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 f566c65..bb3f97c 100644 Binary files a/docs/articles/cheese_files/figure-html/unnamed-chunk-8-1.png and b/docs/articles/cheese_files/figure-html/unnamed-chunk-8-1.png differ diff --git a/docs/articles/data_input.html b/docs/articles/data_input.html index 9764ce5..8a2528f 100644 --- a/docs/articles/data_input.html +++ b/docs/articles/data_input.html @@ -99,18 +99,18 @@

If Your Datasets are Multi-Omics:

To achieve this, simply run the pre_process() function, which will yield the standard input format as demonstrated below.

-
#>   ID      Day_1       Day_2      Day_3      Day_4      Day_5      Day_6
-#> 1  1  0.5817621  0.81234410 -0.5179464 -1.8248280 -0.3686574  0.7726121
-#> 2  2 -0.2293004 -0.07349086 -1.4694419 -0.4497169  2.0161773 -0.6154054
-#> 3  3 -0.4455635 -0.68140943  1.0693325 -0.7090084  0.4993759  1.5274639
-#> 4  4 -1.0958002  0.59286365  0.3753236 -0.8386137 -0.8188066 -1.1248000
-#> 5  5  0.4431089 -0.96834799 -0.8497481 -2.1996118 -1.1090027 -1.1560818
-#>        Day_7       Day_8       type
-#> 1 -0.7790768 -0.12981708    peptide
-#> 2  0.7483253 -0.18939408    peptide
-#> 3  1.3873813  0.05163392    peptide
-#> 4 -0.3007957  1.92998318      lipid
-#> 5 -1.7556661 -0.04460992 metabolite
+
#>   ID      Day_1      Day_2       Day_3      Day_4       Day_5       Day_6
+#> 1  1  1.2771018 -0.1577530  1.34603584  0.4794459 -0.03962597 -1.93681293
+#> 2  2 -1.0881420  0.9066818 -1.20882050 -0.2147851  0.12668836 -0.09973850
+#> 3  3  1.7394820 -0.1523492  0.52824764  1.2019573 -2.26269035 -0.65950722
+#> 4  4  0.7373811  0.5520307 -0.03555728  0.1717543  0.23699563 -1.66964070
+#> 5  5 -1.0674263  0.2494316  0.01451995 -0.6380025 -0.72307189 -0.05581738
+#>        Day_7      Day_8       type
+#> 1  0.8964847  1.1599215    peptide
+#> 2  1.4936596  0.9599760    peptide
+#> 3 -1.4455547  0.2371363    peptide
+#> 4  0.0823294 -1.5614780      lipid
+#> 5  0.8429670  0.6671823 metabolite

If Your Datasets are NOT Multi-Omics: diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml index 272065d..9a8579a 100644 --- a/docs/pkgdown.yml +++ b/docs/pkgdown.yml @@ -6,7 +6,7 @@ articles: cheese: cheese.html data_input: data_input.html navigate: navigate.html -last_built: 2024-06-01T13:36Z +last_built: 2024-06-01T14:12Z urls: reference: https://kaiyanm.github.io/MolPad/reference article: https://kaiyanm.github.io/MolPad/articles diff --git a/docs/reference/gClusters-1.png b/docs/reference/gClusters-1.png index 6b76e6c..05c1425 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 b9a7126..17f3f64 100644 --- a/docs/reference/gClusters.html +++ b/docs/reference/gClusters.html @@ -98,70 +98,70 @@

Examplesreslist <- gClusters(test_data_processed) # k-means result reslist[[1]] -#> K-means clustering with 20 clusters of sizes 5, 1, 6, 3, 3, 7, 2, 3, 7, 10, 10, 8, 3, 4, 5, 6, 6, 2, 4, 5 +#> K-means clustering with 20 clusters of sizes 3, 5, 9, 2, 4, 2, 4, 4, 6, 3, 6, 8, 10, 4, 10, 5, 5, 4, 4, 2 #> #> Cluster means: -#> T1 T2 T3 T4 T5 T6 -#> 1 1.21072292 -0.80608521 -0.80608521 1.09537064 0.5441275 1.2235684 -#> 2 -0.22485951 -1.12429753 1.57401655 0.22485951 0.6745785 0.6745785 -#> 3 1.03050384 -0.62595927 0.30371539 1.08219722 1.2374965 -1.0856661 -#> 4 0.06782472 -0.71014462 -0.28353947 -0.64750373 -0.8105857 1.0715170 -#> 5 2.10403422 0.98921197 -0.78947202 -0.03376065 -0.2091723 0.1416510 -#> 6 1.87029938 -0.31028785 -0.65360764 -0.50294192 -0.2705164 1.6221851 -#> 7 1.40480405 -0.56205578 -0.56205578 -0.81170928 -0.8117093 0.1565366 -#> 8 -0.09187847 -0.87257293 -0.87257293 -0.87257293 -0.5696817 2.0566833 -#> 9 2.63997329 -0.28833058 -0.41045576 -0.49507902 -0.5660167 0.1966284 -#> 10 -0.19020083 -0.69654961 -0.66836949 -0.46576631 -0.3851487 -0.5590421 -#> 11 -0.27715339 -0.39760069 -0.06466866 1.39954070 2.1316423 -0.2806884 -#> 12 0.11559709 -0.19000668 -0.25629794 -0.14876974 -0.4383348 2.5830148 -#> 13 0.89463785 0.01698143 -0.57023879 -1.18158339 -1.1815834 1.1882480 -#> 14 1.45173820 0.26236463 1.31600581 -0.25310115 0.2004547 -1.1238942 -#> 15 -0.81355388 -0.84696982 -0.32573017 1.12185339 2.0955812 -0.5620731 -#> 16 2.43423843 -0.09945335 -0.61374870 -0.19587390 -0.5847831 -0.7182670 -#> 17 0.80396722 0.07346897 -0.82246179 -0.68840032 -0.4999813 -0.7273667 -#> 18 -0.27847690 -0.82258408 -0.82258408 -0.82258408 1.2338761 -0.8225841 -#> 19 0.33814100 1.48152270 0.43414431 0.74731317 0.8107827 -0.5234016 -#> 20 0.65431739 -0.06991189 0.32724119 0.23861006 1.1603437 1.4951461 -#> T7 T8 T9 T10 -#> 1 -0.453071656 -0.749390535 -0.7493905 -0.50976633 -#> 2 -1.574016547 0.224859507 0.6745785 -1.12429753 -#> 3 -1.182978501 -0.350257808 -0.2882471 -0.12080420 -#> 4 -0.973667763 -0.383980588 1.0715170 1.59856325 -#> 5 -0.651845097 -0.246956932 -0.6518451 -0.65184510 -#> 6 -0.230377035 -0.386729172 -0.5109058 -0.62711859 -#> 7 0.655843552 1.404804050 -0.8117093 -0.06274879 -#> 8 0.882337606 0.086536293 0.1268609 0.12686085 -#> 9 -0.085181658 -0.358316791 -0.2769664 -0.35625482 -#> 10 -0.566522777 0.002653353 1.7391798 1.78976662 -#> 11 -0.548494719 -0.646992821 -0.6639215 -0.65166279 -#> 12 -0.006327004 -0.327677972 -0.6655989 -0.66559889 -#> 13 0.894637851 -0.836550610 0.4134369 0.36201421 -#> 14 -0.987867401 -0.340013670 -0.3308568 -0.19483004 -#> 15 -0.651292750 -0.498839228 0.4244601 0.05656427 -#> 16 -0.718266991 -0.312680502 0.2816637 0.52717140 -#> 17 -0.745878993 -0.092802077 0.7897672 1.90968781 -#> 18 -0.822584085 1.233876127 1.2338761 0.68976894 -#> 19 -1.000948275 -0.369116018 -0.9592190 -0.95921900 -#> 20 -0.892735465 -0.964398012 -0.9125525 -1.03606056 +#> T1 T2 T3 T4 T5 T6 +#> 1 0.82771047 1.3203679 -0.09261332 1.2325986 0.41991760 -0.9576634 +#> 2 1.85387177 0.4438755 -0.93320876 -0.4074915 -0.66336413 -0.7901373 +#> 3 2.65728988 -0.2630667 -0.41661365 -0.3609509 -0.53324605 -0.2963200 +#> 4 -0.27847690 -0.8225841 -0.82258408 -0.8225841 1.23387613 -0.8225841 +#> 5 1.45173820 0.2623646 1.31600581 -0.2531012 0.20045467 -1.1238942 +#> 6 0.17892471 1.2615717 0.82115649 0.3899368 1.27076714 -0.2614905 +#> 7 0.28399859 -0.5078664 -0.94828153 -0.5778829 -0.35071448 1.9118354 +#> 8 1.40228407 -0.7483275 -0.74832749 1.2580937 0.56903975 1.0479424 +#> 9 0.50778790 -0.2456428 0.53503708 0.2363183 1.07938288 1.3583848 +#> 10 1.91265369 0.7509656 -0.66969596 0.1328812 -0.04253041 0.7600468 +#> 11 2.09193480 -0.3332029 -0.50786563 -0.5579659 -0.55796593 1.5455977 +#> 12 0.11559709 -0.1900067 -0.25629794 -0.1487697 -0.43833477 2.5830148 +#> 13 0.12802387 -0.5563805 -0.74178584 -0.5385731 -0.36268212 -0.6793574 +#> 14 1.53284649 -0.5849948 -0.58499482 -0.9095554 -0.50281082 1.1583662 +#> 15 -0.27715339 -0.3976007 -0.06466866 1.3995407 2.13164226 -0.2806884 +#> 16 0.93892125 -0.8627824 0.43887930 1.0009533 1.37336455 -1.0423264 +#> 17 -0.81355388 -0.8469698 -0.32573017 1.1218534 2.09558116 -0.5620731 +#> 18 0.58032965 -0.2118479 -0.40586961 -0.7749068 -0.64380111 -0.2632270 +#> 19 0.02192608 -0.7867376 -0.66442613 -0.8008892 -0.92320072 1.3488860 +#> 20 -0.57705193 -0.6891985 -0.57705193 -0.2304106 -0.40373126 -0.3425572 +#> T7 T8 T9 T10 +#> 1 -1.160107459 -0.29505740 -0.64757654 -0.64757654 +#> 2 -0.590414464 0.58744854 -0.02801822 0.52743856 +#> 3 -0.456819515 -0.31750042 -0.04259251 0.02981989 +#> 4 -0.822584085 1.23387613 1.23387613 0.68976894 +#> 5 -0.987867401 -0.34001367 -0.33085684 -0.19483004 +#> 6 -0.912917717 -0.48169804 -1.13312530 -1.13312530 +#> 7 0.932633401 -0.72111308 -0.30660759 0.28399859 +#> 8 -0.677459149 -0.67745915 -0.67745915 -0.74832749 +#> 9 -1.006282312 -0.76618843 -0.64803067 -1.05076672 +#> 10 -0.485203253 -0.48520325 -0.93695720 -0.93695720 +#> 11 -0.425513174 -0.55796593 -0.30571480 -0.39133827 +#> 12 -0.006327004 -0.32767797 -0.66559889 -0.66559889 +#> 13 -0.686838073 0.01492538 1.53688464 1.88578311 +#> 14 1.079820690 -0.09723346 -0.38162251 -0.70982157 +#> 15 -0.548494719 -0.64699282 -0.66392148 -0.65166279 +#> 16 -1.159101258 -0.34588853 -0.27147562 -0.07054420 +#> 17 -0.651292750 -0.49883923 0.42446013 0.05656427 +#> 18 -0.686280126 -0.30973158 0.40750677 2.30782771 +#> 19 -0.281881327 -0.04995115 1.19752435 0.93874976 +#> 20 -0.342557190 -0.35275874 2.32859320 1.18672416 #> #> Clustering vector: #> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 -#> 10 9 18 19 20 10 8 5 11 15 3 17 17 9 3 15 12 10 14 16 +#> 13 11 4 6 9 13 7 10 15 17 16 18 18 3 1 17 12 20 5 3 #> 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 -#> 6 10 12 6 9 15 1 16 12 11 6 11 19 18 11 20 3 17 10 19 +#> 10 13 12 11 3 17 8 2 12 15 14 15 6 4 15 9 16 13 20 1 #> 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 -#> 12 6 10 10 1 10 12 20 6 12 16 9 13 17 12 7 8 11 16 11 +#> 12 11 13 13 7 13 12 9 11 12 3 14 7 13 12 14 7 15 3 15 #> 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 -#> 3 20 1 17 2 6 9 1 14 14 4 15 12 5 17 11 19 11 3 1 +#> 16 9 8 2 9 11 3 8 5 5 18 17 12 10 18 15 1 15 16 8 #> 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 -#> 3 4 7 8 4 6 14 9 13 16 11 13 11 5 10 20 15 9 10 16 +#> 16 19 2 19 19 11 5 3 19 3 15 14 15 2 13 9 17 3 13 2 #> #> Within cluster sum of squares by cluster: -#> [1] 8.2663203 0.0000000 11.7178543 4.1616532 4.7117460 8.1476433 -#> [7] 3.9721000 4.0041862 5.8486193 8.0515257 5.9461649 8.0412334 -#> [13] 5.6033175 9.9418120 3.4617088 4.9244324 9.4996169 0.9921913 -#> [19] 8.1365152 5.9394323 -#> (between_SS / total_SS = 84.4 %) +#> [1] 5.0357352 11.4608785 7.9326275 0.9921913 9.9418120 2.4669004 +#> [7] 8.3425688 3.8879014 13.2343206 4.5166634 3.0260293 8.0412334 +#> [13] 8.5051702 6.8762885 5.9461649 8.3255313 3.4617088 5.2412577 +#> [19] 8.9144667 0.9062989 +#> (between_SS / total_SS = 83.6 %) #> #> Available components: #> diff --git a/docs/reference/gNetwork-1.png b/docs/reference/gNetwork-1.png index cbcf8ce..9c2017f 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 9ccf051..442291e 100644 --- a/docs/reference/gNetwork.html +++ b/docs/reference/gNetwork.html @@ -78,13 +78,13 @@

Examples
data(test_data)
 networkres <- gNetwork(test_cluster, ntop = 3)
 head(networkres)
-#>       weight IncNodePurity var_names    from
-#> 1  0.6261361     0.7938505   Group_3 Group_1
-#> 2  0.3791254     1.0400303   Group_5 Group_1
-#> 3 -2.5774246     0.6075788   Group_4 Group_1
-#> 4  5.5229722     0.9616367   Group_5 Group_2
-#> 5  5.2202034     1.0532797   Group_4 Group_2
-#> 6  1.5106624     1.1098355   Group_1 Group_2
+#>        weight IncNodePurity var_names    from
+#> 1  2.28519855     0.9196328   Group_3 Group_1
+#> 2  0.01325098     1.0647271   Group_5 Group_1
+#> 3 -1.52876440     0.6472361   Group_4 Group_1
+#> 4  5.74000483     1.0925108   Group_4 Group_2
+#> 5  5.40615345     0.9142736   Group_5 Group_2
+#> 6  1.02589705     1.2726073   Group_1 Group_2
 gNetwork_view(networkres)
 
 
diff --git a/docs/search.json b/docs/search.json
index b380221..5c099a1 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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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/data_input.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Data Input: Multi-Omics/Single-Omics","text":"visualization pipeline begins optional pre-processing module offers built-functions streamline data preparation. Depending nature datasets, two primary conditions consider:","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/data_input.html","id":"if-your-datasets-are-multi-omics","dir":"Articles","previous_headings":"","what":"If Your Datasets are Multi-Omics:","title":"Data Input: Multi-Omics/Single-Omics","text":"scenario, assume provide list data tables, collected different omics type. example, might datasets peptides, metabolites, lipids. recommend carefully reviewing data considering applying quantile normalization KNN imputation address issues related library size missing data. achieve , simply run pre_process() function, yield standard input format demonstrated .","code":"#>   ID      Day_1       Day_2      Day_3      Day_4      Day_5      Day_6 #> 1  1  0.5817621  0.81234410 -0.5179464 -1.8248280 -0.3686574  0.7726121 #> 2  2 -0.2293004 -0.07349086 -1.4694419 -0.4497169  2.0161773 -0.6154054 #> 3  3 -0.4455635 -0.68140943  1.0693325 -0.7090084  0.4993759  1.5274639 #> 4  4 -1.0958002  0.59286365  0.3753236 -0.8386137 -0.8188066 -1.1248000 #> 5  5  0.4431089 -0.96834799 -0.8497481 -2.1996118 -1.1090027 -1.1560818 #>        Day_7       Day_8       type #> 1 -0.7790768 -0.12981708    peptide #> 2  0.7483253 -0.18939408    peptide #> 3  1.3873813  0.05163392    peptide #> 4 -0.3007957  1.92998318      lipid #> 5 -1.7556661 -0.04460992 metabolite"},{"path":"https://kaiyanm.github.io/MolPad/articles/data_input.html","id":"if-your-datasets-are-not-multi-omics","dir":"Articles","previous_headings":"","what":"If Your Datasets are NOT Multi-Omics:","title":"Data Input: Multi-Omics/Single-Omics","text":"can still utilize dashboard ensuring data inputs reformatted standard longitudinal format. datasets may multi-omics, can manually assign type column category label describe major groups data. case study, utilized “Kingdom” type label column cheese data.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/data_input.html","id":"choose-your-annotation","dir":"Articles","previous_headings":"","what":"Choose Your Annotation","title":"Data Input: Multi-Omics/Single-Omics","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.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/navigate.html","id":"choose-a-primary-functional-annotation-and-adjust-edge-density","dir":"Articles","previous_headings":"","what":"1. Choose a Primary Functional Annotation and Adjust Edge Density","title":"Network Navigation: Key Steps","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/navigate.html","id":"explore-the-network","dir":"Articles","previous_headings":"","what":"2. Explore the Network","title":"Network Navigation: Key Steps","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/navigate.html","id":"investigate-feature-details-and-related-function-annotations","dir":"Articles","previous_headings":"","what":"3. Investigate Feature Details and Related Function Annotations","title":"Network Navigation: Key Steps","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/navigate.html","id":"examples","dir":"Articles","previous_headings":"","what":"Examples","title":"Network Navigation: Key Steps","text":"’s illustrative example showcasing discover related patterns using network plot: following steps, can easily leverage MolPad dashboard gain insights data, identify significant patterns, make informed decisions based network analysis.","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 5, 1, 6, 3, 3, 7, 2, 3, 7, 10, 10, 8, 3, 4, 5, 6, 6, 2, 4, 5 #>  #> Cluster means: #>             T1          T2          T3          T4         T5         T6 #> 1   1.21072292 -0.80608521 -0.80608521  1.09537064  0.5441275  1.2235684 #> 2  -0.22485951 -1.12429753  1.57401655  0.22485951  0.6745785  0.6745785 #> 3   1.03050384 -0.62595927  0.30371539  1.08219722  1.2374965 -1.0856661 #> 4   0.06782472 -0.71014462 -0.28353947 -0.64750373 -0.8105857  1.0715170 #> 5   2.10403422  0.98921197 -0.78947202 -0.03376065 -0.2091723  0.1416510 #> 6   1.87029938 -0.31028785 -0.65360764 -0.50294192 -0.2705164  1.6221851 #> 7   1.40480405 -0.56205578 -0.56205578 -0.81170928 -0.8117093  0.1565366 #> 8  -0.09187847 -0.87257293 -0.87257293 -0.87257293 -0.5696817  2.0566833 #> 9   2.63997329 -0.28833058 -0.41045576 -0.49507902 -0.5660167  0.1966284 #> 10 -0.19020083 -0.69654961 -0.66836949 -0.46576631 -0.3851487 -0.5590421 #> 11 -0.27715339 -0.39760069 -0.06466866  1.39954070  2.1316423 -0.2806884 #> 12  0.11559709 -0.19000668 -0.25629794 -0.14876974 -0.4383348  2.5830148 #> 13  0.89463785  0.01698143 -0.57023879 -1.18158339 -1.1815834  1.1882480 #> 14  1.45173820  0.26236463  1.31600581 -0.25310115  0.2004547 -1.1238942 #> 15 -0.81355388 -0.84696982 -0.32573017  1.12185339  2.0955812 -0.5620731 #> 16  2.43423843 -0.09945335 -0.61374870 -0.19587390 -0.5847831 -0.7182670 #> 17  0.80396722  0.07346897 -0.82246179 -0.68840032 -0.4999813 -0.7273667 #> 18 -0.27847690 -0.82258408 -0.82258408 -0.82258408  1.2338761 -0.8225841 #> 19  0.33814100  1.48152270  0.43414431  0.74731317  0.8107827 -0.5234016 #> 20  0.65431739 -0.06991189  0.32724119  0.23861006  1.1603437  1.4951461 #>              T7           T8         T9         T10 #> 1  -0.453071656 -0.749390535 -0.7493905 -0.50976633 #> 2  -1.574016547  0.224859507  0.6745785 -1.12429753 #> 3  -1.182978501 -0.350257808 -0.2882471 -0.12080420 #> 4  -0.973667763 -0.383980588  1.0715170  1.59856325 #> 5  -0.651845097 -0.246956932 -0.6518451 -0.65184510 #> 6  -0.230377035 -0.386729172 -0.5109058 -0.62711859 #> 7   0.655843552  1.404804050 -0.8117093 -0.06274879 #> 8   0.882337606  0.086536293  0.1268609  0.12686085 #> 9  -0.085181658 -0.358316791 -0.2769664 -0.35625482 #> 10 -0.566522777  0.002653353  1.7391798  1.78976662 #> 11 -0.548494719 -0.646992821 -0.6639215 -0.65166279 #> 12 -0.006327004 -0.327677972 -0.6655989 -0.66559889 #> 13  0.894637851 -0.836550610  0.4134369  0.36201421 #> 14 -0.987867401 -0.340013670 -0.3308568 -0.19483004 #> 15 -0.651292750 -0.498839228  0.4244601  0.05656427 #> 16 -0.718266991 -0.312680502  0.2816637  0.52717140 #> 17 -0.745878993 -0.092802077  0.7897672  1.90968781 #> 18 -0.822584085  1.233876127  1.2338761  0.68976894 #> 19 -1.000948275 -0.369116018 -0.9592190 -0.95921900 #> 20 -0.892735465 -0.964398012 -0.9125525 -1.03606056 #>  #> Clustering vector: #>   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18  19  20  #>  10   9  18  19  20  10   8   5  11  15   3  17  17   9   3  15  12  10  14  16  #>  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37  38  39  40  #>   6  10  12   6   9  15   1  16  12  11   6  11  19  18  11  20   3  17  10  19  #>  41  42  43  44  45  46  47  48  49  50  51  52  53  54  55  56  57  58  59  60  #>  12   6  10  10   1  10  12  20   6  12  16   9  13  17  12   7   8  11  16  11  #>  61  62  63  64  65  66  67  68  69  70  71  72  73  74  75  76  77  78  79  80  #>   3  20   1  17   2   6   9   1  14  14   4  15  12   5  17  11  19  11   3   1  #>  81  82  83  84  85  86  87  88  89  90  91  92  93  94  95  96  97  98  99 100  #>   3   4   7   8   4   6  14   9  13  16  11  13  11   5  10  20  15   9  10  16  #>  #> Within cluster sum of squares by cluster: #>  [1]  8.2663203  0.0000000 11.7178543  4.1616532  4.7117460  8.1476433 #>  [7]  3.9721000  4.0041862  5.8486193  8.0515257  5.9461649  8.0412334 #> [13]  5.6033175  9.9418120  3.4617088  4.9244324  9.4996169  0.9921913 #> [19]  8.1365152  5.9394323 #>  (between_SS / total_SS =  84.4 %) #>  #> 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  0.6261361     0.7938505   Group_3 Group_1 #> 2  0.3791254     1.0400303   Group_5 Group_1 #> 3 -2.5774246     0.6075788   Group_4 Group_1 #> 4  5.5229722     0.9616367   Group_5 Group_2 #> 5  5.2202034     1.0532797   Group_4 Group_2 #> 6  1.5106624     1.1098355   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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         #>                                                                 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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     #> [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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         #>                                                                 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”). 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/data_input.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Data Input: Multi-Omics/Single-Omics","text":"visualization pipeline begins optional pre-processing module offers built-functions streamline data preparation. Depending nature datasets, two primary conditions consider:","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/data_input.html","id":"if-your-datasets-are-multi-omics","dir":"Articles","previous_headings":"","what":"If Your Datasets are Multi-Omics:","title":"Data Input: Multi-Omics/Single-Omics","text":"scenario, assume provide list data tables, collected different omics type. example, might datasets peptides, metabolites, lipids. recommend carefully reviewing data considering applying quantile normalization KNN imputation address issues related library size missing data. achieve , simply run pre_process() function, yield standard input format demonstrated .","code":"#>   ID      Day_1      Day_2       Day_3      Day_4       Day_5       Day_6 #> 1  1  1.2771018 -0.1577530  1.34603584  0.4794459 -0.03962597 -1.93681293 #> 2  2 -1.0881420  0.9066818 -1.20882050 -0.2147851  0.12668836 -0.09973850 #> 3  3  1.7394820 -0.1523492  0.52824764  1.2019573 -2.26269035 -0.65950722 #> 4  4  0.7373811  0.5520307 -0.03555728  0.1717543  0.23699563 -1.66964070 #> 5  5 -1.0674263  0.2494316  0.01451995 -0.6380025 -0.72307189 -0.05581738 #>        Day_7      Day_8       type #> 1  0.8964847  1.1599215    peptide #> 2  1.4936596  0.9599760    peptide #> 3 -1.4455547  0.2371363    peptide #> 4  0.0823294 -1.5614780      lipid #> 5  0.8429670  0.6671823 metabolite"},{"path":"https://kaiyanm.github.io/MolPad/articles/data_input.html","id":"if-your-datasets-are-not-multi-omics","dir":"Articles","previous_headings":"","what":"If Your Datasets are NOT Multi-Omics:","title":"Data Input: Multi-Omics/Single-Omics","text":"can still utilize dashboard ensuring data inputs reformatted standard longitudinal format. datasets may multi-omics, can manually assign type column category label describe major groups data. case study, utilized “Kingdom” type label column cheese data.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/data_input.html","id":"choose-your-annotation","dir":"Articles","previous_headings":"","what":"Choose Your Annotation","title":"Data Input: Multi-Omics/Single-Omics","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.","code":""},{"path":"https://kaiyanm.github.io/MolPad/articles/navigate.html","id":"choose-a-primary-functional-annotation-and-adjust-edge-density","dir":"Articles","previous_headings":"","what":"1. Choose a Primary Functional Annotation and Adjust Edge Density","title":"Network Navigation: Key Steps","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/navigate.html","id":"explore-the-network","dir":"Articles","previous_headings":"","what":"2. Explore the Network","title":"Network Navigation: Key Steps","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/navigate.html","id":"investigate-feature-details-and-related-function-annotations","dir":"Articles","previous_headings":"","what":"3. Investigate Feature Details and Related Function Annotations","title":"Network Navigation: Key Steps","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/navigate.html","id":"examples","dir":"Articles","previous_headings":"","what":"Examples","title":"Network Navigation: Key Steps","text":"’s illustrative example showcasing discover related patterns using network plot: following steps, can easily leverage MolPad dashboard gain insights data, identify significant patterns, make informed decisions based network analysis.","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 3, 5, 9, 2, 4, 2, 4, 4, 6, 3, 6, 8, 10, 4, 10, 5, 5, 4, 4, 2 #>  #> Cluster means: #>             T1         T2          T3         T4          T5         T6 #> 1   0.82771047  1.3203679 -0.09261332  1.2325986  0.41991760 -0.9576634 #> 2   1.85387177  0.4438755 -0.93320876 -0.4074915 -0.66336413 -0.7901373 #> 3   2.65728988 -0.2630667 -0.41661365 -0.3609509 -0.53324605 -0.2963200 #> 4  -0.27847690 -0.8225841 -0.82258408 -0.8225841  1.23387613 -0.8225841 #> 5   1.45173820  0.2623646  1.31600581 -0.2531012  0.20045467 -1.1238942 #> 6   0.17892471  1.2615717  0.82115649  0.3899368  1.27076714 -0.2614905 #> 7   0.28399859 -0.5078664 -0.94828153 -0.5778829 -0.35071448  1.9118354 #> 8   1.40228407 -0.7483275 -0.74832749  1.2580937  0.56903975  1.0479424 #> 9   0.50778790 -0.2456428  0.53503708  0.2363183  1.07938288  1.3583848 #> 10  1.91265369  0.7509656 -0.66969596  0.1328812 -0.04253041  0.7600468 #> 11  2.09193480 -0.3332029 -0.50786563 -0.5579659 -0.55796593  1.5455977 #> 12  0.11559709 -0.1900067 -0.25629794 -0.1487697 -0.43833477  2.5830148 #> 13  0.12802387 -0.5563805 -0.74178584 -0.5385731 -0.36268212 -0.6793574 #> 14  1.53284649 -0.5849948 -0.58499482 -0.9095554 -0.50281082  1.1583662 #> 15 -0.27715339 -0.3976007 -0.06466866  1.3995407  2.13164226 -0.2806884 #> 16  0.93892125 -0.8627824  0.43887930  1.0009533  1.37336455 -1.0423264 #> 17 -0.81355388 -0.8469698 -0.32573017  1.1218534  2.09558116 -0.5620731 #> 18  0.58032965 -0.2118479 -0.40586961 -0.7749068 -0.64380111 -0.2632270 #> 19  0.02192608 -0.7867376 -0.66442613 -0.8008892 -0.92320072  1.3488860 #> 20 -0.57705193 -0.6891985 -0.57705193 -0.2304106 -0.40373126 -0.3425572 #>              T7          T8          T9         T10 #> 1  -1.160107459 -0.29505740 -0.64757654 -0.64757654 #> 2  -0.590414464  0.58744854 -0.02801822  0.52743856 #> 3  -0.456819515 -0.31750042 -0.04259251  0.02981989 #> 4  -0.822584085  1.23387613  1.23387613  0.68976894 #> 5  -0.987867401 -0.34001367 -0.33085684 -0.19483004 #> 6  -0.912917717 -0.48169804 -1.13312530 -1.13312530 #> 7   0.932633401 -0.72111308 -0.30660759  0.28399859 #> 8  -0.677459149 -0.67745915 -0.67745915 -0.74832749 #> 9  -1.006282312 -0.76618843 -0.64803067 -1.05076672 #> 10 -0.485203253 -0.48520325 -0.93695720 -0.93695720 #> 11 -0.425513174 -0.55796593 -0.30571480 -0.39133827 #> 12 -0.006327004 -0.32767797 -0.66559889 -0.66559889 #> 13 -0.686838073  0.01492538  1.53688464  1.88578311 #> 14  1.079820690 -0.09723346 -0.38162251 -0.70982157 #> 15 -0.548494719 -0.64699282 -0.66392148 -0.65166279 #> 16 -1.159101258 -0.34588853 -0.27147562 -0.07054420 #> 17 -0.651292750 -0.49883923  0.42446013  0.05656427 #> 18 -0.686280126 -0.30973158  0.40750677  2.30782771 #> 19 -0.281881327 -0.04995115  1.19752435  0.93874976 #> 20 -0.342557190 -0.35275874  2.32859320  1.18672416 #>  #> Clustering vector: #>   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18  19  20  #>  13  11   4   6   9  13   7  10  15  17  16  18  18   3   1  17  12  20   5   3  #>  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37  38  39  40  #>  10  13  12  11   3  17   8   2  12  15  14  15   6   4  15   9  16  13  20   1  #>  41  42  43  44  45  46  47  48  49  50  51  52  53  54  55  56  57  58  59  60  #>  12  11  13  13   7  13  12   9  11  12   3  14   7  13  12  14   7  15   3  15  #>  61  62  63  64  65  66  67  68  69  70  71  72  73  74  75  76  77  78  79  80  #>  16   9   8   2   9  11   3   8   5   5  18  17  12  10  18  15   1  15  16   8  #>  81  82  83  84  85  86  87  88  89  90  91  92  93  94  95  96  97  98  99 100  #>  16  19   2  19  19  11   5   3  19   3  15  14  15   2  13   9  17   3  13   2  #>  #> Within cluster sum of squares by cluster: #>  [1]  5.0357352 11.4608785  7.9326275  0.9921913  9.9418120  2.4669004 #>  [7]  8.3425688  3.8879014 13.2343206  4.5166634  3.0260293  8.0412334 #> [13]  8.5051702  6.8762885  5.9461649  8.3255313  3.4617088  5.2412577 #> [19]  8.9144667  0.9062989 #>  (between_SS / total_SS =  83.6 %) #>  #> 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  2.28519855     0.9196328   Group_3 Group_1 #> 2  0.01325098     1.0647271   Group_5 Group_1 #> 3 -1.52876440     0.6472361   Group_4 Group_1 #> 4  5.74000483     1.0925108   Group_4 Group_2 #> 5  5.40615345     0.9142736   Group_5 Group_2 #> 6  1.02589705     1.2726073   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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         #>                                                                 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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     #> [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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         #>                                                                 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/vignettes/FAQ.Rmd b/vignettes/FAQ.Rmd
index 99332f6..67b56cd 100644
--- a/vignettes/FAQ.Rmd
+++ b/vignettes/FAQ.Rmd
@@ -2,7 +2,7 @@
 title: "FAQs"
 output: rmarkdown::html_vignette
 vignette: >
-  %\VignetteIndexEntry{FAQ}
+  %\VignetteIndexEntry{FAQs}
   %\VignetteEngine{knitr::rmarkdown}
   %\VignetteEncoding{UTF-8}
 ---
diff --git a/vignettes/cheese.Rmd b/vignettes/cheese.Rmd
index f092cb4..a26acd4 100644
--- a/vignettes/cheese.Rmd
+++ b/vignettes/cheese.Rmd
@@ -2,7 +2,7 @@
 title: "Case Study: Cheese Communities"
 output: rmarkdown::html_vignette
 vignette: >
-  %\VignetteIndexEntry{Case Study: Cheese}
+  %\VignetteIndexEntry{Case Study: Cheese Communities}
   %\VignetteEngine{knitr::rmarkdown}
   %\VignetteEncoding{UTF-8}
 ---
diff --git a/vignettes/data_input.Rmd b/vignettes/data_input.Rmd
index 4ca1103..425cbf6 100644
--- a/vignettes/data_input.Rmd
+++ b/vignettes/data_input.Rmd
@@ -2,7 +2,7 @@
 title: "Data Input: Multi-Omics/Single-Omics"
 output: rmarkdown::html_vignette
 vignette: >
-  %\VignetteIndexEntry{Data Input}
+  %\VignetteIndexEntry{Data Input: Multi-Omics/Single-Omics}
   %\VignetteEngine{knitr::rmarkdown}
   %\VignetteEncoding{UTF-8}
 ---
diff --git a/vignettes/navigate.Rmd b/vignettes/navigate.Rmd
index 637a234..ac902d9 100644
--- a/vignettes/navigate.Rmd
+++ b/vignettes/navigate.Rmd
@@ -2,7 +2,7 @@
 title: "Network Navigation: Key Steps"
 output: rmarkdown::html_vignette
 vignette: >
-  %\VignetteIndexEntry{Network Navigation}
+  %\VignetteIndexEntry{Network Navigation: Key Steps}
   %\VignetteEngine{knitr::rmarkdown}
   %\VignetteEncoding{UTF-8}
 ---