0008152,GO:0008610,GO:0008654,GO:0008808,GO:0009058,GO:0009987,GO:0016020,GO:0016740,GO:0016772,GO:0016780,GO:0019637,GO:0030572,GO:0032048,GO:0032049,GO:0044237,GO:0044238,GO:0044249,GO:0044255,GO:0045017,GO:0046471,GO:0046474,GO:0046486,GO:0071704,GO:0090407,GO:1901576 #> 4 GO:0003674,GO:0003676,GO:0003723,GO:0003729,GO:0003824,GO:0004654,GO:0005488,GO:0005575,GO:0005622,GO:0005623,GO:0005737,GO:0006139,GO:0006401,GO:0006402,GO:0006725,GO:0006807,GO:0008150,GO:0008152,GO:0009056,GO:0009057,GO:0009892,GO:0009987,GO:0010468,GO:0010605,GO:0010629,GO:0016070,GO:0016071,GO:0016740,GO:0016772,GO:0016779,GO:0019222,GO:0019439,GO:0034641,GO:0034655,GO:0043170,GO:0044237,GO:0044238,GO:0044248,GO:0044260,GO:0044265,GO:0044270,GO:0044424,GO:0044464,GO:0046483,GO:0046700,GO:0048519,GO:0050789,GO:0060255,GO:0065007,GO:0071704,GO:0090304,GO:0097159,GO:1901360,GO:1901361,GO:1901363,GO:1901575 #> 5 #> 6 #> KEGG_ID Pathway taxonomic.scope #> 1 K07124 Integumentary System hormonal proteins #> 2 Skeletal System structural proteins #> 3 K06131 Muscular System enzymes #> 4 K00962 Nervous System contractile proteins #> 5 K02083 Endocrine System contractile proteins #> 6 Cardiovascular System structural proteins head(l[[2]]) #> # A tibble: 6 × 6 #> ID type cluster day value taxonomic.scope #> #> 1 1 type_A Group_2 T1 -0.254 hormonal proteins #> 2 1 type_A Group_2 T2 -0.676 hormonal proteins #> 3 1 type_A Group_2 T3 -0.676 hormonal proteins #> 4 1 type_A Group_2 T4 -0.254 hormonal proteins #> 5 1 type_A Group_2 T5 -0.254 hormonal proteins #> 6 1 type_A Group_2 T6 -0.676 hormonal proteins head(l[[3]]) #> ID cluster #> 1 1 Group_2 #> 2 2 Group_5 #> 3 3 Group_2 #> 4 4 Group_4 #> 5 5 Group_4 #> 6 6 Group_2 #> GO_ID #> 1 #> 2 #> 3 GO:0003674<\/a>
GO:0003824<\/a>
GO:0005575<\/a>
GO:0006629<\/a>
GO:0006644<\/a>
GO:0006650<\/a>
GO:0006655<\/a>
GO:0006793<\/a>
GO:0006796<\/a>
GO:0008150<\/a>
GO:0008152<\/a>
GO:0008610<\/a>
GO:0008654<\/a>
GO:0008808<\/a>
GO:0009058<\/a>
GO:0009987<\/a>
GO:0016020<\/a>
GO:0016740<\/a>
GO:0016772<\/a>
GO:0016780<\/a>
GO:0019637<\/a>
GO:0030572<\/a>
GO:0032048<\/a>
GO:0032049<\/a>
GO:0044237<\/a>
GO:0044238<\/a>
GO:0044249<\/a>
GO:0044255<\/a>
GO:0045017<\/a>
GO:0046471<\/a>
GO:0046474<\/a>
GO:0046486<\/a>
GO:0071704<\/a>
GO:0090407<\/a>
GO:1901576<\/a> #> 4 GO:0003674<\/a>
GO:0003676<\/a>
GO:0003723<\/a>
GO:0003729<\/a>
GO:0003824<\/a>
GO:0004654<\/a>
GO:0005488<\/a>
GO:0005575<\/a>
GO:0005622<\/a>
GO:0005623<\/a>
GO:0005737<\/a>
GO:0006139<\/a>
GO:0006401<\/a>
GO:0006402<\/a>
GO:0006725<\/a>
GO:0006807<\/a>
GO:0008150<\/a>
GO:0008152<\/a>
GO:0009056<\/a>
GO:0009057<\/a>
GO:0009892<\/a>
GO:0009987<\/a>
GO:0010468<\/a>
GO:0010605<\/a>
GO:0010629<\/a>
GO:0016070<\/a>
GO:0016071<\/a>
GO:0016740<\/a>
GO:0016772<\/a>
GO:0016779<\/a>
GO:0019222<\/a>
GO:0019439<\/a>
GO:0034641<\/a>
GO:0034655<\/a>
GO:0043170<\/a>
GO:0044237<\/a>
GO:0044238<\/a>
GO:0044248<\/a>
GO:0044260<\/a>
GO:0044265<\/a>
GO:0044270<\/a>
GO:0044424<\/a>
GO:0044464<\/a>
GO:0046483<\/a>
GO:0046700<\/a>
GO:0048519<\/a>
GO:0050789<\/a>
GO:0060255<\/a>
GO:0065007<\/a>
GO:0071704<\/a>
GO:0090304<\/a>
GO:0097159<\/a>
GO:1901360<\/a>
GO:1901361<\/a>
GO:1901363<\/a>
GO:1901575<\/a> #> 5 #> 6 #> KEGG_ID #> 1 K07124<\/a> #> 2 #> 3 K06131<\/a> #> 4 K00962<\/a> #> 5 K02083<\/a> #> 6 #> Pathway taxonomic.scope #> 1 Integumentary System hormonal proteins #> 2 Skeletal System structural proteins #> 3 Muscular System enzymes #> 4 Nervous System contractile proteins #> 5 Endocrine System contractile proteins #> 6 Cardiovascular System structural proteins"},{"path":"https://kaiyanm.github.io/MolPad/reference/scale_by_row__.html","id":null,"dir":"Reference","previous_headings":"","what":"Scale by row — scale_by_row__","title":"Scale by row — scale_by_row__","text":"Scales values sample, row independently processed.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/scale_by_row__.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Scale by row — scale_by_row__","text":"","code":"scale_by_row__(data)"},{"path":"https://kaiyanm.github.io/MolPad/reference/scale_by_row__.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Scale by row — scale_by_row__","text":"input expected data frame first column ID following columns containing observations different time points. ID column remains unaltered, columns double (dbl) format scaled.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/scale_by_row__.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Scale by row — scale_by_row__","text":"","code":"data(test_data) scale_by_row__(test_data[1:5,1:10]) #> ID T1 T2 T3 T4 T5 T6 #> 1 1 -0.05847053 -0.5847053 -0.58470535 -0.05847053 -0.05847053 -0.5847053 #> 2 2 2.26366583 -0.6467617 -0.64676167 -0.64676167 -0.64676167 0.8084521 #> 3 3 0.22866478 -0.8003267 -0.80032673 -0.80032673 1.25765629 -0.8003267 #> 4 4 0.50395263 0.9575100 0.05039526 0.05039526 1.86462473 -0.4031621 #> 5 5 0.73869087 0.2841119 NA -0.17046712 1.19326987 1.1932699 #> T7 T8 T9 #> 1 -0.5847053 -0.05847053 2.5727035 #> 2 -0.1616904 -0.64676167 0.3233808 #> 3 -0.8003267 1.25765629 1.2576563 #> 4 -0.8567195 -0.85671947 -1.3102768 #> 5 -1.0796251 -1.07962512 -1.0796251"},{"path":"https://kaiyanm.github.io/MolPad/reference/test_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Test data — test_data","title":"Test data — test_data","text":"synthetically generated dataset created basic testing purposes.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/test_data.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Test data — test_data","text":"two datasets: data annotations","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/test_data.html","id":"data","dir":"Reference","previous_headings":"","what":"data","title":"Test data — test_data","text":"data frame 100 rows 12 variables: ID row ID T1~T10 count value 10 timepoints type type ~D","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/test_data.html","id":"annotations","dir":"Reference","previous_headings":"","what":"annotations","title":"Test data — test_data","text":"data frame 100 rows 5 variables: ID row ID GO_ID go ID KEGG_ID kegg ID system primary lable: 'Integumentary System', 'Skeletal System', 'Muscular System', 'Nervous System', 'Endocrine System', 'Cardiovascular System', 'Lymphatic System', 'Respiratory System', 'Digestive System', 'Urinary System' class secondary label: 'antibodies', 'contractile proteins', 'enzymes', 'hormonal proteins', 'structural proteins', 'storage proteins', 'transport proteins'","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/test_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Test data — test_data","text":"","code":"data(test_data)"},{"path":"https://kaiyanm.github.io/MolPad/reference/transpose_dataframe__.html","id":null,"dir":"Reference","previous_headings":"","what":"Transpose dataframe — transpose_dataframe__","title":"Transpose dataframe — transpose_dataframe__","text":"function transposes provided data frame, using values first column new column names.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/transpose_dataframe__.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transpose dataframe — transpose_dataframe__","text":"","code":"transpose_dataframe__(data)"},{"path":"https://kaiyanm.github.io/MolPad/reference/transpose_dataframe__.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transpose dataframe — transpose_dataframe__","text":"expected input data frame first column serves Time, subsequent columns contain observations various features.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/transpose_dataframe__.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transpose dataframe — transpose_dataframe__","text":"","code":"a <- data.frame(\"Day\"=c(\"Day1\",\"Day2\",\"Day3\"),\"feature_1\" =c(1,2,3),\"feature_2\" =c(0,4,1),\"feature_3\" =c(1,1,0)) a #> Day feature_1 feature_2 feature_3 #> 1 Day1 1 0 1 #> 2 Day2 2 4 1 #> 3 Day3 3 1 0 transpose_dataframe__(a) #> Day1 Day2 Day3 #> 1 1 2 3 #> 2 0 4 1 #> 3 1 1 0"}]
+[{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"CC0 1.0 Universal","title":"CC0 1.0 Universal","text":"CREATIVE COMMONS CORPORATION LAW FIRM PROVIDE LEGAL SERVICES. DISTRIBUTION DOCUMENT CREATE ATTORNEY-CLIENT RELATIONSHIP. CREATIVE COMMONS PROVIDES INFORMATION “-” BASIS. CREATIVE COMMONS MAKES WARRANTIES REGARDING USE DOCUMENT INFORMATION WORKS PROVIDED HEREUNDER, DISCLAIMS LIABILITY DAMAGES RESULTING USE DOCUMENT INFORMATION WORKS PROVIDED HEREUNDER.","code":""},{"path":"https://kaiyanm.github.io/MolPad/LICENSE.html","id":"statement-of-purpose","dir":"","previous_headings":"","what":"Statement of Purpose","title":"CC0 1.0 Universal","text":"laws jurisdictions throughout world automatically confer exclusive Copyright Related Rights (defined ) upon creator subsequent owner(s) (, “owner”) original work authorship /database (, “Work”). 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Affirmer disclaims responsibility clearing rights persons may apply Work use thereof, including without limitation person’s Copyright Related Rights Work. , Affirmer disclaims responsibility obtaining necessary consents, permissions rights required use Work. Affirmer understands acknowledges Creative Commons party document duty obligation respect CC0 use Work.","code":""},{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/articles/FAQ.html","id":"how-can-i-tell-if-my-dataset-is-suitable-for-using-this-dashboard","dir":"Articles","previous_headings":"Common Questions:","what":"How can I tell if my dataset is suitable for using this dashboard?","title":"FAQs","text":"lot longitudinal data least two descriptive columns (like types), match minimal needs starting dashboard. addition, data specific web-ID columns, MolPad make convenient createing links databases automatically.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/FAQ.html","id":"how-can-i-customize-my-dashboard","dir":"Articles","previous_headings":"Common Questions:","what":"How can I customize my dashboard?","title":"FAQs","text":"can edit title within gDashboard() function. additional personalization, adjusting colors sizes figures, can fork repository, create copy, make modifications dashboard.css file.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/FAQ.html","id":"what-should-i-do-if-i-only-have-a-few-time-points5","dir":"Articles","previous_headings":"Common Questions:","what":"What should I do if I only have a few time points(<5)?","title":"FAQs","text":"Firstly, strongly recommend extending length longitudinal samples. can achieve increasing number measurements two days incorporating simulated data. options don’t suit needs, can also consider using “linear” method gNetwork() function.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Case Study: Cheese Communities","text":"vignette provides comprehensive guide using MolPad case study, including data pre-processing, network generation, result analysis.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"washed-rind-cheese-microbial-communities","dir":"Articles","previous_headings":"","what":"Washed-Rind Cheese Microbial Communities","title":"Case Study: Cheese Communities","text":"Cheese making ancient craft involves coagulation milk proteins form curds, separated liquid whey. curds processed, shaped, aged develop desired texture flavor. Various factors, type milk, specific cultures bacteria, aging conditions, contribute unique characteristics cheese. One crucial step process aging practice, regular washing brine solution plays significant role. process producing cheese, regular washing brine solution aging practice can homogenize microbial communities cheese’ surface facilitate intermicrobial interactions. following parts, analyze longitudinal data set three washed-rind cheese communities collected cheese ripening.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"data","dir":"Articles","previous_headings":"","what":"Data","title":"Case Study: Cheese Communities","text":"analysis, use data set contained study Smith et al. (2022) microbial communities cheese. original research investigated successional dynamics occur within cheese rind microbial communities using combination 16S rRNA amplicon, Illumina, PacBio sequencing. functionally taxonomically annotate (using eggNOG (21) MMseqs2 (22)) contigs generated Illumina reads, demonstrate utility MolPad using single-omic. Specifically, focus Cheese Sample Cheese Sample C. detailed information attached data, please check documentation.","code":"data(\"cheese\") str(cheese) #> tibble [106,239 × 18] (S3: tbl_df/tbl/data.frame) #> $ ID : chr [1:106239] \"1\" \"2\" \"3\" \"4\" ... #> $ A_1 : int [1:106239] 38 23 24 3 58 12 1 14 1 3 ... #> $ A_2 : int [1:106239] 23 6 5 2 14 9 1 7 1 1 ... #> $ A_3 : int [1:106239] 27 4 37 4 45 14 0 14 3 5 ... #> $ A_4 : int [1:106239] 5 0 10 2 13 4 1 4 4 0 ... #> $ A_5 : int [1:106239] 11 9 19 16 32 13 0 4 1 1 ... #> $ C_1 : int [1:106239] 13 21 3 56 82 2 4 17 7 2 ... #> $ C_3 : int [1:106239] 1 1 0 1 3 0 0 0 0 1 ... #> $ C_4 : int [1:106239] 3 0 1 7 8 1 0 1 2 2 ... #> $ C_5 : int [1:106239] 0 2 4 17 3 4 0 1 8 6 ... #> $ GO_ID : chr [1:106239] NA NA NA NA ... #> $ KEGG_ID: chr [1:106239] NA NA NA NA ... #> $ domain : chr [1:106239] NA NA NA NA ... #> $ phylum : chr [1:106239] NA NA NA NA ... #> $ class : chr [1:106239] NA NA NA NA ... #> $ order : chr [1:106239] NA NA NA NA ... #> $ family : chr [1:106239] NA NA NA NA ... #> $ genus : chr [1:106239] NA NA NA NA ... str(annotations) #> tibble [86,156 × 9] (S3: tbl_df/tbl/data.frame) #> $ ID : chr [1:86156] \"9\" \"10\" \"11\" \"12\" ... #> $ GO_ID : chr [1:86156] \"-\" \"-\" \"-\" \"-\" ... #> $ KEGG_ID: chr [1:86156] \"-\" \"-\" \"-\" \"-\" ... #> $ domain : chr [1:86156] \"Bacteria\" \"Bacteria\" \"Bacteria\" \"Bacteria\" ... #> $ phylum : chr [1:86156] \"Pseudomonadota\" \"Pseudomonadota\" \"Pseudomonadota\" \"Pseudomonadota\" ... #> $ class : chr [1:86156] \"Alphaproteobacteria\" \"Alphaproteobacteria\" \"Alphaproteobacteria\" \"Alphaproteobacteria\" ... #> $ order : chr [1:86156] \"Caulobacterales\" \"Caulobacterales\" \"Hyphomicrobiales\" \"Hyphomicrobiales\" ... #> $ family : chr [1:86156] \"Caulobacteraceae\" \"Caulobacteraceae\" \"Bartonellaceae\" \"Brucellaceae;-_Brucella/Ochrobactrum group\" ... #> $ genus : chr [1:86156] \"Caulobacter\" \"Caulobacter;-_unclassified Caulobacter\" \"Bartonella\" \"Brucella\" ..."},{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"data-and-annotations","dir":"Articles","previous_headings":"Data > Pre-process","what":"Data and Annotations","title":"Case Study: Cheese Communities","text":"select ‘type’ column phylum describe characteristic cheese data. Also, columns phylum class taken tags elemental composition. section, introduce data preparing steps analysis. annotations dataset contains various columns describe characteristics properties samples. First, select ‘type’ column phylum provide broad categorization microbial communities present cheese surface. categorization helps understanding overall composition diversity microbes high taxonomic level. run pre_process() function clean standardize data. annotate dataset, also use columns phylum class tags elemental composition microbial communities. phylum column represents major taxonomic rank, giving us broad overview microbial distribution. class column provides detailed information, allowing us delve deeper specific types microbes present. pre-processing, two datasets put dashboard look like:","code":"cheesedata <- cheese |> select(ID, A_1:C_5, phylum) |> rename(type=phylum) |> pre_process() pathchee <- gAnnotation(annotations,\"phylum\",\"class\") # data cheesedata[112:115,] #> # A tibble: 4 × 11 #> ID A_1 A_2 A_3 A_4 A_5 C_1 C_3 C_4 C_5 type #> #> 1 112 0.943 -0.471 -0.471 -0.471 -0.471 2.36 -0.471 -0.471 -0.471 Other #> 2 113 0.786 -0.124 0.126 -0.623 -0.637 2.33 -0.667 -0.593 -0.593 Other #> 3 114 1.22 -1.09 -0.430 -0.829 -0.170 1.43 -1.30 0.455 0.715 Other #> 4 115 2.67 -0.333 -0.333 -0.333 -0.333 -0.333 -0.333 -0.333 -0.333 Other # annotation pathchee[112:115,] #> # A tibble: 4 × 9 #> ID GO_ID KEGG_ID domain Pathway taxonomic.scope order family genus #> #> 1 145 - ko:K00004 Bacte… Pseudo… Unknown NA NA NA #> 2 147 - ko:K00004 Bacte… Actino… Actinomycetes NA NA NA #> 3 148 - ko:K00004 Bacte… Bacill… Bacilli Baci… Bacil… Lysi… #> 4 149 - ko:K00004,ko:K0… Bacte… Actino… Actinomycetes NA NA NA"},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"cluster-input","dir":"Articles","previous_headings":"Data","what":"Cluster Input","title":"Case Study: Cheese Communities","text":"section, generate clusters first dataset using gClusters function. function takes cheese dataset (cheesedata) input generates clusters based specified parameters. , set number clusters 10 (ncluster = 10) specify maximum number clusters consider determining optimal number clusters (elbow.max=15).","code":"cluschee <- gClusters(cheesedata,ncluster = 10,elbow.max=15)"},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"network-input","dir":"Articles","previous_headings":"Data","what":"network input","title":"Case Study: Cheese Communities","text":"generating clusters major patterns, proceed obtain network results clusters. Taking cluster centroids nodes, prediction process edges divided individual regression tasks, cluster centroid independentally predicted expression cluster centroids, using random forests. pick top 3 related predictors cluster centroid save network output future use. achieved using gNetwork() function. gain insight network results, can visualize details using gNetwork_view() function, shown .","code":"networkchee <- gNetwork(cluschee,ntop = 3) gNetwork_view(networkchee)"},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"run-dashboard","dir":"Articles","previous_headings":"","what":"Run Dashboard","title":"Case Study: Cheese Communities","text":"clusters network results obtained, can proceed run dashboard. involves declaring annotations executing dashboard using gDashboard() function. , pass cheese dataset (cheesedata), cluster results (cluschee), network results (networkchee), specify column names types annotation identifiers.","code":"gDashboard(cheesedata, cluschee, pathchee, networkchee, id_colname = c(\"GO_ID\",\"KEGG_ID\"), id_type = c(\"GO\",\"KEGG\"))"},{"path":"https://kaiyanm.github.io/MolPad/articles/cheese.html","id":"results","dir":"Articles","previous_headings":"","what":"Results","title":"Case Study: Cheese Communities","text":"example, filter Actinomycetota (Actinobacteria) functional group, see edges connecting group 10 group 3- clusters features associated Actinomycetoa Cheese sample (Fig 1.3.). Fig 1: Dashboard showing Actinomycetota filtered network () enrichment pattern Cheese Sample-(B) Cheese Sample-C (C); Cluster pattern Group 9, also enriched Type IV secretion genes (D). Looking pattern traces groups, (Fig 1.3.B), peak samples A4 (week 9) A5 (week 13), mirrors 16S rRNA results Saak et al. Since two clusters edges connecting groups, suggests Actinomycetoa features found groups follow distinct longitudinal succession patterns independent. looking Actinomycetoa within Cheese Sample C see different pattern. Groups 2 5, features associated Actinomycetoa, highly connected groups (Fig 1.3.). results, can hypothesize though Actinomycetoa features abundant later time points cheese samples, dynamics differentially influenced. authors found Type VI secretion enriched Pseudomonadota bacteria (specifically, Psychrobacter), hypothesized enrichment due importance physical species interactions occur habitat. Using MolPad, searched secretion systems associated genes, understand dynamics within community. Focusing KEGG annotated Type IV secretion genes, found Group 9 contained 12/13 genes. Within group, features cluster ones peak Cheese sample C5 (week 13, Fig 1.3.D). also taxonomically diverse sample. , can hypothesize increased taxonomic diversity also associated increases genes related competitive species interactions.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"installation","dir":"Articles","previous_headings":"","what":"Installation","title":"Get started","text":"can either install MolPad devtools, clone repository local computer (example, onto ./Github) installing: , load package:","code":"# Install the package in R: install.packages(\"devtools\") library(devtools) install_github(\"KaiyanM/MolPad\") # Download through Github and install the package in R: setwd(\"./GitHub\") install(\"MolPad\") library(MolPad)"},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"before-input","dir":"Articles","previous_headings":"","what":"Before Input","title":"Get started","text":"recommend carefully reviewing data considering applying normalization (standardization, quantile normalization, log transformation, …) imputation (KNN, PMM, Mean/Median/Mode, …) address issues related library size missing data beforehead, otherwise pre_processing() function default converting NAs 0s.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"tips-for-normalization-and-imputation","dir":"Articles","previous_headings":"Before Input","what":"Tips for normalization and imputation","title":"Get started","text":"choose normalization method, first consider data distribution. Use Z-score normalization data normally distributed, log transformation robust scaling ’s skewed. analysis requires data within specific range, like [0, 1], opt Min-Max normalization. datasets outliers, robust scaling rank normalization better choices. selecting imputation method, assess amount pattern missing data. small amount missing data, simple methods like mean, median, mode imputation effective. missing data related variables, consider sophisticated approaches like Predictive Mean Matching k-NN imputation. computational resources concern, simpler imputation methods might practical, resources allow, advanced methods can improve data quality. ’s hard decide, can always experiment different methods use cross-validation compare effectiveness. Let choice guided domain knowledge specific requirements analysis increase quality following steps.","code":""},{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"multi-omics-yes-or-no","dir":"Articles","previous_headings":"Data Format","what":"Multi-omics: Yes or No","title":"Get started","text":"visualization pipeline starts pre-processing module includes built-functions reshape data inputs standard format streamline data preparation. Depending nature datasets, two primary conditions consider: research object includes multi-omics measurements, assume provide list dataframes, collected different omics type. example, might datasets peptides, metabolites, lipids. combine together standard long dataset row represents feature, simply run pre_process() function, conduct data preparation yield standard input format demonstrated . Datasets multi-omics, can still utilize dashboard long ensuring data inputs reformatted standard longitudinal format. situation, can manually assign type column category label describe major groups data, input data pre_processing() module data preparation . case study, utilized “Kingdom” type label column cheese data. datasets multi-omics, can simply apply dashboard reformatting data standard longitudinal format. case, manually assign category label type column represent major groups. can still input data pre_processing() module cleaning preparation. case study, used “Kingdom” type label cheese datasets.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"data-format-and-pre-process","dir":"Articles","previous_headings":"Data Format","what":"Data Format and Pre-process","title":"Get started","text":"figure, , B, C can represent datasets different sources different aspects measurements. two examples input data might look like transform required format pre_process() modules.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"if-your-input-is-a-list-of-datasets","dir":"Articles","previous_headings":"Data Format > Data Format and Pre-process","what":"If your input is a list of datasets:","title":"Get started","text":"list datasets, following format: ID column identical column names. pre_process():","code":"str(fungal_dlist) #> List of 3 #> $ :'data.frame': 149786 obs. of 12 variables: #> ..$ ID : chr [1:149786] \"grp1_1\" \"grp1_2\" \"grp1_6\" \"grp1_7\" ... #> ..$ D0_01 : num [1:149786] -0.33 -0.33 1.591 1.106 0.659 ... #> ..$ D1_02 : num [1:149786] 0.628 0.628 3.087 3.618 2.843 ... #> ..$ D2_03 : num [1:149786] -1.223 -1.223 0.846 1.178 1.435 ... #> ..$ D3_04 : num [1:149786] -2.411 -1.808 1.239 1.753 0.258 ... #> ..$ D4_05 : num [1:149786] -1.41 -1.18 2.05 1.64 1.21 ... #> ..$ D5_06 : num [1:149786] -2.245 -2.309 0.83 1.64 0.395 ... #> ..$ D6_07 : num [1:149786] -0.784 -1.125 2.758 2.888 2.516 ... #> ..$ D7_08 : num [1:149786] -2.61197 -2.68724 0.42236 0.92846 0.00892 ... #> ..$ D8_09 : num [1:149786] -0.951 -1.3 2.417 2.454 1.418 ... #> ..$ D9_10 : num [1:149786] -1.91 -2.61 1.33 1.8 2.25 ... #> ..$ D10_11: num [1:149786] -2.871 -3.417 1.101 0.619 1.275 ... #> $ :'data.frame': 150 obs. of 12 variables: #> ..$ ID : chr [1:150] \"grp2_1\" \"grp2_2\" \"grp2_3\" \"grp2_4\" ... #> ..$ D0_01 : num [1:150] 0.947 1.012 0.156 0.31 0.138 ... #> ..$ D1_02 : num [1:150] -1.616 -0.917 -1.581 -2.717 -2.578 ... #> ..$ D2_03 : num [1:150] -2.4 -1.67 -2.31 -3.02 -3.66 ... #> ..$ D3_04 : num [1:150] -0.5734 0.0336 -0.351 -1.3052 -1.737 ... #> ..$ D4_05 : num [1:150] 0.662 0.854 1.002 0.12 -0.371 ... #> ..$ D5_06 : num [1:150] -1.09 -1.28 -1.04 -1.82 -3.15 ... #> ..$ D6_07 : num [1:150] -0.212 0.718 0.189 -0.627 -1.081 ... #> ..$ D7_08 : num [1:150] 0.969 2.117 1.462 0.339 -0.038 ... #> ..$ D8_09 : num [1:150] -0.712 0.43 -0.281 -1.297 -1.822 ... #> ..$ D9_10 : num [1:150] -0.649 0.45 -0.124 -1.77 -1.962 ... #> ..$ D10_11: num [1:150] -1.05 -0.85 -1.58 -1.91 -4.05 ... #> $ :'data.frame': 369 obs. of 12 variables: #> ..$ ID : chr [1:369] \"grp3_1\" \"grp3_2\" \"grp3_3\" \"grp3_4\" ... #> ..$ D0_01 : num [1:369] -0.918 -2.112 -1.372 -0.4 -1.75 ... #> ..$ D1_02 : num [1:369] -1.219 -3.502 -1.504 -0.615 -2.11 ... #> ..$ D2_03 : num [1:369] -0.542 -2.324 -0.592 0.161 -1.342 ... #> ..$ D3_04 : num [1:369] -0.101 -1.495 0.1 0.603 -1.058 ... #> ..$ D4_05 : num [1:369] 1.715 1.557 1.89 2.296 0.653 ... #> ..$ D5_06 : num [1:369] -1.452 -2.37 -1.182 -0.846 -2.531 ... #> ..$ D6_07 : num [1:369] 0.0879 -0.9982 0.4382 0.6215 -0.9464 ... #> ..$ D7_08 : num [1:369] 0.0502 0.4668 0.5378 0.8353 -0.9213 ... #> ..$ D8_09 : num [1:369] -0.0268 -0.4226 0.4528 0.6328 -1.0998 ... #> ..$ D9_10 : num [1:369] -0.5807 -1.7755 -0.1956 0.0866 -1.5876 ... #> ..$ D10_11: num [1:369] 0.118 -0.16 0.687 0.572 -1.037 ... #> ID D0_01 D1_02 D2_03 D3_04 D4_05 D5_06 #> 1 grp1_1 -0.3297741 0.6284682 -1.2232968 -2.411339 -1.413062 -2.2445487 #> 2 grp1_2 -0.3297741 0.6284682 -1.2232968 -1.807533 -1.183498 -2.3088022 #> 3 grp1_6 1.5908054 3.0871284 0.8460835 1.239411 2.054109 0.8300876 #> 4 grp1_7 1.1061795 3.6176250 1.1781012 1.753078 1.643661 1.6400102 #> D6_07 D7_08 D8_09 D9_10 D10_11 #> 1 -0.7839412 -2.6119663 -0.9511467 -1.914273 -2.8711810 #> 2 -1.1251434 -2.6872417 -1.2996614 -2.610647 -3.4165861 #> 3 2.7576288 0.4223618 2.4165602 1.333387 1.1012825 #> 4 2.8877343 0.9284640 2.4538561 1.799019 0.6187648 #> ID D0_01 D1_02 D2_03 D3_04 D4_05 D5_06 #> 1 grp2_1 0.9467431 -1.6162900 -2.400394 -0.57337845 0.6616711 -1.090914 #> 2 grp2_2 1.0120048 -0.9165994 -1.674330 0.03361378 0.8540950 -1.283964 #> 3 grp2_3 0.1561394 -1.5805490 -2.307281 -0.35095093 1.0018741 -1.035030 #> 4 grp2_4 0.3104578 -2.7173471 -3.022886 -1.30521496 0.1198075 -1.816677 #> D6_07 D7_08 D8_09 D9_10 D10_11 #> 1 -0.2116778 0.9688582 -0.7123773 -0.6489204 -1.0504845 #> 2 0.7181970 2.1174460 0.4302981 0.4504066 -0.8503481 #> 3 0.1885422 1.4619677 -0.2805926 -0.1235090 -1.5757556 #> 4 -0.6271954 0.3393109 -1.2967938 -1.7697248 -1.9073101 #> ID D0_01 D1_02 D2_03 D3_04 D4_05 D5_06 #> 1 grp3_1 -0.9178218 -1.2193128 -0.5415780 -0.1007493 1.715495 -1.4515238 #> 2 grp3_2 -2.1120636 -3.5015326 -2.3236475 -1.4949366 1.557463 -2.3698578 #> 3 grp3_3 -1.3724446 -1.5043101 -0.5922380 0.1004978 1.889573 -1.1815469 #> 4 grp3_4 -0.4002445 -0.6145252 0.1613003 0.6031744 2.295751 -0.8464012 #> D6_07 D7_08 D8_09 D9_10 D10_11 #> 1 0.08792489 0.05020749 -0.02681783 -0.58065562 0.1177374 #> 2 -0.99822332 0.46681389 -0.42255125 -1.77554842 -0.1603138 #> 3 0.43822354 0.53780990 0.45276561 -0.19558682 0.6870272 #> 4 0.62145080 0.83528878 0.63275758 0.08663959 0.5720591 data <- pre_process(fungal_dlist, typenameList = c('DataSource_A','DataSource_B','DataSource_C'), replaceNA = TRUE, scale = TRUE, autoColName = \"Section_\") #> [1] \"Reformat a list of datasets:\" head(data,10) #> ID D0_01 D1_02 D2_03 D3_04 D4_05 D5_06 #> 1 grp1_1 1.06146675 1.956656 0.2267383 -0.88312967 0.04945980 -0.7273146 #> 2 grp1_2 1.08465667 1.916983 0.3085454 -0.19892034 0.34311497 -0.6343214 #> 3 grp1_6 -0.01899274 1.717871 -0.8834318 -0.42687491 0.51878884 -0.9019992 #> 4 grp1_7 -0.75869657 2.051470 -0.6782202 -0.03485388 -0.15728499 -0.1613700 #> 5 grp1_8 -0.68132827 1.648062 0.1465161 -1.10875822 -0.09101615 -0.9629427 #> 6 grp1_11 0.27709905 1.981042 -0.5786717 0.02888008 0.54647685 -0.7013406 #> 7 grp1_12 0.90487968 2.180079 -0.6439891 -0.14841544 -0.69040101 -0.3834938 #> 8 grp1_13 0.46335031 1.695812 -1.0938459 -0.76868293 0.74415950 -0.6861036 #> 9 grp1_14 0.72014702 1.708604 -0.9344900 -0.35796963 0.63730277 -0.9872778 #> 10 grp1_15 0.54526798 1.784716 -0.8846753 -0.49538649 0.62916563 -0.9742293 #> D6_07 D7_08 D8_09 D9_10 D10_11 type #> 1 0.6371841 -1.0705560 0.4809808 -0.41877137 -1.31271417 DataSource_A #> 2 0.3938012 -0.9630327 0.2422154 -0.89650312 -1.59653914 DataSource_A #> 3 1.3354026 -1.3752687 0.9395058 -0.31779210 -0.58720856 DataSource_A #> 4 1.2347631 -0.9575502 0.7492778 0.01655139 -1.30408627 DataSource_A #> 5 1.2995756 -1.3741657 0.1285139 1.01932257 -0.02377948 DataSource_A #> 6 1.1406352 -1.6339536 0.2041455 -0.45793129 -0.80638109 DataSource_A #> 7 0.4794959 -1.6340692 0.5360766 -0.31430867 -0.28585374 DataSource_A #> 8 1.2235141 -1.2241931 0.6790383 -0.28487806 -0.74817082 DataSource_A #> 9 1.1466328 -1.4265286 0.5357111 -0.51799361 -0.52413804 DataSource_A #> 10 1.1813273 -1.2944121 0.6417621 -0.46906150 -0.66447461 DataSource_A"},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"if-your-input-is-a-single-dataset","dir":"Articles","previous_headings":"Data Format > Data Format and Pre-process","what":"If your input is a single dataset:","title":"Get started","text":"dataset already includes labeled ‘type’ column: pre_process():","code":"#> ID Day_1 Day_2 Day_3 Day_4 Day_5 Day_6 #> 1 1 NA NA -0.82919412 0.08237778 1.30438699 0.4875914 #> 2 2 NA NA -0.41606256 1.42523475 -0.44792066 1.1425154 #> 3 3 1.0354708 NA 0.78841026 -1.49585251 -0.41361867 -0.3271995 #> 4 4 -0.3005323 0.6998150 -1.86018783 1.14167433 -1.04204735 -2.4930409 #> 5 5 -0.1307903 0.7839983 0.25834857 0.62421147 -0.07202124 -0.9214203 #> 6 6 -0.9910321 1.8059465 -1.25395397 -0.45260649 -0.57303842 0.9990113 #> 7 7 -0.9160734 1.4475673 -3.10452531 -0.06560360 -1.27374744 -0.5703149 #> 8 8 -0.7381729 -0.4844302 1.10281675 0.12049188 0.20892638 -0.3115689 #> 9 9 -0.2581716 -0.5906591 0.09794986 2.44249606 1.13135657 0.4886958 #> 10 10 1.8831483 -1.2490571 0.55981559 -2.31731640 0.97323395 -0.7994677 #> Day_7 Day_8 type #> 1 -1.4632070 0.05832042 peptide #> 2 -0.4292379 -0.79047610 peptide #> 3 -0.3508094 -0.34995898 peptide #> 4 -0.3011731 0.67869535 peptide #> 5 1.1761504 -0.86303628 peptide #> 6 -0.8993647 -0.44801227 peptide #> 7 1.1593812 -0.90149427 peptide #> 8 0.5463595 -1.51687039 lipid #> 9 -0.5016034 -0.08241911 lipid #> 10 -0.9442708 NA metabolite x1 <- pre_process(x, replaceNA = TRUE,scale = TRUE) head(x1,10) #> ID Day_1 Day_2 Day_3 Day_4 Day_5 Day_6 #> 1 1 0.05451890 0.05451890 -0.9508450 0.1543986 1.6360347 0.64570353 #> 2 2 -0.07554744 -0.07554744 -0.5950354 1.7039742 -0.6348129 1.35097605 #> 3 3 1.49874204 0.17759695 1.1835204 -1.7309438 -0.3501343 -0.23987305 #> 4 4 0.10376561 0.87801613 -1.1033792 1.2200072 -0.4701534 -1.59319589 #> 5 5 -0.31530188 0.89803359 0.2008348 0.6860993 -0.2373532 -1.36395879 #> 6 6 -0.71746086 1.90776216 -0.9642373 -0.2120986 -0.3251351 1.15037885 #> 7 7 -0.27002806 1.37506283 -1.7931878 0.3218978 -0.5189688 -0.02938062 #> 8 8 -0.74469269 -0.43190500 1.5246885 0.3137802 0.4227931 -0.21881953 #> 9 9 -0.58919072 -0.91616395 -0.2389755 2.0666865 0.7772922 0.14528989 #> 10 10 1.56649401 -0.74805373 0.5886154 -1.5374455 0.8941115 -0.41582898 #> Day_7 Day_8 type #> 1 -1.7195596 0.1252300 peptide #> 2 -0.6114859 -1.0625212 peptide #> 3 -0.2699967 -0.2689116 peptide #> 4 0.1032697 0.8616699 peptide #> 5 1.4181669 -1.2865208 peptide #> 6 -0.6314225 -0.2077865 peptide #> 7 1.1744856 -0.2598810 peptide #> 8 0.8387456 -1.7045902 lipid #> 9 -0.8285852 -0.4163531 lipid #> 10 -0.5228315 0.1749388 metabolite"},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"choose-your-annotation","dir":"Articles","previous_headings":"","what":"Choose Your Annotation","title":"Get started","text":"addition specifying data type mentioned , methods support three levels information: functional annotation, taxonomy annotation, feature annotation. annotations matched ID columns annotation data, serving another crucial input generating dashboard. facilitate automatic feature link generation using KeggID GOID, users set corresponding column names beforehand. example annotation formats align data inputs. data two layers labels (like cheese dataset), can specify two layers want focus network using gAnnotation() function.","code":"#> ID taxonomic.scope ko_term Pathway #> 1 grp1_24 Bacteria K02706 00195_Photosynthesis #> 2 grp1_25 Bacteria K02706 00195_Photosynthesis #> 3 grp1_26 Bacteria K02706 00195_Photosynthesis #> 4 grp1_1075 Bacteria K02112 00195_Photosynthesis #> 5 grp1_1076 Bacteria K02112 00195_Photosynthesis #> 6 grp1_1358 Bacteria K02112 00195_Photosynthesis Annotation_path_taxon <- gAnnotation(annotations,\"phylum\",\"class\")"},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"network-generation","dir":"Articles","previous_headings":"","what":"Network Generation","title":"Get started","text":"generate network, first scale cluster trajectories across input molecular features. Trajectories clustered using K-means, built-elbow method used select optimal K. build network clustered trajectories adapting GENIE3 algorithm. Specifically, network learned series regressions, cluster centroid predicted expression patterns cluster centroids using random forests. choose random forests potential model interacting features non-linearity without strong assumptions. determine feature clusters predictive given trajectory, Mean Decrease Accuracy subset top predictors whose expression directly influences expression target cluster taken indication putative link.","code":"cluster_result <- data |> gClusters(ncluster = 5,elbow.max=15) network_result <- cluster_result |> gNetwork(ntop = 3) gNetwork_view(network_result) #> Warning: Removed 1 rows containing missing values (`geom_segment()`). #> Warning: Removed 1 rows containing missing values (`geom_point()`)."},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"dashboard-navigation","dir":"Articles","previous_headings":"","what":"Dashboard Navigation","title":"Get started","text":"workflow inputs related gDashboard function. key function processes data runs dashboard, requiring input previous steps. ’ve launched Shiny dashboard, can zoom make adjustments explore interesting findings within data. effectively navigate dashboard generated MolPad, ’ll follow three main steps:","code":"gDashboard(data, cluster_result, Annotation_path_taxon, network_result, id_colname = \"ko_term\" , id_type = \"KEGG\")"},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"choose-a-primary-functional-annotation-and-adjust-edge-density","dir":"Articles","previous_headings":"Dashboard Navigation","what":"1. Choose a Primary Functional Annotation and Adjust Edge Density","title":"Get started","text":"Start selecting primary functional annotation available options. , fine-tune edge density adjusting threshold value importance score. Nodes turn bright green indicate clusters containing features related chosen functional annotation.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"explore-the-network","dir":"Articles","previous_headings":"Dashboard Navigation","what":"2. Explore the Network","title":"Get started","text":"Brushing network unveils patterns taxonomic composition typical trajectories. can also zoom specific taxonomic annotations applying filters.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"investigate-feature-details-and-related-function-annotations","dir":"Articles","previous_headings":"Dashboard Navigation","what":"3. Investigate Feature Details and Related Function Annotations","title":"Get started","text":"Delve feature table examine specifics features within selected clusters. Explore additional related function annotations using drop-options. Click provided links access online information items interest. interface encourages iterative exploration, enabling conduct multiple steps answer specific questions, comparing pattern distributions two functions identifying functionally important community members metabolizing feature interest.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"related-visualization-outputs","dir":"Articles","previous_headings":"","what":"Related Visualization Outputs","title":"Get started","text":"following visualizations integrated dashboard, ’s need run function plot individually. ’ve linked stacked bar plot ribbon plot network facilitate focus context explorations. setup helps detecting similar longitudinal patterns understanding relationships.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"network-plot","dir":"Articles","previous_headings":"Related Visualization Outputs","what":"Network Plot","title":"Get started","text":"network plot powerful visualization tool displays relationships different groups features within data. MolPad, network plot helps identify clusters features share similar patterns, revealing underlying connections might immediately obvious. visualizing connections, users can gain clearer understanding structure within data, making easier pinpoint significant associations trends.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"stack-bar-plot","dir":"Articles","previous_headings":"Related Visualization Outputs","what":"Stack Bar Plot","title":"Get started","text":"stacked bar plot provides detailed view composition cluster group dataset. stacking different categories top within single bar, plot allows quick comparison relative proportions across multiple groups. particularly useful microbiome experiments understanding distribution taxa across different conditions time points crucial. stacked bar plot makes easy see distributions change experimental conditions, facilitating deeper insights.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/getstarted.html","id":"ribbon-plot","dir":"Articles","previous_headings":"Related Visualization Outputs","what":"Ribbon Plot","title":"Get started","text":"ribbon plot designed visualize changes time, making ideal tool tracking longitudinal data. MolPad, ribbon plot illustrates abundance expression levels features vary across different time points conditions. smooth, flowing design ribbons helps emphasize trends patterns, enabling users quickly identify periods significant change stability. plot particularly advantageous comparing multiple groups, clearly shows overlapping trends divergences, providing comprehensive view temporal dynamics data.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/whymolpad.html","id":"why-molpad","dir":"Articles","previous_headings":"","what":"Why MolPad?","title":"Why MolPad?","text":"increasing multi-omics data longitudinal designs integrated microbiome experiments, growing need present network, especially complex variations across biological modalities. Network perspective helps detect underlying co-occurrence among microbiome samples, allowing high-level insights global structure. Yet comes experimental data records time series 100,000 features, network collapse entangled clumps therefore unable read.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/whymolpad.html","id":"improvements","dir":"Articles","previous_headings":"","what":"Improvements","title":"Why MolPad?","text":"aim network interpretation, MolPad shows improvements 3 important aspects: Capture groups features share longitudinal patterns functional properties within network. Annotate network complex experimental datasets, way dig specific components notable features. Streamlines exploration modularized functions interactive dashboard. package especially facilitates exploration longitudinal molecular co-expression microbiome experiments using focus-plus-context approach, empowering researchers uncover global patterns insights longitudinal microbiomics data. Unlike existing tools, MolPad stands rich network interactivity support contextualization. also equipped beginner-friendly interface self-contained data processing functions, making accessible users limited programming experience.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/whymolpad.html","id":"key-features","dir":"Articles","previous_headings":"","what":"Key Features","title":"Why MolPad?","text":"Trajectory clustering organizing data clusters, helps identify patterns, relationships, underlying structures within dataset. essence, clustering detects groups features observations behave similarly, allowing deeper insights effective decision-making. Network extracting build network clustered trajectories adapting GENIE3 algorithm. Specifically, network learned series regressions, cluster centroid predicted expression patterns cluster centroids using random forests. choose random forests potential model interacting features non-linearity without strong assumptions. Interactive Dashboard package provides interactive dashboard designed help users explore data uncover relationships within complex networks. utilizing focus-plus-context visualization, dashboard allows users zoom specific details keeping broader dataset view. approach enables deeper understanding data, making easier identify patterns make informed decisions.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/whymolpad.html","id":"what-does-it-look-like","dir":"Articles","previous_headings":"","what":"What does it look like?","title":"Why MolPad?","text":"Now, let’s see can get dashboard. ’ll start overview demonstrate discover patterns within data.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/whymolpad.html","id":"molpad-dashboard-overview","dir":"Articles","previous_headings":"What does it look like?","what":"MolPad Dashboard Overview","title":"Why MolPad?","text":"overview MolPad Dashboard. explore dashboard effectively, can start following sequence -B-C-D. approach guide cluster-level network, taxonomic-level bar plot, type-level line plot, feature-level table. , can modify detailed settings identify investigate special patterns within data.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/whymolpad.html","id":"discovering-related-patterns","dir":"Articles","previous_headings":"What does it look like?","what":"Discovering related patterns","title":"Why MolPad?","text":"short example discovering related patterns using network plot: shade edges represents proximity nodes. brushed area, Groups 1-7-8 (circled solid black lines) Groups 1-2 (circled blue dashed lines) strongly linked. Groups 1, 7, 8, patterns form W-shape noticeable peak time section. Although Group 1 shows higher volatility compared Group 2, groups display highly overlapping increasing trend. detailed instructions, please visit “Get Started” page relevant function reference page.","code":""},{"path":"https://kaiyanm.github.io/MolPad/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Kaiyan Ma. Author, maintainer.","code":""},{"path":"https://kaiyanm.github.io/MolPad/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Ma K (2024). MolPad: MolPad: R-Shiny Package Cluster Co-Expression Analysis Longitudinal Microbiomics. R package version 0.1.0, https://kaiyanm.github.io/MolPad/.","code":"@Manual{, title = {MolPad: MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics}, author = {Kaiyan Ma}, year = {2024}, note = {R package version 0.1.0}, url = {https://kaiyanm.github.io/MolPad/}, }"},{"path":"https://kaiyanm.github.io/MolPad/index.html","id":"molpad-","dir":"","previous_headings":"","what":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","title":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","text":"R-Shiny Package Cluster Co-Expression Analysis Longitudinal Microbiomics","code":""},{"path":"https://kaiyanm.github.io/MolPad/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","text":"MolPad offers visualization dashboard tool designed enhance understanding molecular co-expression works context microbiome data. approach involves using cluster network provide initial overview relationships across multiple omics, added functionality interactively zoom specific areas interest. facilitate analysis, ’ve developed focus-plus-context strategy connects online curated annotations. Additionally, package simplifies entire pipeline creating dashboard. user-friendly design makes accessible even people limited R programming experience. Check cheese-data demo .","code":""},{"path":"https://kaiyanm.github.io/MolPad/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","text":"can either install devtools, {r, eval = FALSE} # Install package R: install.packages(\"devtools\") library(devtools) install_github(\"KaiyanM/MolPad\") clone repository local computer (example, onto ./Github) installing: {r, eval = FALSE} # Download install package R: setwd(\"./GitHub\") install(\"MolPad\") , load package: {r,eval=FALSE} library(MolPad)","code":""},{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/index.html","id":"molpad-could-help-you-with","dir":"","previous_headings":"Usage","what":"MolPad could help you with:","title":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","text":"Clustering data k-means building group network. Find significant trend patterns datasets. Target interaction groups, taxons, pathways. Visualize distribution features specific pathways group network. Search particular features user-defined labels. Check detailed information feature automatically generated hyperlinks. better overall understanding datasets.","code":""},{"path":[]},{"path":"https://kaiyanm.github.io/MolPad/index.html","id":"getting-help","dir":"","previous_headings":"","what":"Getting Help","title":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","text":"need assistance MolPad, two primary ways seek help: Ask us anything related MolPad! add question, create issue repository. Stack Overflow another excellent resource answering common issues R. Remember ’s particularly effective can provide reproducible example shows specific problem ’re .","code":""},{"path":"https://kaiyanm.github.io/MolPad/index.html","id":"contribution","dir":"","previous_headings":"","what":"Contribution","title":"MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics","text":"contribute project, use following workflow: fork repository –> create local copy –> submit pull request.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":null,"dir":"Reference","previous_headings":"","what":"Cheese data — cheese","title":"Cheese data — cheese","text":"context cheese production, regular application brine solution maturation technique promotes uniformity microbial populations cheese's surface facilitates interactions among microorganisms. investigation involved analysis longitudinal dataset encompassing three washed-rind cheese communities sampled ripening process.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Cheese data — cheese","text":"two datasets: cheese annotations","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Cheese data — cheese","text":"Reference: doi:10.1128/msystems.00701-22","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Cheese data — cheese","text":"study uncovered remarkably consistent microbial progression within cheese. bacterial community, Firmicutes dominate outset, Proteobacteria swiftly assuming dominance end ripening period. Additionally, Cheese Cheese C consistently demonstrate establishment Actinobacteria Bacteroidetes, distinct manner. corroborate findings using MolPad dashboard, conducted analysis two cheeses (C) three production batches, spanning weeks 2 13.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":"cheese-data","dir":"Reference","previous_headings":"","what":"cheese data","title":"Cheese data — cheese","text":"data frame 106239 rows 18 variables: ID sample ID A_1~A_5 Time series data measured cheese . C_1~C_5 Time series data measured cheese C. domain category feature phylum category feature class category feature order category feature family category feature genus category feature","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":"annotation-data","dir":"Reference","previous_headings":"","what":"annotation data","title":"Cheese data — cheese","text":"data frame 86156 rows 9 variables: ID sample ID GO_ID GO IDs, represents link gene product type molecular function KEGG_ID KEGG IDs, linking genomic information higher order functional information. domain category feature phylum category feature class category feature order category feature family category feature genus category feature","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/cheese.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cheese data — cheese","text":"","code":"data(cheese) head(annotations) #> # A tibble: 6 × 9 #> ID GO_ID KEGG_ID domain phylum class order family genus #> #> 1 9 - - Bacteria Pseudomonadota Alphaproteobac… Caul… Caulo… Caul… #> 2 10 - - Bacteria Pseudomonadota Alphaproteobac… Caul… Caulo… Caul… #> 3 11 - - Bacteria Pseudomonadota Alphaproteobac… Hyph… Barto… Bart… #> 4 12 - - Bacteria Pseudomonadota Alphaproteobac… Hyph… Bruce… Bruc… #> 5 13 - - Bacteria Pseudomonadota Alphaproteobac… Hyph… Nitro… Brad… #> 6 14 - - Bacteria Pseudomonadota Alphaproteobac… Hyph… Phyll… Meso… head(cheese) #> # A tibble: 6 × 18 #> ID A_1 A_2 A_3 A_4 A_5 C_1 C_3 C_4 C_5 GO_ID KEGG_ID #> #> 1 1 38 23 27 5 11 13 1 3 0 NA NA #> 2 2 23 6 4 0 9 21 1 0 2 NA NA #> 3 3 24 5 37 10 19 3 0 1 4 NA NA #> 4 4 3 2 4 2 16 56 1 7 17 NA NA #> 5 5 58 14 45 13 32 82 3 8 3 NA NA #> 6 6 12 9 14 4 13 2 0 1 4 NA NA #> # ℹ 6 more variables: domain , phylum , class , order , #> # family , genus "},{"path":"https://kaiyanm.github.io/MolPad/reference/color_palettes__.html","id":null,"dir":"Reference","previous_headings":"","what":"Color palettes — color_palettes__","title":"Color palettes — color_palettes__","text":"internal function built-color palettes.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/color_palettes__.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Color palettes — color_palettes__","text":"","code":"color_palettes__(name)"},{"path":"https://kaiyanm.github.io/MolPad/reference/color_palettes__.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Color palettes — color_palettes__","text":"name string two options: \"graytone\" \"darkwarm\".","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/color_palettes__.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Color palettes — color_palettes__","text":"","code":"color_palettes__(\"graytone\") #> [1] \"#c3c5c7\" \"#30beba\" \"#998a73\" \"#807566\" \"#a5b1c9\" \"#5d5232\" \"#9a2b41\" #> [8] \"#d6b7a2\" \"#882db4\" \"#b47a53\" color_palettes__(\"darkwarm\") #> [1] \"#251305\" \"#C70A80\" \"#FBCB0A\" \"#ff1122\" \"#7D7463\" \"#CECE5A\" \"#FF9B9B\" #> [8] \"#A459D1\" \"#00235B\" \"#7E1717\""},{"path":"https://kaiyanm.github.io/MolPad/reference/convert_range.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert range — convert_range","title":"Convert range — convert_range","text":"internal function range conversion. element x, returns distance minimal values divided range.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/convert_range.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert range — convert_range","text":"","code":"convert_range(x)"},{"path":"https://kaiyanm.github.io/MolPad/reference/convert_range.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert range — convert_range","text":"x vector numbers.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/convert_range.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert range — convert_range","text":"vector calculated (x - min(x)) / (max(x) - min(x))","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/convert_range.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert range — convert_range","text":"","code":"convert_range(5:10) #> [1] 0.0 0.2 0.4 0.6 0.8 1.0"},{"path":"https://kaiyanm.github.io/MolPad/reference/extend.color__.html","id":null,"dir":"Reference","previous_headings":"","what":"Extend color palettes — extend.color__","title":"Extend color palettes — extend.color__","text":"internal function designed pair vectors color palettes automatically generate colors longer vectors find match.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/extend.color__.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extend color palettes — extend.color__","text":"","code":"extend.color__(n, colors, extendby = 1, alpha = 1, ...)"},{"path":"https://kaiyanm.github.io/MolPad/reference/extend.color__.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extend color palettes — extend.color__","text":"n number. length feature vector want match colors. colors vector colors (finite). extendby number select 1, 2, 3, 4 5, representing distinct auto-fill schemes. alpha number select range 0,1.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gAnnotation.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate processed annotation — gAnnotation","title":"Generate processed annotation — gAnnotation","text":"gAnnotation() provides standard input format dashboard, allowing users select two columns primary factors wish visualize describe data.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gAnnotation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate processed annotation — gAnnotation","text":"","code":"gAnnotation(data, first_order, second_order)"},{"path":"https://kaiyanm.github.io/MolPad/reference/gAnnotation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate processed annotation — gAnnotation","text":"data data.frame containing annotations used describing features measured time point. also required include ID least two categorical variables. first_order string. name one column categorical variable data. second_order string. name another column categorical variable data.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gAnnotation.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate processed annotation — gAnnotation","text":"Guidelines Selecting Annotations: first-order annotation recommended functional, pathway functional system. parameter primarily serve purpose filtering one network time displaying dashboard. second-order annotation utilized illustrate composition first-order annotation using bar plot. Therefore, better set taxon, class label, etc.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gAnnotation.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate processed annotation — gAnnotation","text":"","code":"data(test_data) test_annotations_processed <- gAnnotation(test_annotations,system,class) head(test_annotations_processed) #> ID #> 1 1 #> 2 2 #> 3 3 #> 4 4 #> 5 5 #> 6 6 #> GO_ID #> 1 #> 2 #> 3 GO:0003674,GO:0003824,GO:0005575,GO:0006629,GO:0006644,GO:0006650,GO:0006655,GO:0006793,GO:0006796,GO:0008150,GO:0008152,GO:0008610,GO:0008654,GO:0008808,GO:0009058,GO:0009987,GO:0016020,GO:0016740,GO:0016772,GO:0016780,GO:0019637,GO:0030572,GO:0032048,GO:0032049,GO:0044237,GO:0044238,GO:0044249,GO:0044255,GO:0045017,GO:0046471,GO:0046474,GO:0046486,GO:0071704,GO:0090407,GO:1901576 #> 4 GO:0003674,GO:0003676,GO:0003723,GO:0003729,GO:0003824,GO:0004654,GO:0005488,GO:0005575,GO:0005622,GO:0005623,GO:0005737,GO:0006139,GO:0006401,GO:0006402,GO:0006725,GO:0006807,GO:0008150,GO:0008152,GO:0009056,GO:0009057,GO:0009892,GO:0009987,GO:0010468,GO:0010605,GO:0010629,GO:0016070,GO:0016071,GO:0016740,GO:0016772,GO:0016779,GO:0019222,GO:0019439,GO:0034641,GO:0034655,GO:0043170,GO:0044237,GO:0044238,GO:0044248,GO:0044260,GO:0044265,GO:0044270,GO:0044424,GO:0044464,GO:0046483,GO:0046700,GO:0048519,GO:0050789,GO:0060255,GO:0065007,GO:0071704,GO:0090304,GO:0097159,GO:1901360,GO:1901361,GO:1901363,GO:1901575 #> 5 #> 6 #> KEGG_ID Pathway taxonomic.scope #> 1 K07124 Integumentary System hormonal proteins #> 2 Skeletal System structural proteins #> 3 K06131 Muscular System enzymes #> 4 K00962 Nervous System contractile proteins #> 5 K02083 Endocrine System contractile proteins #> 6 Cardiovascular System structural proteins"},{"path":"https://kaiyanm.github.io/MolPad/reference/gClusters.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate clusters — gClusters","title":"Generate clusters — gClusters","text":"gClusters() returns clusters generated k-means yield elbow plot way finding optimal parameter.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gClusters.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate clusters — gClusters","text":"","code":"gClusters(data, ncluster = 20, elbow.max = 50, ...)"},{"path":"https://kaiyanm.github.io/MolPad/reference/gClusters.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate clusters — gClusters","text":"data scaled data.frame contain variables ID, value time_1, ..., value time_k, type extracting patterns across time. See also pre_process(). ncluster number clusters. related complexity information network: choosing ncluster, suggest thinking many nodes show visualization representative want clustered pattern. elbow.max number maximum value x-axis elbow method plot. larger expected ncluster smaller sample size. iter.max number maximum iterations allowed k-means. See also stats::kmeans. nstart number random attempts generating initial configurations. k-means algorithm choose best one among attempts. larger data, 'nstart' can set lower just set 1. See also stats::kmeans.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gClusters.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate clusters — gClusters","text":"function return list 2 elements: k-means cluster result elbow method plot.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gClusters.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate clusters — gClusters","text":"determine optimal number clusters (ncluster), advised closely examine elbow plot identify point graph substantial change 'elbow' occurs. often indicative suitable cluster count. cases dataset extensive intricate, might consider increasing value elbow.max ensure comprehensive exploration potential cluster counts. can help achieving accurate meaningful results, especially working larger complex datasets. function can executed data parameter outset. However, achieve best clustering results, adjustments recommended. initial run, users expected adjust function's parameters based clustering outcomes elbow plot analysis.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gClusters.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate clusters — gClusters","text":"","code":"data(test_data) reslist <- gClusters(test_data_processed) # k-means result reslist[[1]] #> K-means clustering with 20 clusters of sizes 6, 3, 6, 5, 5, 8, 6, 5, 5, 6, 4, 11, 3, 3, 3, 2, 4, 2, 2, 11 #> #> Cluster means: #> T1 T2 T3 T4 T5 T6 #> 1 2.0919348 -0.33320285 -0.50786563 -0.5579659 -0.5579659 1.5455977 #> 2 0.8277105 1.32036794 -0.09261332 1.2325986 0.4199176 -0.9576634 #> 3 0.5077879 -0.24564283 0.53503708 0.2363183 1.0793829 1.3583848 #> 4 0.2914366 -0.41308350 -0.76541563 -0.9724353 -0.9724353 1.6040887 #> 5 1.2081037 -0.66790409 -0.66790409 -0.9275526 -0.4204221 1.2719890 #> 6 0.1155971 -0.19000668 -0.25629794 -0.1487697 -0.4383348 2.5830148 #> 7 1.2574004 -0.64914997 -0.87502695 0.9353966 0.4760273 1.2681050 #> 8 0.9389212 -0.86278238 0.43887930 1.0009533 1.3733646 -1.0423264 #> 9 -0.8135539 -0.84696982 -0.32573017 1.1218534 2.0955812 -0.5620731 #> 10 -0.3802219 -0.36840199 0.02254271 1.6459653 1.9633618 -0.3246455 #> 11 -0.1225506 -0.44139873 -0.19548572 1.0299038 2.3840629 -0.2147528 #> 12 2.5784883 -0.02446156 -0.60355041 -0.2547486 -0.5120230 -0.3947043 #> 13 0.9922755 0.32399254 -0.94565021 -0.7125240 -0.5104935 -0.9456502 #> 14 0.2699801 -0.08094677 -0.28926312 -0.7813127 -0.6065051 -0.1494523 #> 15 1.7947637 -0.47746004 -0.52783931 -0.7635958 -0.7635958 -0.6438957 #> 16 0.1789247 1.26157166 0.82115649 0.3899368 1.2707671 -0.2614905 #> 17 1.4517382 0.26236463 1.31600581 -0.2531012 0.2004547 -1.1238942 #> 18 -0.2784769 -0.82258408 -0.82258408 -0.8225841 1.2338761 -0.8225841 #> 19 2.1235865 1.05868538 -0.39467611 0.1315587 -0.1315587 0.3946761 #> 20 -0.2262821 -0.73107613 -0.66098092 -0.4323193 -0.4035074 -0.3836848 #> T7 T8 T9 T10 #> 1 -0.425513174 -0.557965933 -0.30571480 -0.391338269 #> 2 -1.160107459 -0.295057401 -0.64757654 -0.647576540 #> 3 -1.006282312 -0.766188425 -0.64803067 -1.050766722 #> 4 0.301678216 -0.371684852 0.66435231 0.633498705 #> 5 1.209152596 -0.095960244 -0.32347149 -0.586030729 #> 6 -0.006327004 -0.327677972 -0.66559889 -0.665598891 #> 7 -0.354972016 -0.601904415 -0.82778139 -0.628094550 #> 8 -1.159101258 -0.345888528 -0.27147562 -0.070544200 #> 9 -0.651292750 -0.498839228 0.42446013 0.056564270 #> 10 -0.647007074 -0.667438225 -0.63229313 -0.611861976 #> 11 -0.400726188 -0.616324716 -0.71136401 -0.711364008 #> 12 -0.526022073 -0.185303954 -0.06989626 -0.007778173 #> 13 -0.945650212 0.121962031 1.19430596 1.427432164 #> 14 -0.763902351 -0.412975440 0.39220455 2.422173070 #> 15 -0.260645101 0.389799714 -0.08327821 1.335746686 #> 16 -0.912917717 -0.481698036 -1.13312530 -1.133125299 #> 17 -0.987867401 -0.340013670 -0.33085684 -0.194830041 #> 18 -0.822584085 1.233876127 1.23387613 0.689768939 #> 19 -0.795567972 -0.795567972 -0.79556797 -0.795567972 #> 20 -0.612869917 -0.006483243 1.70560793 1.751595921 #> #> Clustering vector: #> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 #> 20 1 18 16 3 20 5 19 10 9 8 14 15 12 2 9 6 20 17 12 #> 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 #> 7 20 6 1 12 9 7 12 6 11 5 11 16 18 10 3 8 13 20 2 #> 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 #> 6 1 20 20 7 20 6 3 1 6 15 5 4 13 6 5 4 10 12 10 #> 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 #> 8 3 7 13 3 1 12 7 17 17 14 9 6 19 14 10 2 11 8 7 #> 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 #> 8 20 15 4 4 1 17 12 4 12 10 5 11 12 20 3 9 12 20 12 #> #> Within cluster sum of squares by cluster: #> [1] 3.0260293 5.0357352 13.2343206 13.1118010 10.3695240 8.0412334 #> [7] 11.7251902 8.3255313 3.4617088 2.4693324 1.6353685 13.0645655 #> [13] 2.9416715 3.1842269 5.0413438 2.4669004 9.9418120 0.9921913 #> [19] 0.9833964 12.4042363 #> (between_SS / total_SS = 83.1 %) #> #> Available components: #> #> [1] \"cluster\" \"centers\" \"totss\" \"withinss\" \"tot.withinss\" #> [6] \"betweenss\" \"size\" \"iter\" \"ifault\" # elbow plot reslist[[2]]"},{"path":"https://kaiyanm.github.io/MolPad/reference/gDashboard.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate shiny dashboard — gDashboard","title":"Generate shiny dashboard — gDashboard","text":"outputs g-functions, ready create custom Molpad dashboard. sure specify web ID columns corresponding column names.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gDashboard.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate shiny dashboard — gDashboard","text":"","code":"gDashboard( data, cluster, annotation, networkres, dashboardtitle = \"MolPad Dashboard\", id_colname = NULL, id_type = NULL )"},{"path":"https://kaiyanm.github.io/MolPad/reference/gDashboard.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate shiny dashboard — gDashboard","text":"data output pre_process():scaled data.frame contain variables ID, value time_1, ..., value time_k, type. cluster output gClusters(): list contains result k-means Cluster means, vectors sum squares. annotation output gAnnotation(): data.frame containing annotations describing features. Variables must include ID, Pathway, taxonomic.scope. Note NA permitted 3 variables. networkres output gNetwork(): data.frame 4 variables weight,IncNodePurity,var_names,. dashboardtitle string customized dashboard name. id_colname single string string vector. column names annotation dataset vector contains external database IDs. id_type single string string vector. corresponding database names columns, must chosen \"KEGG\" \"GO\".","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gDashboard.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate shiny dashboard — gDashboard","text":"Please ensure columns containing external database IDs adhere following standards: KEGG ID: Begin 'K' followed 5 digits, example, K05685 K06671. GO ID: Begin 'GO:' followed 7 digits, GO:0003674.\"","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gDashboard.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate shiny dashboard — gDashboard","text":"","code":"if (FALSE) { data(test_data) gDashboard(test_data_processed,test_cluster,test_annotations_processed,test_network,id_colname = c(\"GO_ID\",\"KEGG_ID\"),id_type = c(\"GO\",\"KEGG\")) }"},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate prediction network — gNetwork","title":"Generate prediction network — gNetwork","text":"gNetwork() generates prediction network functional annotation. every feature, features considered independent variables, top predictors selected based %IncMSE.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate prediction network — gNetwork","text":"","code":"gNetwork(clusters, ntop = 10, method = \"randomforest\")"},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate prediction network — gNetwork","text":"clusters list two outputs gClusters(). first element k-means result (See also stats::kmeans), element plot automatically omitted ease directly passing results function. ntop number pick set top n predictors(default: top 10) feature important ones. used construct network among clustered patterns. seed number random seed.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate prediction network — gNetwork","text":"assess relationships clusters using Mean Squared Error (MSE) changes resulting random shuffling. gNetwork()'s output includes edge weights node pairs, essential inputs dashboard.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate prediction network — gNetwork","text":"","code":"data(test_data) networkres <- gNetwork(test_cluster, ntop = 3) head(networkres) #> weight IncNodePurity var_names from #> 1 1.4215243 1.1776012 Group_5 Group_1 #> 2 0.7911266 0.7011049 Group_3 Group_1 #> 3 -1.9891284 0.5811186 Group_2 Group_1 #> 4 5.8098014 1.1107592 Group_4 Group_2 #> 5 5.4000389 0.8744358 Group_5 Group_2 #> 6 1.2492288 1.1816976 Group_1 Group_2 gNetwork_view(networkres)"},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork_view.html","id":null,"dir":"Reference","previous_headings":"","what":"View the feature importance plot — gNetwork_view","title":"View the feature importance plot — gNetwork_view","text":"gNetwork_view() Generate feature importance plot random forest result. plot visually highlights importance individual cluster within dataset, helping identify key factors predictive modeling.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork_view.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"View the feature importance plot — gNetwork_view","text":"","code":"gNetwork_view(data)"},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork_view.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"View the feature importance plot — gNetwork_view","text":"data dataframe; output gNetwork(), including 4 variables: weight, IncNodePurity, var_names . weight edge value, IncNodePurity(%IncMSE) measure predictions result var_names permuted. dependent variable round random forest.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/gNetwork_view.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"View the feature importance plot — gNetwork_view","text":"","code":"data(test_data) gNetwork_view(test_network) #> Warning: Removed 1 rows containing missing values (`geom_segment()`). #> Warning: Removed 1 rows containing missing values (`geom_point()`)."},{"path":"https://kaiyanm.github.io/MolPad/reference/make_line_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Make line plot — make_line_plot","title":"Make line plot — make_line_plot","text":"Generate line ribbon plot dashboard.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_line_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make line plot — make_line_plot","text":"","code":"make_line_plot(dfgroup_long, selected_groups, selected_taxa)"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_line_plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Make line plot — make_line_plot","text":"function makes ribbon plot every brush action Shiny show min, max, mean value clustered pattern group across time. ribbon plot grouped colored type variable input datasets. See also ggplot2::geom_ribbon.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_line_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Make line plot — make_line_plot","text":"","code":"data(test_data) make_line_plot(test_maindata, \"Group_5\", c(\"hormonal proteins\",\"structural proteins\",\"enzymes\",\"storage proteins\",\"antibodies\",\"transport proteins\"))"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_stackbar_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Make stackbar plot — make_stackbar_plot","title":"Make stackbar plot — make_stackbar_plot","text":"Generate stackbar plot dashboard.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_stackbar_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make stackbar plot — make_stackbar_plot","text":"","code":"make_stackbar_plot(dfgroup_long, selected_groups, selected_taxa)"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_stackbar_plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Make stackbar plot — make_stackbar_plot","text":"function makes bar plot every brush action Shiny show components clustered pattern group. bar plot colored taxonomic.scope variable processed annotation dataset generated gAnnotation(). See also ggplot2::geom_bar.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_stackbar_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Make stackbar plot — make_stackbar_plot","text":"","code":"data(test_data) make_stackbar_plot(test_maindata, \"Group_5\", c(\"hormonal proteins\",\"structural proteins\",\"enzymes\",\"storage proteins\",\"antibodies\",\"transport proteins\"))"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_graph.html","id":null,"dir":"Reference","previous_headings":"","what":"Make graph plot — make_the_graph","title":"Make graph plot — make_the_graph","text":"Generate graph dashboard.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_graph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make graph plot — make_the_graph","text":"","code":"make_the_graph(ptw, network_output, min_weight, s_ptw, graph_layout)"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_graph.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Make graph plot — make_the_graph","text":"function makes network plot every Pathway selection Shiny show relationship among clustered pattern groups. network plot built dataset generated gNetwork() can adjusted layout minimum weights. See also ggraph::ggraph.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_graph.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Make graph plot — make_the_graph","text":"","code":"data(test_data) make_the_graph(test_graphptw, test_network, 0.03, \"Muscular System\",\"kk\")"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_table.html","id":null,"dir":"Reference","previous_headings":"","what":"Make table from brushed region — make_the_table","title":"Make table from brushed region — make_the_table","text":"Generate table selected groups dashboard.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make table from brushed region — make_the_table","text":"","code":"make_the_table(p)"},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Make table from brushed region — make_the_table","text":"p ggplot output, see also ggplot2::ggplot_build.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/make_the_table.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Make table from brushed region — make_the_table","text":"function simply aims collect position information brushed area network plot returns annotation table corresponding features. takes plot object produce object information table.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/mass_produce_lm__.html","id":null,"dir":"Reference","previous_headings":"","what":"Mass produce linear model — mass_produce_lm__","title":"Mass produce linear model — mass_produce_lm__","text":"Take column dependent vairable time, produce n linear models n columns dataset.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/mass_produce_lm__.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mass produce linear model — mass_produce_lm__","text":"","code":"mass_produce_lm__(data)"},{"path":"https://kaiyanm.github.io/MolPad/reference/mass_produce_lm__.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mass produce linear model — mass_produce_lm__","text":"data dataframe.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/mass_produce_lm__.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Mass produce linear model — mass_produce_lm__","text":"internal function designed automatically generate list functions regression. column considered response variable columns. resulting functions can utilized inputs random forest regression gNetwork().","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/match.color__.html","id":null,"dir":"Reference","previous_headings":"","what":"Match color — match.color__","title":"Match color — match.color__","text":"Match vector finite list colors.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/match.color__.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Match color — match.color__","text":"","code":"match.color__(valist, mycolors, extendby = 5)"},{"path":"https://kaiyanm.github.io/MolPad/reference/match.color__.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Match color — match.color__","text":"valist vector want specify colors element. mycolors vector colors. extendby number select 1, 2, 3, 4, 5, representing distinct auto-fill schemes.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/match.color__.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Match color — match.color__","text":"","code":"my_vector <- paste0(\"N_\",1:10) match.color__(my_vector,c(\"red\",\"yellow\",\"blue\")) #> N_1 N_2 N_3 N_4 N_5 #> \"red\" \"yellow\" \"blue\" \"white\" \"antiquewhite3\" #> N_6 N_7 N_8 N_9 N_10 #> \"aquamarine3\" \"azure3\" \"bisque2\" \"blue\" \"blueviolet\""},{"path":"https://kaiyanm.github.io/MolPad/reference/match_database.html","id":null,"dir":"Reference","previous_headings":"","what":"Match database — match_database","title":"Match database — match_database","text":"matches selected columns online database names.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/match_database.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Match database — match_database","text":"","code":"match_database(data, id_colname, id_type)"},{"path":"https://kaiyanm.github.io/MolPad/reference/match_database.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Match database — match_database","text":"match_database() internal function matches selected columns dataset corresponding databases. Currently MolPad support two types annotation database: GO KEGG. want custom URL platform, download modify paste_URL() function.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/match_database.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Match database — match_database","text":"","code":"data(test_data) head(test_annotations_processed) #> ID #> 1 1 #> 2 2 #> 3 3 #> 4 4 #> 5 5 #> 6 6 #> GO_ID #> 1 #> 2 #> 3 GO:0003674,GO:0003824,GO:0005575,GO:0006629,GO:0006644,GO:0006650,GO:0006655,GO:0006793,GO:0006796,GO:0008150,GO:0008152,GO:0008610,GO:0008654,GO:0008808,GO:0009058,GO:0009987,GO:0016020,GO:0016740,GO:0016772,GO:0016780,GO:0019637,GO:0030572,GO:0032048,GO:0032049,GO:0044237,GO:0044238,GO:0044249,GO:0044255,GO:0045017,GO:0046471,GO:0046474,GO:0046486,GO:0071704,GO:0090407,GO:1901576 #> 4 GO:0003674,GO:0003676,GO:0003723,GO:0003729,GO:0003824,GO:0004654,GO:0005488,GO:0005575,GO:0005622,GO:0005623,GO:0005737,GO:0006139,GO:0006401,GO:0006402,GO:0006725,GO:0006807,GO:0008150,GO:0008152,GO:0009056,GO:0009057,GO:0009892,GO:0009987,GO:0010468,GO:0010605,GO:0010629,GO:0016070,GO:0016071,GO:0016740,GO:0016772,GO:0016779,GO:0019222,GO:0019439,GO:0034641,GO:0034655,GO:0043170,GO:0044237,GO:0044238,GO:0044248,GO:0044260,GO:0044265,GO:0044270,GO:0044424,GO:0044464,GO:0046483,GO:0046700,GO:0048519,GO:0050789,GO:0060255,GO:0065007,GO:0071704,GO:0090304,GO:0097159,GO:1901360,GO:1901361,GO:1901363,GO:1901575 #> 5 #> 6 #> KEGG_ID Pathway taxonomic.scope #> 1 K07124 Integumentary System hormonal proteins #> 2 Skeletal System structural proteins #> 3 K06131 Muscular System enzymes #> 4 K00962 Nervous System contractile proteins #> 5 K02083 Endocrine System contractile proteins #> 6 Cardiovascular System structural proteins head(match_database(test_annotations_processed,id_colname = c(\"GO_ID\",\"KEGG_ID\"),id_type = c(\"GO\",\"KEGG\"))) #> ID #> 1 1 #> 2 2 #> 3 3 #> 4 4 #> 5 5 #> 6 6 #> GO_ID #> 1 #> 2 #> 3 GO:0003674<\/a>
GO:0003824<\/a>
GO:0005575<\/a>
GO:0006629<\/a>
GO:0006644<\/a>
GO:0006650<\/a>
GO:0006655<\/a>
GO:0006793<\/a>
GO:0006796<\/a>
GO:0008150<\/a>
GO:0008152<\/a>
GO:0008610<\/a>
GO:0008654<\/a>
GO:0008808<\/a>
GO:0009058<\/a>
GO:0009987<\/a>
GO:0016020<\/a>
GO:0016740<\/a>
GO:0016772<\/a>
GO:0016780<\/a>
GO:0019637<\/a>
GO:0030572<\/a>
GO:0032048<\/a>
GO:0032049<\/a>
GO:0044237<\/a>
GO:0044238<\/a>
GO:0044249<\/a>
GO:0044255<\/a>
GO:0045017<\/a>
GO:0046471<\/a>
GO:0046474<\/a>
GO:0046486<\/a>
GO:0071704<\/a>
GO:0090407<\/a>
GO:1901576<\/a> #> 4 GO:0003674<\/a>
GO:0003676<\/a>
GO:0003723<\/a>
GO:0003729<\/a>
GO:0003824<\/a>
GO:0004654<\/a>
GO:0005488<\/a>
GO:0005575<\/a>
GO:0005622<\/a>
GO:0005623<\/a>
GO:0005737<\/a>
GO:0006139<\/a>
GO:0006401<\/a>
GO:0006402<\/a>
GO:0006725<\/a>
GO:0006807<\/a>
GO:0008150<\/a>
GO:0008152<\/a>
GO:0009056<\/a>
GO:0009057<\/a>
GO:0009892<\/a>
GO:0009987<\/a>
GO:0010468<\/a>
GO:0010605<\/a>
GO:0010629<\/a>
GO:0016070<\/a>
GO:0016071<\/a>
GO:0016740<\/a>
GO:0016772<\/a>
GO:0016779<\/a>
GO:0019222<\/a>
GO:0019439<\/a>
GO:0034641<\/a>
GO:0034655<\/a>
GO:0043170<\/a>
GO:0044237<\/a>
GO:0044238<\/a>
GO:0044248<\/a>
GO:0044260<\/a>
GO:0044265<\/a>
GO:0044270<\/a>
GO:0044424<\/a>
GO:0044464<\/a>
GO:0046483<\/a>
GO:0046700<\/a>
GO:0048519<\/a>
GO:0050789<\/a>
GO:0060255<\/a>
GO:0065007<\/a>
GO:0071704<\/a>
GO:0090304<\/a>
GO:0097159<\/a>
GO:1901360<\/a>
GO:1901361<\/a>
GO:1901363<\/a>
GO:1901575<\/a> #> 5 #> 6 #> KEGG_ID #> 1 K07124<\/a> #> 2 #> 3 K06131<\/a> #> 4 K00962<\/a> #> 5 K02083<\/a> #> 6 #> Pathway taxonomic.scope #> 1 Integumentary System hormonal proteins #> 2 Skeletal System structural proteins #> 3 Muscular System enzymes #> 4 Nervous System contractile proteins #> 5 Endocrine System contractile proteins #> 6 Cardiovascular System structural proteins"},{"path":"https://kaiyanm.github.io/MolPad/reference/paste_URL.html","id":null,"dir":"Reference","previous_headings":"","what":"Paste URL — paste_URL","title":"Paste URL — paste_URL","text":"Retrieve database IDs associate respective URLs.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/paste_URL.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Paste URL — paste_URL","text":"","code":"paste_URL(x, id_type)"},{"path":"https://kaiyanm.github.io/MolPad/reference/paste_URL.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Paste URL — paste_URL","text":"","code":"data(test_data) paste_URL(test_annotations$GO_ID[1:4], id_type = \"GO\") #> [1] NA #> [2] NA #> [3] \"GO:0003674<\/a>
GO:0003824<\/a>
GO:0005575<\/a>
GO:0006629<\/a>
GO:0006644<\/a>
GO:0006650<\/a>
GO:0006655<\/a>
GO:0006793<\/a>
GO:0006796<\/a>
GO:0008150<\/a>
GO:0008152<\/a>
GO:0008610<\/a>
GO:0008654<\/a>
GO:0008808<\/a>
GO:0009058<\/a>
GO:0009987<\/a>
GO:0016020<\/a>
GO:0016740<\/a>
GO:0016772<\/a>
GO:0016780<\/a>
GO:0019637<\/a>
GO:0030572<\/a>
GO:0032048<\/a>
GO:0032049<\/a>
GO:0044237<\/a>
GO:0044238<\/a>
GO:0044249<\/a>
GO:0044255<\/a>
GO:0045017<\/a>
GO:0046471<\/a>
GO:0046474<\/a>
GO:0046486<\/a>
GO:0071704<\/a>
GO:0090407<\/a>
GO:1901576<\/a>\" #> [4] \"GO:0003674<\/a>
GO:0003676<\/a>
GO:0003723<\/a>
GO:0003729<\/a>
GO:0003824<\/a>
GO:0004654<\/a>
GO:0005488<\/a>
GO:0005575<\/a>
GO:0005622<\/a>
GO:0005623<\/a>
GO:0005737<\/a>
GO:0006139<\/a>
GO:0006401<\/a>
GO:0006402<\/a>
GO:0006725<\/a>
GO:0006807<\/a>
GO:0008150<\/a>
GO:0008152<\/a>
GO:0009056<\/a>
GO:0009057<\/a>
GO:0009892<\/a>
GO:0009987<\/a>
GO:0010468<\/a>
GO:0010605<\/a>
GO:0010629<\/a>
GO:0016070<\/a>
GO:0016071<\/a>
GO:0016740<\/a>
GO:0016772<\/a>
GO:0016779<\/a>
GO:0019222<\/a>
GO:0019439<\/a>
GO:0034641<\/a>
GO:0034655<\/a>
GO:0043170<\/a>
GO:0044237<\/a>
GO:0044238<\/a>
GO:0044248<\/a>
GO:0044260<\/a>
GO:0044265<\/a>
GO:0044270<\/a>
GO:0044424<\/a>
GO:0044464<\/a>
GO:0046483<\/a>
GO:0046700<\/a>
GO:0048519<\/a>
GO:0050789<\/a>
GO:0060255<\/a>
GO:0065007<\/a>
GO:0071704<\/a>
GO:0090304<\/a>
GO:0097159<\/a>
GO:1901360<\/a>
GO:1901361<\/a>
GO:1901363<\/a>
GO:1901575<\/a>\""},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":null,"dir":"Reference","previous_headings":"","what":"Pre-processing datasets — pre_process","title":"Pre-processing datasets — pre_process","text":"pre_process() function aids processing data inputs automatically establishes standardized format future use. allows two types data input: list datasets different sources long dataset containing specified last column type.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pre-processing datasets — pre_process","text":"","code":"pre_process( data, typenameList = NULL, replaceNA = TRUE, scale = TRUE, autoColName = \"Sec_\" )"},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pre-processing datasets — pre_process","text":"data data.frame describe feature one row. data contain variables ID value time_1, ..., value time_k, type extracting patterns across time. Note initial last column must exactly ID type. multiple data.frame format needs analyzed, also put list data.frame argument. case, variable type required generated next argument typenameList. typenameList vector strings. parameter used clarify source names data.frame, applicable input data list data.frame. default, set \"Dataset_1\", \"Dataset_2\", ..., etc. scale Logical; scale TRUE (default), standardize data.frame row base::scale. converts original value z-score. See also scale_by_row__(). autoColName string; autoColName -NULL (default), automatically set uniform column names data.frames. parameter applicable input data list data.frame. replaceNa Logical; replaceNa TRUE (default), replace NA 0.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pre-processing datasets — pre_process","text":"function returns long data.frame columns ID, value time_1, ..., value time_k, type.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Pre-processing datasets — pre_process","text":"consider two distinct scenarios application: one scenario, individuals collect several datasets various aspects instruments objects. example, might separately detecting lipids, metabolites, peptides specific soil sample. scenario, data uniform quality, can categorized larger groups exhibit significant differences. cases, pre_process() function serves valuable versatile tool. Yet, function optional generating dashboard. Users can perform processing long format matches required output. However, mindful number samples (timepoints) must greater 5 avoid potential errors subsequent prediction section.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pre-processing datasets — pre_process","text":"","code":"data(test_data) head(test_data, 10) #> ID T1 T2 T3 T4 T5 T6 T7 T8 T9 T10 type #> 1 1 1 0 0 1 1 0 0 1 6 6 type_A #> 2 2 6 0 0 0 0 3 1 0 2 1 type_A #> 3 3 1 0 0 0 2 0 0 2 2 1 type_A #> 4 4 4 5 3 3 7 2 1 1 0 0 type_A #> 5 5 4 3 NA 2 5 5 0 0 0 0 type_A #> 6 6 4 1 0 1 3 1 3 5 11 14 type_A #> 7 7 1 0 0 0 1 3 3 1 1 1 type_A #> 8 8 4 2 1 1 1 1 0 0 0 0 type_A #> 9 9 1 1 1 19 22 1 2 1 1 2 type_A #> 10 10 1 1 3 5 8 2 2 2 5 2 type_A a <- pre_process(test_data) head(a, 10) #> ID T1 T2 T3 T4 T5 T6 #> 1 1 -0.25354628 -0.6761234 -0.67612340 -0.25354628 -0.25354628 -0.6761234 #> 2 2 2.41458180 -0.6678631 -0.66786305 -0.66786305 -0.66786305 0.8733594 #> 3 3 0.21764288 -0.8705715 -0.87057150 -0.87057150 1.30585725 -0.8705715 #> 4 4 0.61658123 1.0569964 0.17616607 0.17616607 1.93782672 -0.2642491 #> 5 5 0.96186009 0.5038315 -0.87025436 0.04580286 1.41988870 1.4198887 #> 6 6 -0.06459959 -0.7105955 -0.92592741 -0.71059546 -0.27993154 -0.7105955 #> 7 7 -0.09086738 -0.9995412 -0.99954118 -0.99954118 -0.09086738 1.7264802 #> 8 8 2.40535118 0.8017837 0.00000000 0.00000000 0.00000000 0.0000000 #> 9 9 -0.50260633 -0.5026063 -0.50260633 1.70395805 2.07171878 -0.5026063 #> 10 10 -0.94019379 -0.9401938 -0.04477113 0.85065153 2.19378551 -0.4924825 #> T7 T8 T9 T10 type #> 1 -0.6761234 -0.25354628 1.85933936 1.85933936 type_A #> 2 -0.1541222 -0.66786305 0.35961857 -0.15412224 type_A #> 3 -0.8705715 1.30585725 1.30585725 0.21764288 type_A #> 4 -0.7046643 -0.70466426 -1.14507943 -1.14507943 type_A #> 5 -0.8702544 -0.87025436 -0.87025436 -0.87025436 type_A #> 6 -0.2799315 0.15073237 1.44272411 2.08871998 type_A #> 7 1.7264802 -0.09086738 -0.09086738 -0.09086738 type_A #> 8 -0.8017837 -0.80178373 -0.80178373 -0.80178373 type_A #> 9 -0.3800194 -0.50260633 -0.50260633 -0.38001942 type_A #> 10 -0.4924825 -0.49248246 0.85065153 -0.49248246 type_A"},{"path":"https://kaiyanm.github.io/MolPad/reference/reshape_for_make_functions.html","id":null,"dir":"Reference","previous_headings":"","what":"Reshape for 'make' functions — reshape_for_make_functions","title":"Reshape for 'make' functions — reshape_for_make_functions","text":"internal function produces three primary datasets dashboard intended \"make\" functions.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/reshape_for_make_functions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reshape for 'make' functions — reshape_for_make_functions","text":"","code":"reshape_for_make_functions(data, cluster, annotation, id_colname, id_type)"},{"path":"https://kaiyanm.github.io/MolPad/reference/reshape_for_make_functions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reshape for 'make' functions — reshape_for_make_functions","text":"data output pre_process() cluster output gClusters() annotation output gPathway() id_colname columns contain database IDs. id_type corresponding database names columns.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/reshape_for_make_functions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reshape for 'make' functions — reshape_for_make_functions","text":"","code":"data(test_data) l <- reshape_for_make_functions(test_data_processed, test_cluster, test_annotations_processed, id_colname = c(\"GO_ID\",\"KEGG_ID\"),id_type = c(\"GO\",\"KEGG\")) head(l[[1]]) #> ID cluster #> 1 1 Group_2 #> 2 2 Group_5 #> 3 3 Group_2 #> 4 4 Group_4 #> 5 5 Group_4 #> 6 6 Group_2 #> GO_ID #> 1 #> 2 #> 3 GO:0003674,GO:0003824,GO:0005575,GO:0006629,GO:0006644,GO:0006650,GO:0006655,GO:0006793,GO:0006796,GO:0008150,GO:0008152,GO:0008610,GO:0008654,GO:0008808,GO:0009058,GO:0009987,GO:0016020,GO:0016740,GO:0016772,GO:0016780,GO:0019637,GO:0030572,GO:0032048,GO:0032049,GO:0044237,GO:0044238,GO:0044249,GO:0044255,GO:0045017,GO:0046471,GO:0046474,GO:0046486,GO:0071704,GO:0090407,GO:1901576 #> 4 GO:0003674,GO:0003676,GO:0003723,GO:0003729,GO:0003824,GO:0004654,GO:0005488,GO:0005575,GO:0005622,GO:0005623,GO:0005737,GO:0006139,GO:0006401,GO:0006402,GO:0006725,GO:0006807,GO:0008150,GO:0008152,GO:0009056,GO:0009057,GO:0009892,GO:0009987,GO:0010468,GO:0010605,GO:0010629,GO:0016070,GO:0016071,GO:0016740,GO:0016772,GO:0016779,GO:0019222,GO:0019439,GO:0034641,GO:0034655,GO:0043170,GO:0044237,GO:0044238,GO:0044248,GO:0044260,GO:0044265,GO:0044270,GO:0044424,GO:0044464,GO:0046483,GO:0046700,GO:0048519,GO:0050789,GO:0060255,GO:0065007,GO:0071704,GO:0090304,GO:0097159,GO:1901360,GO:1901361,GO:1901363,GO:1901575 #> 5 #> 6 #> KEGG_ID Pathway taxonomic.scope #> 1 K07124 Integumentary System hormonal proteins #> 2 Skeletal System structural proteins #> 3 K06131 Muscular System enzymes #> 4 K00962 Nervous System contractile proteins #> 5 K02083 Endocrine System contractile proteins #> 6 Cardiovascular System structural proteins head(l[[2]]) #> # A tibble: 6 × 6 #> ID type cluster day value taxonomic.scope #> #> 1 1 type_A Group_2 T1 -0.254 hormonal proteins #> 2 1 type_A Group_2 T2 -0.676 hormonal proteins #> 3 1 type_A Group_2 T3 -0.676 hormonal proteins #> 4 1 type_A Group_2 T4 -0.254 hormonal proteins #> 5 1 type_A Group_2 T5 -0.254 hormonal proteins #> 6 1 type_A Group_2 T6 -0.676 hormonal proteins head(l[[3]]) #> ID cluster #> 1 1 Group_2 #> 2 2 Group_5 #> 3 3 Group_2 #> 4 4 Group_4 #> 5 5 Group_4 #> 6 6 Group_2 #> GO_ID #> 1 #> 2 #> 3 GO:0003674<\/a>
GO:0003824<\/a>
GO:0005575<\/a>
GO:0006629<\/a>
GO:0006644<\/a>
GO:0006650<\/a>
GO:0006655<\/a>
GO:0006793<\/a>
GO:0006796<\/a>
GO:0008150<\/a>
GO:0008152<\/a>
GO:0008610<\/a>
GO:0008654<\/a>
GO:0008808<\/a>
GO:0009058<\/a>
GO:0009987<\/a>
GO:0016020<\/a>
GO:0016740<\/a>
GO:0016772<\/a>
GO:0016780<\/a>
GO:0019637<\/a>
GO:0030572<\/a>
GO:0032048<\/a>
GO:0032049<\/a>
GO:0044237<\/a>
GO:0044238<\/a>
GO:0044249<\/a>
GO:0044255<\/a>
GO:0045017<\/a>
GO:0046471<\/a>
GO:0046474<\/a>
GO:0046486<\/a>
GO:0071704<\/a>
GO:0090407<\/a>
GO:1901576<\/a> #> 4 GO:0003674<\/a>
GO:0003676<\/a>
GO:0003723<\/a>
GO:0003729<\/a>
GO:0003824<\/a>
GO:0004654<\/a>
GO:0005488<\/a>
GO:0005575<\/a>
GO:0005622<\/a>
GO:0005623<\/a>
GO:0005737<\/a>
GO:0006139<\/a>
GO:0006401<\/a>
GO:0006402<\/a>
GO:0006725<\/a>
GO:0006807<\/a>
GO:0008150<\/a>
GO:0008152<\/a>
GO:0009056<\/a>
GO:0009057<\/a>
GO:0009892<\/a>
GO:0009987<\/a>
GO:0010468<\/a>
GO:0010605<\/a>
GO:0010629<\/a>
GO:0016070<\/a>
GO:0016071<\/a>
GO:0016740<\/a>
GO:0016772<\/a>
GO:0016779<\/a>
GO:0019222<\/a>
GO:0019439<\/a>
GO:0034641<\/a>
GO:0034655<\/a>
GO:0043170<\/a>
GO:0044237<\/a>
GO:0044238<\/a>
GO:0044248<\/a>
GO:0044260<\/a>
GO:0044265<\/a>
GO:0044270<\/a>
GO:0044424<\/a>
GO:0044464<\/a>
GO:0046483<\/a>
GO:0046700<\/a>
GO:0048519<\/a>
GO:0050789<\/a>
GO:0060255<\/a>
GO:0065007<\/a>
GO:0071704<\/a>
GO:0090304<\/a>
GO:0097159<\/a>
GO:1901360<\/a>
GO:1901361<\/a>
GO:1901363<\/a>
GO:1901575<\/a> #> 5 #> 6 #> KEGG_ID #> 1 K07124<\/a> #> 2 #> 3 K06131<\/a> #> 4 K00962<\/a> #> 5 K02083<\/a> #> 6 #> Pathway taxonomic.scope #> 1 Integumentary System hormonal proteins #> 2 Skeletal System structural proteins #> 3 Muscular System enzymes #> 4 Nervous System contractile proteins #> 5 Endocrine System contractile proteins #> 6 Cardiovascular System structural proteins"},{"path":"https://kaiyanm.github.io/MolPad/reference/scale_by_row__.html","id":null,"dir":"Reference","previous_headings":"","what":"Scale by row — scale_by_row__","title":"Scale by row — scale_by_row__","text":"Scales values sample, row independently processed.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/scale_by_row__.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Scale by row — scale_by_row__","text":"","code":"scale_by_row__(data)"},{"path":"https://kaiyanm.github.io/MolPad/reference/scale_by_row__.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Scale by row — scale_by_row__","text":"input expected data frame first column ID following columns containing observations different time points. ID column remains unaltered, columns double (dbl) format scaled.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/scale_by_row__.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Scale by row — scale_by_row__","text":"","code":"data(test_data) scale_by_row__(test_data[1:5,1:10]) #> ID T1 T2 T3 T4 T5 T6 #> 1 1 -0.05847053 -0.5847053 -0.58470535 -0.05847053 -0.05847053 -0.5847053 #> 2 2 2.26366583 -0.6467617 -0.64676167 -0.64676167 -0.64676167 0.8084521 #> 3 3 0.22866478 -0.8003267 -0.80032673 -0.80032673 1.25765629 -0.8003267 #> 4 4 0.50395263 0.9575100 0.05039526 0.05039526 1.86462473 -0.4031621 #> 5 5 0.73869087 0.2841119 NA -0.17046712 1.19326987 1.1932699 #> T7 T8 T9 #> 1 -0.5847053 -0.05847053 2.5727035 #> 2 -0.1616904 -0.64676167 0.3233808 #> 3 -0.8003267 1.25765629 1.2576563 #> 4 -0.8567195 -0.85671947 -1.3102768 #> 5 -1.0796251 -1.07962512 -1.0796251"},{"path":"https://kaiyanm.github.io/MolPad/reference/test_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Test data — test_data","title":"Test data — test_data","text":"synthetically generated dataset created basic testing purposes.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/test_data.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Test data — test_data","text":"two datasets: data annotations","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/test_data.html","id":"data","dir":"Reference","previous_headings":"","what":"data","title":"Test data — test_data","text":"data frame 100 rows 12 variables: ID row ID T1~T10 count value 10 timepoints type type ~D","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/test_data.html","id":"annotations","dir":"Reference","previous_headings":"","what":"annotations","title":"Test data — test_data","text":"data frame 100 rows 5 variables: ID row ID GO_ID go ID KEGG_ID kegg ID system primary lable: 'Integumentary System', 'Skeletal System', 'Muscular System', 'Nervous System', 'Endocrine System', 'Cardiovascular System', 'Lymphatic System', 'Respiratory System', 'Digestive System', 'Urinary System' class secondary label: 'antibodies', 'contractile proteins', 'enzymes', 'hormonal proteins', 'structural proteins', 'storage proteins', 'transport proteins'","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/test_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Test data — test_data","text":"","code":"data(test_data)"},{"path":"https://kaiyanm.github.io/MolPad/reference/transpose_dataframe__.html","id":null,"dir":"Reference","previous_headings":"","what":"Transpose dataframe — transpose_dataframe__","title":"Transpose dataframe — transpose_dataframe__","text":"function transposes provided data frame, using values first column new column names.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/transpose_dataframe__.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transpose dataframe — transpose_dataframe__","text":"","code":"transpose_dataframe__(data)"},{"path":"https://kaiyanm.github.io/MolPad/reference/transpose_dataframe__.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transpose dataframe — transpose_dataframe__","text":"expected input data frame first column serves Time, subsequent columns contain observations various features.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/transpose_dataframe__.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transpose dataframe — transpose_dataframe__","text":"","code":"a <- data.frame(\"Day\"=c(\"Day1\",\"Day2\",\"Day3\"),\"feature_1\" =c(1,2,3),\"feature_2\" =c(0,4,1),\"feature_3\" =c(1,1,0)) a #> Day feature_1 feature_2 feature_3 #> 1 Day1 1 0 1 #> 2 Day2 2 4 1 #> 3 Day3 3 1 0 transpose_dataframe__(a) #> Day1 Day2 Day3 #> 1 1 2 3 #> 2 0 4 1 #> 3 1 1 0"}]
diff --git a/man/figures/.DS_Store b/man/figures/.DS_Store
index bbe3881..862bd8d 100644
Binary files a/man/figures/.DS_Store and b/man/figures/.DS_Store differ
diff --git a/man/figures/cheesecase.png b/man/figures/cheesecase.png
deleted file mode 100644
index d91f265..0000000
Binary files a/man/figures/cheesecase.png and /dev/null differ
diff --git a/man/figures/cheesecase1.png b/man/figures/cheesecase1.png
new file mode 100644
index 0000000..6d4d8aa
Binary files /dev/null and b/man/figures/cheesecase1.png differ
diff --git a/vignettes/cheese.Rmd b/vignettes/cheese.Rmd
index a26acd4..18967b8 100644
--- a/vignettes/cheese.Rmd
+++ b/vignettes/cheese.Rmd
@@ -98,3 +98,18 @@ gDashboard(cheesedata,
id_colname = c("GO_ID","KEGG_ID"),
id_type = c("GO","KEGG"))
```
+
+# Results
+
+For example, when we filter for Actinomycetota (Actinobacteria) as the functional group, we see that there are no edges connecting to group 10 and group 3- the clusters that have the most features associated with Actinomycetoa for Cheese sample A (Fig 1.3.A).
+
+
+
+ { width=100% }
+ *Fig 1: Dashboard showing Actinomycetota filtered network (A) with enrichment pattern for Cheese Sample-A (B) and Cheese Sample-C (C); Cluster pattern for Group 9, which also is enriched for Type IV secretion genes (D).*
+
+
+
+Looking at the pattern traces of these groups, (Fig 1.3.B), there is a peak in samples A4 (week 9) and A5 (week 13), which mirrors the 16S rRNA results of Saak et al. Since these two clusters do not have edges connecting them to other groups, this suggests that the Actinomycetoa features found in these groups follow distinct longitudinal succession patterns that are independent. When looking at Actinomycetoa within Cheese Sample C we see a different pattern. Groups 2 and 5, have the most features associated with Actinomycetoa, but they are highly connected to the other groups (Fig 1.3.A). From these results, we can hypothesize that though Actinomycetoa features are more abundant in later time points for both cheese samples, their dynamics are differentially influenced.
+
+The authors found that Type VI secretion was enriched in Pseudomonadota bacteria (specifically, Psychrobacter), and hypothesized this enrichment was due to the importance of physical species interactions that occur with this habitat. Using MolPad, we searched for other secretion systems associated genes, to understand their dynamics within the community. Focusing on KEGG annotated Type IV secretion genes, we found that Group 9 contained 12/13 of these genes. Within this group, features that cluster are ones that peak in Cheese sample C5 (week 13, Fig 1.3.D). This is also the most taxonomically diverse sample. From this, we can hypothesize that increased taxonomic diversity is also associated with increases in genes that are related to competitive species interactions.
\ No newline at end of file
diff --git a/vignettes/getstarted.Rmd b/vignettes/getstarted.Rmd
index b9ec236..5724477 100644
--- a/vignettes/getstarted.Rmd
+++ b/vignettes/getstarted.Rmd
@@ -45,6 +45,8 @@ library(MolPad)
load("/Users/hazelma/Documents/GitHub/MolPad_Data/multiomics_simulate.RData")
```
+
+
# Before Input
We recommend carefully reviewing your data and considering applying normalization (standardization, quantile normalization, log transformation, ...) and imputation (KNN, PMM, Mean/Median/Mode, ...) to address issues related to library size and missing data beforehead, otherwise the `pre_processing()` function will default to converting NAs to 0s.
@@ -57,6 +59,7 @@ We recommend carefully reviewing your data and considering applying normalizatio
* If it's hard to decide, you can always experiment with different methods and use cross-validation to compare their effectiveness. Let your choice be guided by domain knowledge and the specific requirements of your analysis to increase the quality of the following steps.
+
# Data Format
@@ -72,14 +75,17 @@ If your datasets are not multi-omics, you can simply apply it to our dashboard b
### Data Format and Pre-process
+
{ width=70% }
-
+
In the figure, A, B, and C can represent datasets from different sources or different aspects of measurements. Below are two examples of what the input data might look like and how to transform it into the required format through `pre_process()` modules.
+
+
#### If your input is a list of datasets:
Here is a list of datasets, each following the same format: they all have an ID column and identical column names.
@@ -105,6 +111,8 @@ data <- pre_process(fungal_dlist, typenameList = c('DataSource_A','DataSource_B'
head(data,10)
```
+
+
#### If your input is a single dataset:
Here is a dataset that already includes a labeled 'type' column:
@@ -121,6 +129,7 @@ x1 <- pre_process(x, replaceNA = TRUE,scale = TRUE)
head(x1,10)
```
+
# Choose Your Annotation
@@ -137,15 +146,18 @@ If your data has more than two layers of labels (like the cheese dataset), you c
Annotation_path_taxon <- gAnnotation(annotations,"phylum","class")
```
+
+
# Network Generation
To generate the network, we first scale and cluster the trajectories across the input molecular features. Trajectories are clustered using K-means, and a built-in elbow method is used to select the optimal K. We build network between clustered trajectories by adapting the GENIE3 algorithm. Specifically, the network is learned through a series of regressions, where each cluster centroid is predicted from the expression patterns of all the other cluster centroids using random forests. We choose random forests because of their potential to model interacting features and non-linearity without strong assumptions.
+
{ width=50% }
-
+
To determine which feature clusters are predictive of a given trajectory, the Mean Decrease Accuracy of a subset of top predictors whose expression directly influences the expression of the target cluster is taken as an indication of a putative link.
@@ -159,6 +171,8 @@ network_result <- cluster_result |>
gNetwork_view(network_result)
```
+
+
# Dashboard Navigation
@@ -173,46 +187,68 @@ gDashboard(data,
id_type = "KEGG")
```
+
{ width=70% }
+
Once you've launched the Shiny dashboard, you can zoom in or make adjustments to explore interesting findings within your data. To effectively navigate the dashboard generated by MolPad, you'll follow three main steps:
-### Choose a Primary Functional Annotation and Adjust Edge Density
+### 1. Choose a Primary Functional Annotation and Adjust Edge Density
Start by selecting a primary functional annotation from the available options. Then, fine-tune the edge density by adjusting the threshold value on the importance score. Nodes that turn bright green indicate clusters containing the most features related to the chosen functional annotation.
-### Explore the Network
+### 2. Explore the Network
Brushing on the network unveils patterns of taxonomic composition and typical trajectories. You can also zoom into specific taxonomic annotations by applying filters.
-### Investigate Feature Details and Related Function Annotations
+### 3. Investigate Feature Details and Related Function Annotations
Delve into the feature table to examine the specifics of the features within the selected clusters. Explore additional related function annotations using the drop-down options. Click on the provided links to access online information about the items of interest. The interface encourages iterative exploration, enabling you to conduct multiple steps to answer specific questions, such as comparing pattern distributions between two functions or identifying functionally important community members metabolizing a feature of interest.
+
+
+
{ width=100% }
+
# Related Visualization Outputs
The following visualizations are integrated into the dashboard, so there's no need to run function for each plot individually. We've linked a stacked bar plot and a ribbon plot with the network to facilitate focus and context explorations. This setup helps in detecting similar longitudinal patterns and understanding their relationships.
+
+
+
### Network Plot
+
+The network plot is a powerful visualization tool that displays the relationships between different groups or features within your data. In MolPad, the network plot helps to identify clusters of features that share similar patterns, revealing underlying connections that might not be immediately obvious. By visualizing these connections, users can gain a clearer understanding of the structure within their data, making it easier to pinpoint significant associations and trends.
+
```{r,echo=FALSE}
data("test_data")
make_the_graph(test_graphptw, test_network, 0.03, "Muscular System","nicely")
```
+
+
### Stack Bar Plot
+
+The stacked bar plot provides a detailed view of the composition of each cluster or group in your dataset. By stacking different categories on top of each other within a single bar, this plot allows for a quick comparison of relative proportions across multiple groups. This is particularly useful in microbiome experiments where understanding the distribution of taxa across different conditions or time points is crucial. The stacked bar plot makes it easy to see how these distributions change between experimental conditions, facilitating deeper insights.
+
```{r,echo=FALSE}
make_stackbar_plot(test_maindata, c("Group_1","Group_2","Group_3","Group_4"), c("hormonal proteins","structural proteins","enzymes","storage proteins","antibodies","transport proteins"))
```
+
+
+
### Ribbon Plot
+The ribbon plot is designed to visualize changes over time, making it an ideal tool for tracking longitudinal data. In MolPad, the ribbon plot illustrates how the abundance or expression levels of features vary across different time points or conditions. The smooth, flowing design of the ribbons helps to emphasize trends and patterns, enabling users to quickly identify periods of significant change or stability. This plot is particularly advantageous when comparing multiple groups, as it clearly shows overlapping trends and divergences, providing a comprehensive view of temporal dynamics in the data.
+
```{r,echo=FALSE}
make_line_plot(test_maindata, c("Group_1","Group_2","Group_3","Group_4"), c("hormonal proteins","structural proteins","enzymes","storage proteins","antibodies","transport proteins"))
diff --git a/vignettes/whymolpad.Rmd b/vignettes/whymolpad.Rmd
index c308dba..38cb484 100644
--- a/vignettes/whymolpad.Rmd
+++ b/vignettes/whymolpad.Rmd
@@ -24,11 +24,17 @@ library(dplyr)
# Why MolPad?
With the increasing multi-omics data and longitudinal designs integrated into microbiome experiments, there is a growing need to present the network, especially with complex variations across biological modalities. Network perspective helps detect the underlying co-occurrence among microbiome samples, allowing for high-level insights into the global structure. Yet when it comes to experimental data that records time series for 100,000 features, the network will collapse into some entangled clumps and therefore unable to read.
+
+
{ width=50% }
+
+
+# Improvements
+
For the aim of network interpretation, MolPad shows improvements in 3 important aspects:
* Capture groups of features that share longitudinal patterns and functional properties within the network.
@@ -39,6 +45,7 @@ This package especially facilitates the exploration of longitudinal molecular co
Unlike existing tools, MolPad stands out for its rich network interactivity and support for contextualization. It is also equipped with a beginner-friendly interface and self-contained data processing functions, making it accessible to users with limited programming experience.
+
# Key Features
@@ -54,25 +61,31 @@ We build network between clustered trajectories by adapting the GENIE3 algorithm
This package provides an interactive dashboard designed to help users explore data and uncover relationships within complex networks. By utilizing focus-plus-context visualization, the dashboard allows users to zoom in on specific details while keeping the broader dataset in view. This approach enables a deeper understanding of the data, making it easier to identify patterns and make informed decisions.
+
+
# What does it look like?
+Now, let’s see what you can get from the dashboard. We’ll start with an overview and then demonstrate how to discover patterns within your data.
+
+
### MolPad Dashboard Overview
+
{ width=100% }
-
+
Above is the overview of the MolPad Dashboard. To explore the dashboard effectively, you can start by following the sequence A-B-C-D. This approach will guide you through the cluster-level network, taxonomic-level bar plot, type-level line plot, and feature-level table. From there, you can modify detailed settings to identify and investigate special patterns within your data.
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### Discovering related patterns
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Here is a short example of discovering related patterns using the network plot: The shade of the edges represents the proximity of nodes. In the brushed area, Groups 1-7-8 (circled by solid black lines) and Groups 1-2 (circled by blue dashed lines) are strongly linked. For Groups 1, 7, and 8, the patterns form a W-shape with a noticeable peak at the same time section. Although Group 1 shows higher volatility compared to Group 2, both groups display a highly overlapping increasing trend.
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Note initial last column must exactly ID type. multiple data.frame format needs analyzed, also put list data.frame argument. case, variable type required generated next argument typenameList. typenameList vector strings. parameter used clarify source names data.frame, applicable input data list data.frame. default, set \"Dataset_1\", \"Dataset_2\", ..., etc. scale Logical; scale TRUE (default), standardize data.frame row base::scale. converts original value z-score. See also scale_by_row__(). autoColName string; autoColName -NULL (default), automatically set uniform column names data.frames. parameter applicable input data list data.frame. replaceNa Logical; replaceNa TRUE (default), replace NA 0.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pre-processing datasets — pre_process","text":"function returns long data.frame columns ID, value time_1, ..., value time_k, type.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Pre-processing datasets — pre_process","text":"consider two distinct scenarios application: one scenario, individuals collect several datasets various aspects instruments objects. example, might separately detecting lipids, metabolites, peptides specific soil sample. scenario, data uniform quality, can categorized larger groups exhibit significant differences. cases, pre_process() function serves valuable versatile tool. Yet, function optional generating dashboard. Users can perform processing long format matches required output. However, mindful number samples (timepoints) must greater 5 avoid potential errors subsequent prediction section.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pre-processing datasets — pre_process","text":"","code":"data(test_data) head(test_data, 10) #> ID T1 T2 T3 T4 T5 T6 T7 T8 T9 T10 type #> 1 1 1 0 0 1 1 0 0 1 6 6 type_A #> 2 2 6 0 0 0 0 3 1 0 2 1 type_A #> 3 3 1 0 0 0 2 0 0 2 2 1 type_A #> 4 4 4 5 3 3 7 2 1 1 0 0 type_A #> 5 5 4 3 NA 2 5 5 0 0 0 0 type_A #> 6 6 4 1 0 1 3 1 3 5 11 14 type_A #> 7 7 1 0 0 0 1 3 3 1 1 1 type_A #> 8 8 4 2 1 1 1 1 0 0 0 0 type_A #> 9 9 1 1 1 19 22 1 2 1 1 2 type_A #> 10 10 1 1 3 5 8 2 2 2 5 2 type_A a <- pre_process(test_data) head(a, 10) #> ID T1 T2 T3 T4 T5 T6 #> 1 1 -0.25354628 -0.6761234 -0.67612340 -0.25354628 -0.25354628 -0.6761234 #> 2 2 2.41458180 -0.6678631 -0.66786305 -0.66786305 -0.66786305 0.8733594 #> 3 3 0.21764288 -0.8705715 -0.87057150 -0.87057150 1.30585725 -0.8705715 #> 4 4 0.61658123 1.0569964 0.17616607 0.17616607 1.93782672 -0.2642491 #> 5 5 0.96186009 0.5038315 -0.87025436 0.04580286 1.41988870 1.4198887 #> 6 6 -0.06459959 -0.7105955 -0.92592741 -0.71059546 -0.27993154 -0.7105955 #> 7 7 -0.09086738 -0.9995412 -0.99954118 -0.99954118 -0.09086738 1.7264802 #> 8 8 2.40535118 0.8017837 0.00000000 0.00000000 0.00000000 0.0000000 #> 9 9 -0.50260633 -0.5026063 -0.50260633 1.70395805 2.07171878 -0.5026063 #> 10 10 -0.94019379 -0.9401938 -0.04477113 0.85065153 2.19378551 -0.4924825 #> T7 T8 T9 T10 type #> 1 -0.6761234 -0.25354628 1.85933936 1.85933936 type_A #> 2 -0.1541222 -0.66786305 0.35961857 -0.15412224 type_A #> 3 -0.8705715 1.30585725 1.30585725 0.21764288 type_A #> 4 -0.7046643 -0.70466426 -1.14507943 -1.14507943 type_A #> 5 -0.8702544 -0.87025436 -0.87025436 -0.87025436 type_A #> 6 -0.2799315 0.15073237 1.44272411 2.08871998 type_A #> 7 1.7264802 -0.09086738 -0.09086738 -0.09086738 type_A #> 8 -0.8017837 -0.80178373 -0.80178373 -0.80178373 type_A #> 9 -0.3800194 -0.50260633 -0.50260633 -0.38001942 type_A #> 10 -0.4924825 -0.49248246 0.85065153 -0.49248246 type_A"},{"path":"https://kaiyanm.github.io/MolPad/reference/reshape_for_make_functions.html","id":null,"dir":"Reference","previous_headings":"","what":"Reshape for 'make' functions — reshape_for_make_functions","title":"Reshape for 'make' functions — reshape_for_make_functions","text":"internal function produces three primary datasets dashboard intended \"make\" functions.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/reshape_for_make_functions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reshape for 'make' functions — reshape_for_make_functions","text":"","code":"reshape_for_make_functions(data, cluster, annotation, id_colname, id_type)"},{"path":"https://kaiyanm.github.io/MolPad/reference/reshape_for_make_functions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reshape for 'make' functions — reshape_for_make_functions","text":"data output pre_process() cluster output gClusters() annotation output gPathway() id_colname columns contain database IDs. id_type corresponding database names columns.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/reshape_for_make_functions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reshape for 'make' functions — reshape_for_make_functions","text":"","code":"data(test_data) l <- reshape_for_make_functions(test_data_processed, test_cluster, test_annotations_processed, id_colname = c(\"GO_ID\",\"KEGG_ID\"),id_type = c(\"GO\",\"KEGG\")) head(l[[1]]) #> ID cluster #> 1 1 Group_2 #> 2 2 Group_5 #> 3 3 Group_2 #> 4 4 Group_4 #> 5 5 Group_4 #> 6 6 Group_2 #> GO_ID #> 1 #> 2 #> 3 GO:0003674,GO:0003824,GO:0005575,GO:0006629,GO:0006644,GO:0006650,GO:0006655,GO:0006793,GO:0006796,GO:0008150,GO:0008152,GO:0008610,GO:0008654,GO:0008808,GO:0009058,GO:0009987,GO:0016020,GO:0016740,GO:0016772,GO:0016780,GO:0019637,GO:0030572,GO:0032048,GO:0032049,GO:0044237,GO:0044238,GO:0044249,GO:0044255,GO:0045017,GO:0046471,GO:0046474,GO:0046486,GO:0071704,GO:0090407,GO:1901576 #> 4 GO:0003674,GO:0003676,GO:0003723,GO:0003729,GO:0003824,GO:0004654,GO:0005488,GO:0005575,GO:0005622,GO:0005623,GO:0005737,GO:0006139,GO:0006401,GO:0006402,GO:0006725,GO:0006807,GO:0008150,GO:0008152,GO:0009056,GO:0009057,GO:0009892,GO:0009987,GO:0010468,GO:0010605,GO:0010629,GO:0016070,GO:0016071,GO:0016740,GO:0016772,GO:0016779,GO:0019222,GO:0019439,GO:0034641,GO:0034655,GO:0043170,GO:0044237,GO:0044238,GO:0044248,GO:0044260,GO:0044265,GO:0044270,GO:0044424,GO:0044464,GO:0046483,GO:0046700,GO:0048519,GO:0050789,GO:0060255,GO:0065007,GO:0071704,GO:0090304,GO:0097159,GO:1901360,GO:1901361,GO:1901363,GO:1901575 #> 5 #> 6 #> KEGG_ID Pathway taxonomic.scope #> 1 K07124 Integumentary System hormonal proteins #> 2 Skeletal System structural proteins #> 3 K06131 Muscular System enzymes #> 4 K00962 Nervous System contractile proteins #> 5 K02083 Endocrine System contractile proteins #> 6 Cardiovascular System structural proteins head(l[[2]]) #> # A tibble: 6 × 6 #> ID type cluster day value taxonomic.scope #>
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GO:1901575<\/a>\""},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":null,"dir":"Reference","previous_headings":"","what":"Pre-processing datasets — pre_process","title":"Pre-processing datasets — pre_process","text":"pre_process() function aids processing data inputs automatically establishes standardized format future use. allows two types data input: list datasets different sources long dataset containing specified last column type.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pre-processing datasets — pre_process","text":"","code":"pre_process( data, typenameList = NULL, replaceNA = TRUE, scale = TRUE, autoColName = \"Sec_\" )"},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pre-processing datasets — pre_process","text":"data data.frame describe feature one row. data contain variables ID value time_1, ..., value time_k, type extracting patterns across time. Note initial last column must exactly ID type. multiple data.frame format needs analyzed, also put list data.frame argument. case, variable type required generated next argument typenameList. typenameList vector strings. parameter used clarify source names data.frame, applicable input data list data.frame. default, set \"Dataset_1\", \"Dataset_2\", ..., etc. scale Logical; scale TRUE (default), standardize data.frame row base::scale. converts original value z-score. See also scale_by_row__(). autoColName string; autoColName -NULL (default), automatically set uniform column names data.frames. parameter applicable input data list data.frame. replaceNa Logical; replaceNa TRUE (default), replace NA 0.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pre-processing datasets — pre_process","text":"function returns long data.frame columns ID, value time_1, ..., value time_k, type.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Pre-processing datasets — pre_process","text":"consider two distinct scenarios application: one scenario, individuals collect several datasets various aspects instruments objects. example, might separately detecting lipids, metabolites, peptides specific soil sample. scenario, data uniform quality, can categorized larger groups exhibit significant differences. cases, pre_process() function serves valuable versatile tool. Yet, function optional generating dashboard. Users can perform processing long format matches required output. However, mindful number samples (timepoints) must greater 5 avoid potential errors subsequent prediction section.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/pre_process.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pre-processing datasets — pre_process","text":"","code":"data(test_data) head(test_data, 10) #> ID T1 T2 T3 T4 T5 T6 T7 T8 T9 T10 type #> 1 1 1 0 0 1 1 0 0 1 6 6 type_A #> 2 2 6 0 0 0 0 3 1 0 2 1 type_A #> 3 3 1 0 0 0 2 0 0 2 2 1 type_A #> 4 4 4 5 3 3 7 2 1 1 0 0 type_A #> 5 5 4 3 NA 2 5 5 0 0 0 0 type_A #> 6 6 4 1 0 1 3 1 3 5 11 14 type_A #> 7 7 1 0 0 0 1 3 3 1 1 1 type_A #> 8 8 4 2 1 1 1 1 0 0 0 0 type_A #> 9 9 1 1 1 19 22 1 2 1 1 2 type_A #> 10 10 1 1 3 5 8 2 2 2 5 2 type_A a <- pre_process(test_data) head(a, 10) #> ID T1 T2 T3 T4 T5 T6 #> 1 1 -0.25354628 -0.6761234 -0.67612340 -0.25354628 -0.25354628 -0.6761234 #> 2 2 2.41458180 -0.6678631 -0.66786305 -0.66786305 -0.66786305 0.8733594 #> 3 3 0.21764288 -0.8705715 -0.87057150 -0.87057150 1.30585725 -0.8705715 #> 4 4 0.61658123 1.0569964 0.17616607 0.17616607 1.93782672 -0.2642491 #> 5 5 0.96186009 0.5038315 -0.87025436 0.04580286 1.41988870 1.4198887 #> 6 6 -0.06459959 -0.7105955 -0.92592741 -0.71059546 -0.27993154 -0.7105955 #> 7 7 -0.09086738 -0.9995412 -0.99954118 -0.99954118 -0.09086738 1.7264802 #> 8 8 2.40535118 0.8017837 0.00000000 0.00000000 0.00000000 0.0000000 #> 9 9 -0.50260633 -0.5026063 -0.50260633 1.70395805 2.07171878 -0.5026063 #> 10 10 -0.94019379 -0.9401938 -0.04477113 0.85065153 2.19378551 -0.4924825 #> T7 T8 T9 T10 type #> 1 -0.6761234 -0.25354628 1.85933936 1.85933936 type_A #> 2 -0.1541222 -0.66786305 0.35961857 -0.15412224 type_A #> 3 -0.8705715 1.30585725 1.30585725 0.21764288 type_A #> 4 -0.7046643 -0.70466426 -1.14507943 -1.14507943 type_A #> 5 -0.8702544 -0.87025436 -0.87025436 -0.87025436 type_A #> 6 -0.2799315 0.15073237 1.44272411 2.08871998 type_A #> 7 1.7264802 -0.09086738 -0.09086738 -0.09086738 type_A #> 8 -0.8017837 -0.80178373 -0.80178373 -0.80178373 type_A #> 9 -0.3800194 -0.50260633 -0.50260633 -0.38001942 type_A #> 10 -0.4924825 -0.49248246 0.85065153 -0.49248246 type_A"},{"path":"https://kaiyanm.github.io/MolPad/reference/reshape_for_make_functions.html","id":null,"dir":"Reference","previous_headings":"","what":"Reshape for 'make' functions — reshape_for_make_functions","title":"Reshape for 'make' functions — reshape_for_make_functions","text":"internal function produces three primary datasets dashboard intended \"make\" functions.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/reshape_for_make_functions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reshape for 'make' functions — reshape_for_make_functions","text":"","code":"reshape_for_make_functions(data, cluster, annotation, id_colname, id_type)"},{"path":"https://kaiyanm.github.io/MolPad/reference/reshape_for_make_functions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reshape for 'make' functions — reshape_for_make_functions","text":"data output pre_process() cluster output gClusters() annotation output gPathway() id_colname columns contain database IDs. id_type corresponding database names columns.","code":""},{"path":"https://kaiyanm.github.io/MolPad/reference/reshape_for_make_functions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reshape for 'make' functions — reshape_for_make_functions","text":"","code":"data(test_data) l <- reshape_for_make_functions(test_data_processed, test_cluster, test_annotations_processed, id_colname = c(\"GO_ID\",\"KEGG_ID\"),id_type = c(\"GO\",\"KEGG\")) head(l[[1]]) #> ID cluster #> 1 1 Group_2 #> 2 2 Group_5 #> 3 3 Group_2 #> 4 4 Group_4 #> 5 5 Group_4 #> 6 6 Group_2 #> GO_ID #> 1 #> 2 #> 3 GO:0003674,GO:0003824,GO:0005575,GO:0006629,GO:0006644,GO:0006650,GO:0006655,GO:0006793,GO:0006796,GO:0008150,GO:0008152,GO:0008610,GO:0008654,GO:0008808,GO:0009058,GO:0009987,GO:0016020,GO:0016740,GO:0016772,GO:0016780,GO:0019637,GO:0030572,GO:0032048,GO:0032049,GO:0044237,GO:0044238,GO:0044249,GO:0044255,GO:0045017,GO:0046471,GO:0046474,GO:0046486,GO:0071704,GO:0090407,GO:1901576 #> 4 GO:0003674,GO:0003676,GO:0003723,GO:0003729,GO:0003824,GO:0004654,GO:0005488,GO:0005575,GO:0005622,GO:0005623,GO:0005737,GO:0006139,GO:0006401,GO:0006402,GO:0006725,GO:0006807,GO:0008150,GO:0008152,GO:0009056,GO:0009057,GO:0009892,GO:0009987,GO:0010468,GO:0010605,GO:0010629,GO:0016070,GO:0016071,GO:0016740,GO:0016772,GO:0016779,GO:0019222,GO:0019439,GO:0034641,GO:0034655,GO:0043170,GO:0044237,GO:0044238,GO:0044248,GO:0044260,GO:0044265,GO:0044270,GO:0044424,GO:0044464,GO:0046483,GO:0046700,GO:0048519,GO:0050789,GO:0060255,GO:0065007,GO:0071704,GO:0090304,GO:0097159,GO:1901360,GO:1901361,GO:1901363,GO:1901575 #> 5 #> 6 #> KEGG_ID Pathway taxonomic.scope #> 1 K07124 Integumentary System hormonal proteins #> 2 Skeletal System structural proteins #> 3 K06131 Muscular System enzymes #> 4 K00962 Nervous System contractile proteins #> 5 K02083 Endocrine System contractile proteins #> 6 Cardiovascular System structural proteins head(l[[2]]) #> # A tibble: 6 × 6 #> ID type cluster day value taxonomic.scope #>
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GO:1901575<\/a> #> 5
+ # Before Input We recommend carefully reviewing your data and considering applying normalization (standardization, quantile normalization, log transformation, ...) and imputation (KNN, PMM, Mean/Median/Mode, ...) to address issues related to library size and missing data beforehead, otherwise the `pre_processing()` function will default to converting NAs to 0s. @@ -57,6 +59,7 @@ We recommend carefully reviewing your data and considering applying normalizatio * If it's hard to decide, you can always experiment with different methods and use cross-validation to compare their effectiveness. Let your choice be guided by domain knowledge and the specific requirements of your analysis to increase the quality of the following steps. +
# Data Format @@ -72,14 +75,17 @@ If your datasets are not multi-omics, you can simply apply it to our dashboard b ### Data Format and Pre-process +
In the figure, A, B, and C can represent datasets from different sources or different aspects of measurements. Below are two examples of what the input data might look like and how to transform it into the required format through `pre_process()` modules. +
+ #### If your input is a list of datasets: Here is a list of datasets, each following the same format: they all have an ID column and identical column names. @@ -105,6 +111,8 @@ data <- pre_process(fungal_dlist, typenameList = c('DataSource_A','DataSource_B' head(data,10) ``` +
+ #### If your input is a single dataset: Here is a dataset that already includes a labeled 'type' column: @@ -121,6 +129,7 @@ x1 <- pre_process(x, replaceNA = TRUE,scale = TRUE) head(x1,10) ``` +
# Choose Your Annotation @@ -137,15 +146,18 @@ If your data has more than two layers of labels (like the cheese dataset), you c Annotation_path_taxon <- gAnnotation(annotations,"phylum","class") ``` +
+ # Network Generation To generate the network, we first scale and cluster the trajectories across the input molecular features. Trajectories are clustered using K-means, and a built-in elbow method is used to select the optimal K. We build network between clustered trajectories by adapting the GENIE3 algorithm. Specifically, the network is learned through a series of regressions, where each cluster centroid is predicted from the expression patterns of all the other cluster centroids using random forests. We choose random forests because of their potential to model interacting features and non-linearity without strong assumptions. +
To determine which feature clusters are predictive of a given trajectory, the Mean Decrease Accuracy of a subset of top predictors whose expression directly influences the expression of the target cluster is taken as an indication of a putative link. @@ -159,6 +171,8 @@ network_result <- cluster_result |> gNetwork_view(network_result) ``` +
+ # Dashboard Navigation @@ -173,46 +187,68 @@ gDashboard(data, id_type = "KEGG") ``` +
Once you've launched the Shiny dashboard, you can zoom in or make adjustments to explore interesting findings within your data. To effectively navigate the dashboard generated by MolPad, you'll follow three main steps: -### Choose a Primary Functional Annotation and Adjust Edge Density +### 1. Choose a Primary Functional Annotation and Adjust Edge Density Start by selecting a primary functional annotation from the available options. Then, fine-tune the edge density by adjusting the threshold value on the importance score. Nodes that turn bright green indicate clusters containing the most features related to the chosen functional annotation. -### Explore the Network +### 2. Explore the Network Brushing on the network unveils patterns of taxonomic composition and typical trajectories. You can also zoom into specific taxonomic annotations by applying filters. -### Investigate Feature Details and Related Function Annotations +### 3. Investigate Feature Details and Related Function Annotations Delve into the feature table to examine the specifics of the features within the selected clusters. Explore additional related function annotations using the drop-down options. Click on the provided links to access online information about the items of interest. The interface encourages iterative exploration, enabling you to conduct multiple steps to answer specific questions, such as comparing pattern distributions between two functions or identifying functionally important community members metabolizing a feature of interest. + +
+
# Related Visualization Outputs The following visualizations are integrated into the dashboard, so there's no need to run function for each plot individually. We've linked a stacked bar plot and a ribbon plot with the network to facilitate focus and context explorations. This setup helps in detecting similar longitudinal patterns and understanding their relationships. +
+ + ### Network Plot + +The network plot is a powerful visualization tool that displays the relationships between different groups or features within your data. In MolPad, the network plot helps to identify clusters of features that share similar patterns, revealing underlying connections that might not be immediately obvious. By visualizing these connections, users can gain a clearer understanding of the structure within their data, making it easier to pinpoint significant associations and trends. + ```{r,echo=FALSE} data("test_data") make_the_graph(test_graphptw, test_network, 0.03, "Muscular System","nicely") ``` +
+ ### Stack Bar Plot + +The stacked bar plot provides a detailed view of the composition of each cluster or group in your dataset. By stacking different categories on top of each other within a single bar, this plot allows for a quick comparison of relative proportions across multiple groups. This is particularly useful in microbiome experiments where understanding the distribution of taxa across different conditions or time points is crucial. The stacked bar plot makes it easy to see how these distributions change between experimental conditions, facilitating deeper insights. + ```{r,echo=FALSE} make_stackbar_plot(test_maindata, c("Group_1","Group_2","Group_3","Group_4"), c("hormonal proteins","structural proteins","enzymes","storage proteins","antibodies","transport proteins")) ``` +
+ + ### Ribbon Plot +The ribbon plot is designed to visualize changes over time, making it an ideal tool for tracking longitudinal data. In MolPad, the ribbon plot illustrates how the abundance or expression levels of features vary across different time points or conditions. The smooth, flowing design of the ribbons helps to emphasize trends and patterns, enabling users to quickly identify periods of significant change or stability. This plot is particularly advantageous when comparing multiple groups, as it clearly shows overlapping trends and divergences, providing a comprehensive view of temporal dynamics in the data. + ```{r,echo=FALSE} make_line_plot(test_maindata, c("Group_1","Group_2","Group_3","Group_4"), c("hormonal proteins","structural proteins","enzymes","storage proteins","antibodies","transport proteins")) diff --git a/vignettes/whymolpad.Rmd b/vignettes/whymolpad.Rmd index c308dba..38cb484 100644 --- a/vignettes/whymolpad.Rmd +++ b/vignettes/whymolpad.Rmd @@ -24,11 +24,17 @@ library(dplyr) # Why MolPad? With the increasing multi-omics data and longitudinal designs integrated into microbiome experiments, there is a growing need to present the network, especially with complex variations across biological modalities. Network perspective helps detect the underlying co-occurrence among microbiome samples, allowing for high-level insights into the global structure. Yet when it comes to experimental data that records time series for 100,000 features, the network will collapse into some entangled clumps and therefore unable to read. + +
+ +# Improvements + For the aim of network interpretation, MolPad shows improvements in 3 important aspects: * Capture groups of features that share longitudinal patterns and functional properties within the network. @@ -39,6 +45,7 @@ This package especially facilitates the exploration of longitudinal molecular co Unlike existing tools, MolPad stands out for its rich network interactivity and support for contextualization. It is also equipped with a beginner-friendly interface and self-contained data processing functions, making it accessible to users with limited programming experience. +
# Key Features @@ -54,25 +61,31 @@ We build network between clustered trajectories by adapting the GENIE3 algorithm This package provides an interactive dashboard designed to help users explore data and uncover relationships within complex networks. By utilizing focus-plus-context visualization, the dashboard allows users to zoom in on specific details while keeping the broader dataset in view. This approach enables a deeper understanding of the data, making it easier to identify patterns and make informed decisions. +
+ # What does it look like? +Now, let’s see what you can get from the dashboard. We’ll start with an overview and then demonstrate how to discover patterns within your data. + + ### MolPad Dashboard Overview +
Above is the overview of the MolPad Dashboard. To explore the dashboard effectively, you can start by following the sequence A-B-C-D. This approach will guide you through the cluster-level network, taxonomic-level bar plot, type-level line plot, and feature-level table. From there, you can modify detailed settings to identify and investigate special patterns within your data. - ### Discovering related patterns
Here is a short example of discovering related patterns using the network plot: The shade of the edges represents the proximity of nodes. In the brushed area, Groups 1-7-8 (circled by solid black lines) and Groups 1-2 (circled by blue dashed lines) are strongly linked. For Groups 1, 7, and 8, the patterns form a W-shape with a noticeable peak at the same time section. Although Group 1 shows higher volatility compared to Group 2, both groups display a highly overlapping increasing trend.