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    bartools: preprocessing datasets

    -

    Dane -Vassiliadis

    - -

    June 27, 2019

    - - -
    preprocessing.Rmd
    -
    - - - -
    -

    Cellular Barcoding Dataset Preprocessing -

    -
    -

    Getting started -

    -

    The first step in any cellular barcoding analysis is to extract -barcode containing reads from high throughput sequencing datasets and -count the number of each barcode within each sample. The -bartools package is optimised for use with SPLINTR lineage -barcode libraries (See Fennell and -Vassiliadis et al. Nature 2021) however the functions within can be -adapted to other DNA barcoding methodologies that use randomised DNA -barcodes.

    -
    -
    -

    The reference barcode library -

    -

    In the current version, bartools assumes that any -barcoding experiment uses a known reference library of barcodes. Since -DNA barcodes are generally introduced into cells using viral vectors, -the plasmid pool used to generate the barcode virus represents the -reference set of barcodes that can be present in an experiment. The -primary step in any barcoding analysis is to determine which barcodes -are present in this reference pool and at what proportion.

    -

    In the case of SPLINTR, barcodes take the following form:

    -
    tgaccatgtacgattgactaNNSWSNNWSWNNSWSNNWSWNNSWSNNWSWNNSWSNNWSWNNSWSNNWSWNNSWSNNWSWtgctaatgcgtactg
    -

    where lower case sequence represents upstream and downstream constant -regions that are critical for accurately retrieving barcodes from the -genomic DNA of cells.

    -

    Load the bartools library

    -
    -library(bartools)
-#> Loading required package: edgeR
-#> Loading required package: limma
-#> Loading required package: ggplot2
    -
    -
    -

    Barcode pre-processing -

    -

    Barcode information can be extracted from raw fastq files and -summarised into a table of counts per barcode as follows

    -

    Firstly we define the expected barcode pattern and the upstream and -downstream constant regions that flank true barcodes. And then extract -reads containing this barcode pattern using the -extractBarcodeReads function

    -
    -pattern <- "([ACTG][ACTG][GC][AT][GC][ACTG][ACTG][AT][GC][AT]){3,6}"
-fastq <- system.file("extdata", "test_extract_75bp_single-end.fastq.gz", package = "bartools", mustWork = T)
-constant <- toupper("tgaccatgtacgattgacta")
-test.extract <- bartools::extractbartoolseads(infile = fastq, 
-                              barcode_pattern = pattern,
-                              constant = constant, 
-                              yieldSize = 1e6)
-test.extract
    -

    Following barcode extraction we make use of previously generated -library of reference barcodes and map our extracted sequences to this -reference using mapBarcodeReads. True barcodes will align -without mismatches. Reads containing sequencing errors or false barcode -sequences that snuck through the extraction step will be filtered out -here.

    -
    -test.map <- bartools::mapbartoolseads(reads = test.extract,
-                                      bowtie_index = "../data/bowtie/index",
-                                      mismatches = 1,
-                                      threads = 1,
-                                      prefix = "test_map")
-test.map
    -

    If a bowtie reference has not yet been generated we can specify -reference_fasta in place of bowtie_index and -it will be generated automatically.

    -
    -reference.fa <- system.file("extdata", "barcode_lib_reference_test.fasta", package = "bartools", mustWork = T)
-test.map <- bartools::mapBarcodeReads(reads = test.extract,
-                                      bowtie_index = NULL,
-                                      reference_fasta = reference.fa,
-                                      mismatches = 1, # maximum 3 mismatches
-                                      threads = 2,
-                                      prefix = "test_map")
-test.map
    -

    mapBarcodeReads counts the number of occurrences of each -barcode and saves the output in a file. We can easily load and examine -the final count data here.

    -
    -test.counts <- utils::read.delim("./test_map_counts.csv", header = T, sep = ",", row.names = 1)
-test.counts %>% tibble::rownames_to_column() %>% dplyr::arrange(dplyr::desc(mapped))
    -
    -
    -

    Alternative pre-processing workflow using BARtab -

    -

    For large numbers of samples it can be cumbersome and time consuming -to process raw count data within R as shown above. It is more efficient -to process these samples and generate count tables using a pipeline that -can be distributed across HPC infrastructure

    -

    We have developed BARtab, a Nextflow pipeline for the -preprocessing of DNA barcoding datasets from bulk and single-cell -experiments. The pipeline and associated running instructions can be -found at https://github.com/DaneVass/BARtab.

    -
    -
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    Session Info -

    -
    -sessionInfo()
-#> R version 4.2.2 (2022-10-31)
-#> Platform: aarch64-apple-darwin20 (64-bit)
-#> Running under: macOS Ventura 13.1
-#> 
-#> Matrix products: default
-#> BLAS:   /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRblas.0.dylib
-#> LAPACK: /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRlapack.dylib
-#> 
-#> locale:
-#> [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
-#> 
-#> attached base packages:
-#> [1] stats     graphics  grDevices utils     datasets  methods   base     
-#> 
-#> other attached packages:
-#> [1] bartools_0.2.3 ggplot2_3.4.2  edgeR_3.40.2   limma_3.54.2  
-#> 
-#> loaded via a namespace (and not attached):
-#>  [1] locfit_1.5-9.8    tidyselect_1.2.0  xfun_0.39         bslib_0.5.0      
-#>  [5] purrr_1.0.1       splines_4.2.2     lattice_0.21-8    colorspace_2.1-0 
-#>  [9] vctrs_0.6.3       generics_0.1.3    htmltools_0.5.5   mgcv_1.9-0       
-#> [13] yaml_2.3.7        utf8_1.2.3        rlang_1.1.1       pkgdown_2.0.7    
-#> [17] jquerylib_0.1.4   pillar_1.9.0      glue_1.6.2        withr_2.5.0      
-#> [21] lifecycle_1.0.3   stringr_1.5.0     munsell_0.5.0     gtable_0.3.3     
-#> [25] ragg_1.2.5        memoise_2.0.1     evaluate_0.21     knitr_1.43       
-#> [29] permute_0.9-7     fastmap_1.1.1     parallel_4.2.2    fansi_1.0.4      
-#> [33] Rcpp_1.0.11       scales_1.2.1      cachem_1.0.8      vegan_2.6-4      
-#> [37] desc_1.4.2        jsonlite_1.8.7    ineq_0.2-13       systemfonts_1.0.4
-#> [41] fs_1.6.2          textshaping_0.3.6 digest_0.6.33     stringi_1.7.12   
-#> [45] dplyr_1.1.2       grid_4.2.2        rprojroot_2.0.3   cli_3.6.1        
-#> [49] tools_4.2.2       magrittr_2.0.3    sass_0.4.6        tibble_3.2.1     
-#> [53] cluster_2.1.4     pkgconfig_2.0.3   Matrix_1.6-0      MASS_7.3-60      
-#> [57] rmarkdown_2.23    rstudioapi_0.15.0 R6_2.5.1          nlme_3.1-162     
-#> [61] compiler_4.2.2
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    - - - -
    -

    -

    Developed by Dane Vassiliadis.

    -
    - -
    -

    -

    Site built with pkgdown 2.0.7.

    -
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    - - - - - - - diff --git a/docs/articles/quickstart.html b/docs/articles/quickstart.html deleted file mode 100644 index 54e31c2..0000000 --- a/docs/articles/quickstart.html +++ /dev/null @@ -1,1864 +0,0 @@ - - - - - - - - -bartools: quickstart guide • bartools - - - - - - - - - - Skip to contents - - -
    - - - - -
    -
    -

    bartools: quickstart guide

    -

    Dane -Vassiliadis

    - -

    August 2, 2022

    - - -
    quickstart.Rmd
    -
    - - - -
    -

    Bartools Quickstart Guide -

    -

    Load the bartools library

    -
    -library(bartools)
    -
    ## Loading required package: edgeR
    -
    ## Loading required package: limma
    -
    ## Loading required package: ggplot2
    -
    -

    1. Importing DNA barcode count data -

    -

    Raw barcode count data can be thought of similarly to raw -integer-based count data from other count based experiments such as -RNA-sequencing. For these data types the edgeR package -provides an efficient DGEList object structure to store -sample counts and associated metadata. bartools makes use -of this object structure to store and process DNA barcode counts.

    -
    -

    An example barcoding experiment -

    -

    For this section we will make use of a hypothetical DNA barcoding -dataset based on recent unpublished data from the Dawson lab -investigating the response of acute myeloid leukaemia (AML) cells to a -novel class of MYST acetyltransferase inhibitor described recently in MacPherson et -al. Nature 2019.

    -

    AML cells were cultured in vitro, barcoded using a -lentiviral based barcoding library called SPLINTR, and -transplanted into three groups of C57BL/6J mice with daily dosing of -MYST inhibitor at low or high dose or a corresponding vehicle control. -Barcode samples were harvested from the bone marrow of moribund mice and -sequenced for in technical replicate replicate.

    -

    To follow along with this vignette the raw counts tables and sample -metadata are included in the bartools package.

    -
    -data(test.dge)
    -
    -
    -

    Generating a DGEList object from sample counts and metadata -

    -

    Counts objects defined above can be specified in a sample metadata -sheet as shown below. This is the easiest way to generate a -DGEList object containing the count information and -metadata of interest for a set of barcode sequencing samples. An example -of this process is shown below.

    -
    -samplesheet <- read.csv(system.file("extdata", "test_sampletable.csv", package = "bartools", mustWork = T), header = T, stringsAsFactors = F)
-samplesheet
    -

    Load in the counts as specified in the samplesheet into a DGEList -object

    -
    -dge <- edgeR::readDGE(files = samplesheet, group = samplesheet$treatment, labels = samplesheet$sample, header = T)
    -

    This results in the creation of a DGEList object containing counts -and metadata information for each sample

    -
    -data(test.dge)
-test.dge
    -
    ## An object of class "DGEList"
-## $samples
-##       Sample Experiment        Group PCR_Replicate Treatment        group
-## T0-1    T0-1    test_01           T0             1        T0           T0
-## T0-2    T0-2    test_01           T0             2        T0           T0
-## S10-1  S10-1    test_01 10_High_dose             1 High_dose 10_High_dose
-## S10-2  S10-2    test_01 10_High_dose             2 High_dose 10_High_dose
-## S11-1  S11-1    test_01   11_Vehicle             1   Vehicle   11_Vehicle
-##       lib.size norm.factors
-## T0-1   3584606            1
-## T0-2   3349340            1
-## S10-1  4114186            1
-## S10-2  4196458            1
-## S11-1  2907500            1
-## 33 more rows ...
-## 
-## $counts
-##         Samples
-## Tags     T0-1 T0-2 S10-1 S10-2 S11-1 S11-2 S12-1 S12-2 S13-1 S13-2 S14-1 S14-2
-##   BC_1    175   79     0     0     0     0     0     0     0     0     0     0
-##   BC_13  1458  834     0     0     0     0     0     0     0     0     0     0
-##   BC_99  1155 1554     0     0     0     0     0     0     0     0     0     0
-##   BC_120  285  184     0     0     0     0     0     0     0     0     0     0
-##   BC_351    0    0     0     0     0     0     0     0     0     0     0     0
-##         Samples
-## Tags     S15-1 S15-2 S16-1 S16-2 S17-1 S17-2 S18-1 S18-2 S1-1 S1-2 S2-1 S2-2
-##   BC_1       0     0     0     0     0     0     0     0    0    0    0    0
-##   BC_13      0     0     0     0     0     0     0     0    0    0    0    0
-##   BC_99      0     0     0     0     0     0     0     0  105  205    0    0
-##   BC_120     0     0     0     0     0     0     0     0    0    0    0    0
-##   BC_351     0     0     0     0     0     0     0     0    0    0    0    0
-##         Samples
-## Tags     S3-1 S3-2 S4-1 S4-2 S5-1 S5-2 S6-1 S6-2 S7-1 S7-2 S8-1 S8-2 S9-1 S9-2
-##   BC_1      0    0    0    0    0    0    0    0    0    0    0    0    0    0
-##   BC_13     0    0    0    0    0    0    0    0    0    0    0    0    0    0
-##   BC_99     0    0    0    0    0    0    0    0    0    0    0    0    0    0
-##   BC_120    0    0    0    0    0    0    0    0    0    0    0    0    0    0
-##   BC_351    0    0    0    0    0    0    0    0    0    0    0    0    0    0
-## 1634 more rows ...
    -
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    2. Data QC and Normalisation -

    -
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    Data QC -

    -

    We first want to ensure that we are working with clean data. We -remove barcodes that have no counts across all samples.

    -
    -# Remove rows with no data
-keeprows = rowSums(test.dge$counts) >= 1
-dge.filtered <- test.dge[keeprows,]
-dge.filtered.counts <- dge.filtered$counts
-dim(dge.filtered.counts)
    -
    ## [1] 1490   38
    -

    We then normalise samples to sequencing depth

    -
    -# Normalise each sample to counts per million
-dge.cpmnorm <- normaliseCounts(dge.filtered, method = "cpm", threshold = 1)
    -
    ## Error in normaliseCounts(dge.filtered, method = "cpm", threshold = 1): method argument must be one of CPM, RLE, TMM, upperquartile or TMMwsp
    -

    We can plot the raw and normalised sequencing depth to get an idea of -depth discrepancies between PCR replicates

    -
    -# raw counts per sample
-plotReadCounts(dge.filtered.counts, group = dge.filtered$samples$Treatment)
    -

    -
    -# normalised counts per sample
-plotReadCounts(dge.cpmnorm, group = dge.filtered$samples$Treatment)
    -
    ## Error in is.data.frame(x): object 'dge.cpmnorm' not found
    -

    For lentiviral based barcoding experiments, such as this one, it is -common for the library to exhibit a degree of skewness based on the -cloning method. This means that some barcodes are represented in the -library more than others and so have a greater chance to be transduced -into multiple cells. Most experiments assume that each individual -barcode is transduced into only one cell, and that each cell is only -transduced with one barcode.This is ensured using a low multiplicity of -infection (MOI) transduction in which the likelihood that a cell is -transduced with one or more barcode containing virions follows a Poisson -distribution. With this in mind, it also can be useful to check the -total counts per barcode to identify bias in counts in sample -vs. frequency of barcode in reference library.

    -
    -# plot detected barcodes
-plotBarcodeCounts(dge.cpmnorm, log10 = F)
    -
    ## Error in is.data.frame(x): object 'dge.cpmnorm' not found
    -
    -plotBarcodeCounts(dge.cpmnorm, log10 = T)
    -
    ## Error in is.data.frame(x): object 'dge.cpmnorm' not found
    -

    In this plot individual barcodes on the x-axis are ordered based on -their frequency in the library pool. An increased number of counts per -barcode toward the left hand side of the plot would be suggestive of -transduction bias, meaning that there are more reads on average -attributed to the more abundant barcodes in the library. And so, likely -multiple cells were transduced with the same barcode. We don’t see this -here suggesting that this is not a problem for this experiment.

    -
    -
    -

    Check correlation between PCR replicates -

    -

    It is also important to ensure that individual samples are sequenced -to an appropriate depth as this ensures that the entire barcode -repertoire present in a sample is captured in the data. Sequencing -technical duplicates of a sample generated at the library PCR stage is a -good way to ensure this.

    -

    In our experiment we have 9 samples total, each with two PCR -technical replicates. Here we correlate the barcode distributions for -each pair of technical replicates.

    -
    -samps <- unique(test.dge$samples$group)
-lapply(samps, function(x){
-  df <- test.dge[,test.dge$samples$group %in% as.character(x)]
-  plotBarcodeRegression(df, samp1 = colnames(df)[[1]], samp2 = colnames(df)[[2]])
-})
    -
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    -

    We fit a linear model to both technical replicates per sample and -plot the regression line. Note that we expect a very high correlation -because these are PCR duplicates of the same barcode pool. We can easily -get the correlation values between replicates

    -
    -corrs <- calcReplicateCorr(test.dge, group = test.dge$samples$Group, corr.thresh = 0.9)
    -
    ## Warning in colnames(dge$samples) == group: longer object length is not a
-## multiple of shorter object length
    -
    ## Error in table(dge$samples[, group.pos]): nothing to tabulate
    -
    -corrs
    -
    ## Error in eval(expr, envir, enclos): object 'corrs' not found
    -
    -which(corrs < 0.9)
    -
    ## Error in which(corrs < 0.9): object 'corrs' not found
    -
    -
    -

    Collapse PCR replicates in object -

    -

    Now that we know our samples are of good quality we can merge the PCR -replicate information. From this point onward its a good idea to -collapse our PCR replicates.

    -
    -dim(dge.filtered)
    -
    ## [1] 1490   38
    -

    Here we take the average of PCR technical replicates within each -sample

    -
    -dge.filtered.collapsed <- collapseReplicates(dge.filtered, groupby = dge.filtered$samples$Group, by = "mean", show_reps = F)
    -

    The result is a clean barcode sequencing dataset ready for further -investigation and visualisation.

    -
    -dim(dge.filtered.collapsed)
    -
    ## [1] 1490   19
    -
    -head(dge.filtered.collapsed)
    -
    ## An object of class "DGEList"
-## $samples
-##              Sample Experiment        Group PCR_Replicate Treatment
-## 1_High_dose    S1-1    test_01  1_High_dose             1 High_dose
-## 10_High_dose  S10-1    test_01 10_High_dose             1 High_dose
-## 11_Vehicle    S11-1    test_01   11_Vehicle             1   Vehicle
-## 12_Vehicle    S12-1    test_01   12_Vehicle             1   Vehicle
-## 13_Low_dose   S13-1    test_01  13_Low_dose             1  Low_dose
-##                     group lib.size norm.factors
-## 1_High_dose   1_High_dose  3447629            1
-## 10_High_dose 10_High_dose  4114186            1
-## 11_Vehicle     11_Vehicle  2907500            1
-## 12_Vehicle     12_Vehicle  4202337            1
-## 13_Low_dose   13_Low_dose  4513559            1
-## 14 more rows ...
-## 
-## $counts
-##         Samples
-## Tags     1_High_dose 10_High_dose 11_Vehicle 12_Vehicle 13_Low_dose 14_Low_dose
-##   BC_1             0            0          0          0           0           0
-##   BC_13            0            0          0          0           0           0
-##   BC_99          155            0          0          0           0           0
-##   BC_120           0            0          0          0           0           0
-##   BC_426           0            0          0          0           0           0
-##   BC_430           0            0          0          0           0           0
-##         Samples
-## Tags     15_Low_dose 16_Low_dose 17_High_dose 18_Vehicle 2_High_dose
-##   BC_1             0           0            0          0           0
-##   BC_13            0           0            0          0           0
-##   BC_99            0           0            0          0           0
-##   BC_120           0           0            0          0           0
-##   BC_426           0           0            0          0           0
-##   BC_430           0           0            0        413           0
-##         Samples
-## Tags     3_High_dose 4_Vehicle 5_Low_dose 6_High_dose 7_Vehicle 8_Vehicle
-##   BC_1             0         0          0           0         0         0
-##   BC_13            0         0          0           0         0         0
-##   BC_99            0         0          0           0         0         0
-##   BC_120           0         0          0           0         0         0
-##   BC_426           0         0          0           0         0         0
-##   BC_430           0        13          0           0         0         0
-##         Samples
-## Tags     9_Low_dose   T0
-##   BC_1            0  127
-##   BC_13           0 1146
-##   BC_99           0 1354
-##   BC_120          0  234
-##   BC_426          0   81
-##   BC_430          0 1022
    -
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    -

    3. Visualisation -

    -

    bartools includes a range of visualisation options for -examining barcode-seq datasets.

    -
    -

    Bubble plot -

    -

    Sometimes a visual depiction of the data is most suitable. Here -barcodes/tags are represented by bubbles aligned on a single plane. The -size of the bubbles reflects the percentage abundance of each barcode -within a sample

    -
    -plotBarcodeBubble(dge.filtered.collapsed$counts, proportion.cutoff = 10, labels = T)
    -

    -

    Bubbleplots can also be ordered according to a particular sample -which can help with visual representation of large vs small clones.

    -
    -plotOrderedBubble(dge.filtered.collapsed$counts, proportion.cutoff = 10, labels = T, orderSample = "T0", colorDominant = T)
    -
    ## Error in methods::is(counts): object 'counts' not found
    -

    Barcodes that fail to meet a defined abundance threshold in any -sample can be greyed out

    -
    -plotOrderedBubble(dge.filtered.collapsed$counts, proportion.cutoff = 10, labels = T, orderSample = "T0", colorDominant = F)
    -
    ## Error in methods::is(counts): object 'counts' not found
    -
    -
    -

    Barcode Plot -

    -

    Alternatively, we can focus in on the most abundant barcodes within a -set of samples to more easily observe how these change in frequency over -the course of an experiment.

    -
    -plotBarcodeHistogram(dge.filtered.collapsed$counts, 
-                     sample = dge.filtered.collapsed$samples$Group[[1]], 
-                     top = 50)
    -

    -
    -
    -

    Timeseries Plots -

    -

    For timecourse experiments it is useful to visualise the kinetics of -barcode diversity changes over time. In this instance we can use -plotBarcodeTimeseries to get an idea of the relative -abundance of the top n barcodes in a sample relative to -others.

    -
    -plotBarcodeTimeseries(dge.filtered.collapsed, top = 5)
    -
    ## Using barcode as id variables
    -

    -
    -
    -

    Principal Components Analysis -

    -

    A global level PCA analysis is a good way to get a high level -understanding of the similarities and differences between samples.

    -
    -plotBarcodePCA(dge.filtered.collapsed, intgroup = "Treatment")
    -

    -
    -plotBarcodePCA(dge.filtered.collapsed[,dge.filtered.collapsed$samples$Treatment %in% c("T0","Vehicle","High_dose")], intgroup = "Treatment")
    -

    -
    -
    -

    Heatmaps -

    -

    Another method of comparing abundance across samples is using a -heatmap. Here barcodes ranked among the top n most abundant within each -sample are indicated by an asterix. This heatmap shows high dose samples -are generally distinct from the low dose and vehicle group.

    -
    -plotBarcodeHeatmap(cpm(dge.filtered.collapsed$counts), N = 5)
    -

    -
    -
    -
    -

    4. Analysing Composition and Diversity -

    -

    Its important to not only be able to visualise the data but also -understand relationships between barcodes/tags at the data level

    -
    -

    Identify compositional bias within samples -

    -

    The above plots give a global visualisation of the abundance of each -barcode within a sample however the compositional makeup can be obscured -by visualising the data in this way. it can be helpful to examine the -fraction of barcodes that comprise a sample. These plots calculate the -cumulative sum of a sample in relation to other samples defined by the -user.

    -
    -plotBarcodeCumSum(dge.filtered.collapsed$counts, sample1 = "T0", samples = colnames(dge.filtered.collapsed$counts)[1:5])
    -

    -
    -
    -

    Identifying abundant barcodes within samples -

    -

    It is important to be able to determine which barcodes are most -abundant wihtin each sample. Bartools allows this to be easily -calculated according to an abundance threshold.

    -
    -top.bc <- getDominantBarcodes(dge.filtered.collapsed, pct.thresh = 5)
-top.bc
    -
    ## $`1_High_dose`
-## [1] "BC_79755"  "BC_102160" "BC_59493"  "BC_23361"  "BC_53234"  "BC_400391"
-## 
-## $`10_High_dose`
-## [1] "BC_8419"   "BC_124796"
-## 
-## $`11_Vehicle`
-## [1] "BC_53234"  "BC_205581" "BC_90135"  "BC_172626" "BC_58978" 
-## 
-## $`12_Vehicle`
-## [1] "BC_389078" "BC_159570" "BC_135438" "BC_500780" "BC_79755" 
-## 
-## $`13_Low_dose`
-## [1] "BC_8419"   "BC_124796" "BC_102160" "BC_257382"
-## 
-## $`14_Low_dose`
-## [1] "BC_90135"  "BC_388103" "BC_31610" 
-## 
-## $`15_Low_dose`
-## [1] "BC_1478"   "BC_94579"  "BC_124796" "BC_8419"  
-## 
-## $`16_Low_dose`
-## [1] "BC_53234"  "BC_79755"  "BC_140320" "BC_31610"  "BC_248880" "BC_1478"  
-## 
-## $`17_High_dose`
-## [1] "BC_183691" "BC_90135"  "BC_400391"
-## 
-## $`18_Vehicle`
-## [1] "BC_49397"  "BC_91412"  "BC_159570" "BC_62602"  "BC_93485"  "BC_70225" 
-## 
-## $`2_High_dose`
-## [1] "BC_248880" "BC_65104"  "BC_9141"   "BC_1478"   "BC_31610"  "BC_79755" 
-## 
-## $`3_High_dose`
-## [1] "BC_79755"  "BC_142024" "BC_102160" "BC_183691"
-## 
-## $`4_Vehicle`
-## [1] "BC_53234"  "BC_58978"  "BC_62602"  "BC_68847"  "BC_140320" "BC_79755" 
-## 
-## $`5_Low_dose`
-## [1] "BC_561471" "BC_118640" "BC_59493"  "BC_1607"   "BC_31610" 
-## 
-## $`6_High_dose`
-## [1] "BC_79755"  "BC_8419"   "BC_124796" "BC_4564"  
-## 
-## $`7_Vehicle`
-## [1] "BC_53234"  "BC_54442"  "BC_142024" "BC_140320" "BC_93485" 
-## 
-## $`8_Vehicle`
-## [1] "BC_142024" "BC_90135"  "BC_58978"  "BC_93485" 
-## 
-## $`9_Low_dose`
-## [1] "BC_79755"  "BC_4198"   "BC_81579"  "BC_118640" "BC_560839" "BC_345889"
-## [7] "BC_400391"
-## 
-## $T0
-## [1] "BC_245016" "BC_121651"
    -

    We can then use specific plots to visualise the dominance of specific -barcodes within and across samples. These plots show that both top -barcodes in the T0 sample were essentially extinguished by the high dose -treatment

    -
    -plotBarcodeBoxplot(dge.filtered.collapsed, barcodes = top.bc$T0, condition = c("T0", "Low_dose", "High_dose"))
    -
    ## multiple barcodes given. No metadata group
    -

    -
    -
    -

    Calculating and plotting percentile abundance. -

    -

    The above graphs demonstrate that relatively few barcodes can -sometimes comprise the majority of a sample’s clonality, particularly -following a selective event such as drug treatment. It is useful to -formally analyse this based on a desired percentile threshold. A common -threshold is the 95th percentile. This can eliminate small barcodes that -comprise the tail of the dataset and give a sense of how many clones -truly comprise each sample

    -
    -calcPercentileBarcodes(dge.filtered.collapsed, percentile = 0.95)
    -
    ## $NumBarcodes
-##          Sample NumBarcodes
-## 1   1_High_dose          15
-## 2  10_High_dose           3
-## 3    11_Vehicle          19
-## 4    12_Vehicle          14
-## 5   13_Low_dose          11
-## 6   14_Low_dose           6
-## 7   15_Low_dose           7
-## 8   16_Low_dose          19
-## 9  17_High_dose           5
-## 10   18_Vehicle          27
-## 11  2_High_dose          12
-## 12  3_High_dose          10
-## 13    4_Vehicle          25
-## 14   5_Low_dose          11
-## 15  6_High_dose           9
-## 16    7_Vehicle          16
-## 17    8_Vehicle          21
-## 18   9_Low_dose          19
-## 19           T0         596
-## 
-## $TopBarcodes
-## $TopBarcodes$`1_High_dose`
-##  [1] "BC_79755"  "BC_102160" "BC_59493"  "BC_23361"  "BC_53234"  "BC_400391"
-##  [7] "BC_142112" "BC_94511"  "BC_68847"  "BC_142024" "BC_23241"  "BC_47310" 
-## [13] "BC_9141"   "BC_5472"   "BC_65104" 
-## 
-## $TopBarcodes$`10_High_dose`
-## [1] "BC_8419"   "BC_124796" "BC_102160"
-## 
-## $TopBarcodes$`11_Vehicle`
-##  [1] "BC_53234"  "BC_205581" "BC_90135"  "BC_172626" "BC_58978"  "BC_79755" 
-##  [7] "BC_159570" "BC_118640" "BC_31610"  "BC_635211" "BC_257382" "BC_324711"
-## [13] "BC_1607"   "BC_4531"   "BC_327422" "BC_297704" "BC_194823" "BC_415460"
-## [19] "BC_140320"
-## 
-## $TopBarcodes$`12_Vehicle`
-##  [1] "BC_389078" "BC_159570" "BC_135438" "BC_500780" "BC_79755"  "BC_31610" 
-##  [7] "BC_49629"  "BC_118640" "BC_47726"  "BC_78349"  "BC_58978"  "BC_245016"
-## [13] "BC_1607"   "BC_329679"
-## 
-## $TopBarcodes$`13_Low_dose`
-##  [1] "BC_8419"   "BC_124796" "BC_102160" "BC_257382" "BC_172626" "BC_345889"
-##  [7] "BC_53234"  "BC_5472"   "BC_79755"  "BC_205581" "BC_17608" 
-## 
-## $TopBarcodes$`14_Low_dose`
-## [1] "BC_90135"  "BC_388103" "BC_31610"  "BC_58978"  "BC_292962" "BC_159570"
-## 
-## $TopBarcodes$`15_Low_dose`
-## [1] "BC_1478"   "BC_94579"  "BC_124796" "BC_8419"   "BC_316204" "BC_400391"
-## [7] "BC_23241" 
-## 
-## $TopBarcodes$`16_Low_dose`
-##  [1] "BC_53234"  "BC_79755"  "BC_140320" "BC_31610"  "BC_248880" "BC_1478"  
-##  [7] "BC_17608"  "BC_257382" "BC_345889" "BC_94511"  "BC_142112" "BC_142024"
-## [13] "BC_91412"  "BC_58978"  "BC_190202" "BC_4198"   "BC_68847"  "BC_389078"
-## [19] "BC_47726" 
-## 
-## $TopBarcodes$`17_High_dose`
-## [1] "BC_183691" "BC_90135"  "BC_400391" "BC_31610"  "BC_68847" 
-## 
-## $TopBarcodes$`18_Vehicle`
-##  [1] "BC_49397"  "BC_91412"  "BC_159570" "BC_62602"  "BC_93485"  "BC_70225" 
-##  [7] "BC_79755"  "BC_4564"   "BC_124796" "BC_58978"  "BC_8419"   "BC_149935"
-## [13] "BC_68847"  "BC_90135"  "BC_4173"   "BC_4469"   "BC_172626" "BC_31610" 
-## [19] "BC_383329" "BC_55960"  "BC_62970"  "BC_102160" "BC_244320" "BC_400391"
-## [25] "BC_135438" "BC_203989" "BC_133178"
-## 
-## $TopBarcodes$`2_High_dose`
-##  [1] "BC_248880" "BC_65104"  "BC_9141"   "BC_1478"   "BC_31610"  "BC_79755" 
-##  [7] "BC_388103" "BC_257382" "BC_68618"  "BC_102160" "BC_4531"   "BC_90135" 
-## 
-## $TopBarcodes$`3_High_dose`
-##  [1] "BC_79755"  "BC_142024" "BC_102160" "BC_183691" "BC_59493"  "BC_345889"
-##  [7] "BC_23361"  "BC_70225"  "BC_30946"  "BC_159570"
-## 
-## $TopBarcodes$`4_Vehicle`
-##  [1] "BC_53234"  "BC_58978"  "BC_62602"  "BC_68847"  "BC_140320" "BC_79755" 
-##  [7] "BC_327422" "BC_36103"  "BC_102160" "BC_81579"  "BC_118640" "BC_135438"
-## [13] "BC_205581" "BC_1607"   "BC_4564"   "BC_91412"  "BC_229324" "BC_560839"
-## [19] "BC_159570" "BC_468335" "BC_49397"  "BC_203989" "BC_124796" "BC_8419"  
-## [25] "BC_190202"
-## 
-## $TopBarcodes$`5_Low_dose`
-##  [1] "BC_561471" "BC_118640" "BC_59493"  "BC_1607"   "BC_31610"  "BC_23361" 
-##  [7] "BC_159570" "BC_93485"  "BC_468335" "BC_81579"  "BC_257382"
-## 
-## $TopBarcodes$`6_High_dose`
-## [1] "BC_79755"  "BC_8419"   "BC_124796" "BC_4564"   "BC_400391" "BC_31610" 
-## [7] "BC_1478"   "BC_90135"  "BC_388103"
-## 
-## $TopBarcodes$`7_Vehicle`
-##  [1] "BC_53234"  "BC_54442"  "BC_142024" "BC_140320" "BC_93485"  "BC_124796"
-##  [7] "BC_23544"  "BC_8419"   "BC_324711" "BC_159570" "BC_58978"  "BC_90135" 
-## [13] "BC_345889" "BC_4564"   "BC_102160" "BC_400391"
-## 
-## $TopBarcodes$`8_Vehicle`
-##  [1] "BC_142024" "BC_90135"  "BC_58978"  "BC_93485"  "BC_363879" "BC_4531"  
-##  [7] "BC_205581" "BC_118640" "BC_79755"  "BC_31610"  "BC_1607"   "BC_82758" 
-## [13] "BC_194823" "BC_389078" "BC_251167" "BC_68847"  "BC_77209"  "BC_102160"
-## [19] "BC_4173"   "BC_4564"   "BC_94579" 
-## 
-## $TopBarcodes$`9_Low_dose`
-##  [1] "BC_79755"  "BC_4198"   "BC_81579"  "BC_118640" "BC_560839" "BC_345889"
-##  [7] "BC_400391" "BC_1607"   "BC_319105" "BC_35704"  "BC_205581" "BC_90135" 
-## [13] "BC_124796" "BC_142024" "BC_8419"   "BC_561471" "BC_19146"  "BC_68847" 
-## [19] "BC_78912" 
-## 
-## $TopBarcodes$T0
-##   [1] "BC_245016" "BC_121651" "BC_420967" "BC_108059" "BC_323670" "BC_9141"  
-##   [7] "BC_331193" "BC_119418" "BC_65104"  "BC_17570"  "BC_35704"  "BC_92942" 
-##  [13] "BC_383329" "BC_1396"   "BC_83680"  "BC_24510"  "BC_55858"  "BC_38589" 
-##  [19] "BC_137495" "BC_446739" "BC_69627"  "BC_313129" "BC_171385" "BC_280320"
-##  [25] "BC_12367"  "BC_221975" "BC_15782"  "BC_310257" "BC_3618"   "BC_67201" 
-##  [31] "BC_107789" "BC_36103"  "BC_2596"   "BC_215831" "BC_43535"  "BC_69641" 
-##  [37] "BC_13611"  "BC_16618"  "BC_588849" "BC_294188" "BC_22306"  "BC_141060"
-##  [43] "BC_249402" "BC_428803" "BC_344050" "BC_25084"  "BC_46538"  "BC_175651"
-##  [49] "BC_206193" "BC_205581" "BC_403084" "BC_56819"  "BC_400391" "BC_16956" 
-##  [55] "BC_219379" "BC_19146"  "BC_172626" "BC_18278"  "BC_43128"  "BC_47439" 
-##  [61] "BC_148563" "BC_114027" "BC_325065" "BC_62602"  "BC_58978"  "BC_252374"
-##  [67] "BC_68847"  "BC_262618" "BC_111272" "BC_224300" "BC_476380" "BC_289383"
-##  [73] "BC_50136"  "BC_202891" "BC_276866" "BC_521149" "BC_21018"  "BC_550404"
-##  [79] "BC_162898" "BC_255436" "BC_458543" "BC_168818" "BC_176946" "BC_425129"
-##  [85] "BC_204266" "BC_103528" "BC_175871" "BC_83707"  "BC_64584"  "BC_105387"
-##  [91] "BC_11643"  "BC_34617"  "BC_227720" "BC_72619"  "BC_516753" "BC_168530"
-##  [97] "BC_2040"   "BC_556149" "BC_376665" "BC_18345"  "BC_249161" "BC_345717"
-## [103] "BC_114040" "BC_24159"  "BC_41692"  "BC_248159" "BC_236596" "BC_166467"
-## [109] "BC_54442"  "BC_206263" "BC_363714" "BC_56338"  "BC_16754"  "BC_30946" 
-## [115] "BC_503475" "BC_11277"  "BC_612467" "BC_67854"  "BC_43673"  "BC_160057"
-## [121] "BC_32325"  "BC_7405"   "BC_118831" "BC_418545" "BC_120596" "BC_5256"  
-## [127] "BC_39281"  "BC_258799" "BC_29284"  "BC_85441"  "BC_103714" "BC_37062" 
-## [133] "BC_140320" "BC_20263"  "BC_46524"  "BC_321677" "BC_172412" "BC_139928"
-## [139] "BC_203520" "BC_105732" "BC_173736" "BC_13395"  "BC_11391"  "BC_142999"
-## [145] "BC_172238" "BC_79026"  "BC_43123"  "BC_128266" "BC_355420" "BC_415373"
-## [151] "BC_129519" "BC_3733"   "BC_93485"  "BC_40856"  "BC_42612"  "BC_289568"
-## [157] "BC_10454"  "BC_38398"  "BC_160158" "BC_32680"  "BC_135438" "BC_346124"
-## [163] "BC_146666" "BC_42178"  "BC_53234"  "BC_356531" "BC_389078" "BC_635211"
-## [169] "BC_183401" "BC_132225" "BC_229995" "BC_102205" "BC_47726"  "BC_564039"
-## [175] "BC_40516"  "BC_45955"  "BC_113839" "BC_102803" "BC_42310"  "BC_8835"  
-## [181] "BC_91412"  "BC_15740"  "BC_173674" "BC_44599"  "BC_324711" "BC_519985"
-## [187] "BC_77030"  "BC_350464" "BC_235128" "BC_151544" "BC_33699"  "BC_474982"
-## [193] "BC_210529" "BC_217211" "BC_221056" "BC_187025" "BC_320233" "BC_7122"  
-## [199] "BC_87047"  "BC_76947"  "BC_63846"  "BC_560839" "BC_65130"  "BC_2294"  
-## [205] "BC_102160" "BC_273124" "BC_161563" "BC_389742" "BC_521485" "BC_145719"
-## [211] "BC_373901" "BC_162119" "BC_189813" "BC_17608"  "BC_324953" "BC_320752"
-## [217] "BC_283335" "BC_67965"  "BC_2865"   "BC_242383" "BC_227308" "BC_346627"
-## [223] "BC_67233"  "BC_672202" "BC_8324"   "BC_211828" "BC_410172" "BC_495859"
-## [229] "BC_35659"  "BC_199881" "BC_44600"  "BC_377226" "BC_147006" "BC_41523" 
-## [235] "BC_336794" "BC_26769"  "BC_244320" "BC_183112" "BC_14377"  "BC_7299"  
-## [241] "BC_53380"  "BC_10347"  "BC_164378" "BC_806"    "BC_20557"  "BC_57621" 
-## [247] "BC_389087" "BC_100344" "BC_408928" "BC_156353" "BC_154990" "BC_228994"
-## [253] "BC_33764"  "BC_23522"  "BC_4564"   "BC_78349"  "BC_10808"  "BC_201293"
-## [259] "BC_81309"  "BC_45652"  "BC_123791" "BC_214604" "BC_2206"   "BC_14492" 
-## [265] "BC_146146" "BC_6093"   "BC_421415" "BC_58144"  "BC_167638" "BC_366637"
-## [271] "BC_207651" "BC_154519" "BC_4733"   "BC_19353"  "BC_326596" "BC_1764"  
-## [277] "BC_86358"  "BC_101581" "BC_23241"  "BC_138900" "BC_11270"  "BC_64945" 
-## [283] "BC_37363"  "BC_638624" "BC_195246" "BC_121663" "BC_672021" "BC_194823"
-## [289] "BC_206798" "BC_521264" "BC_12191"  "BC_130431" "BC_462254" "BC_4531"  
-## [295] "BC_22999"  "BC_64931"  "BC_214878" "BC_9340"   "BC_350335" "BC_150291"
-## [301] "BC_196282" "BC_95934"  "BC_211811" "BC_587139" "BC_199093" "BC_147380"
-## [307] "BC_228018" "BC_116921" "BC_514247" "BC_380559" "BC_141246" "BC_218093"
-## [313] "BC_88464"  "BC_157906" "BC_145403" "BC_73098"  "BC_61489"  "BC_77101" 
-## [319] "BC_266451" "BC_138737" "BC_201372" "BC_654016" "BC_444573" "BC_53219" 
-## [325] "BC_57692"  "BC_175957" "BC_12418"  "BC_357846" "BC_14810"  "BC_49226" 
-## [331] "BC_175198" "BC_49044"  "BC_539228" "BC_527469" "BC_181957" "BC_181554"
-## [337] "BC_91989"  "BC_353112" "BC_130861" "BC_55675"  "BC_10293"  "BC_617554"
-## [343] "BC_346761" "BC_302238" "BC_426662" "BC_77187"  "BC_19610"  "BC_171953"
-## [349] "BC_217320" "BC_446649" "BC_29208"  "BC_93884"  "BC_20573"  "BC_146322"
-## [355] "BC_221691" "BC_261055" "BC_3583"   "BC_9626"   "BC_6304"   "BC_354350"
-## [361] "BC_13093"  "BC_203989" "BC_114257" "BC_234390" "BC_249649" "BC_88099" 
-## [367] "BC_322542" "BC_351660" "BC_52081"  "BC_225019" "BC_596629" "BC_15448" 
-## [373] "BC_528062" "BC_2430"   "BC_324042" "BC_587343" "BC_67374"  "BC_14484" 
-## [379] "BC_83816"  "BC_141114" "BC_318282" "BC_11055"  "BC_45402"  "BC_79755" 
-## [385] "BC_357977" "BC_415903" "BC_115436" "BC_43206"  "BC_50222"  "BC_655329"
-## [391] "BC_147556" "BC_182320" "BC_31779"  "BC_9322"   "BC_162322" "BC_296283"
-## [397] "BC_381245" "BC_283541" "BC_67247"  "BC_4198"   "BC_31196"  "BC_11152" 
-## [403] "BC_61500"  "BC_90135"  "BC_47310"  "BC_292962" "BC_82595"  "BC_26258" 
-## [409] "BC_99"     "BC_94579"  "BC_35283"  "BC_238577" "BC_49397"  "BC_363879"
-## [415] "BC_309303" "BC_12410"  "BC_231962" "BC_7691"   "BC_147063" "BC_302454"
-## [421] "BC_92584"  "BC_31185"  "BC_110945" "BC_144061" "BC_75688"  "BC_70909" 
-## [427] "BC_238160" "BC_491991" "BC_149347" "BC_195887" "BC_125634" "BC_1593"  
-## [433] "BC_57794"  "BC_207333" "BC_228333" "BC_316483" "BC_169627" "BC_52856" 
-## [439] "BC_129559" "BC_67370"  "BC_21535"  "BC_515934" "BC_329007" "BC_275321"
-## [445] "BC_444761" "BC_13"     "BC_516611" "BC_154696" "BC_129768" "BC_159570"
-## [451] "BC_103862" "BC_268745" "BC_9747"   "BC_194092" "BC_2791"   "BC_176231"
-## [457] "BC_57582"  "BC_50311"  "BC_48168"  "BC_330625" "BC_413271" "BC_322219"
-## [463] "BC_179919" "BC_56489"  "BC_257382" "BC_120505" "BC_472019" "BC_282868"
-## [469] "BC_164652" "BC_124624" "BC_355439" "BC_563624" "BC_430"    "BC_15707" 
-## [475] "BC_1977"   "BC_17509"  "BC_345889" "BC_473006" "BC_567519" "BC_26384" 
-## [481] "BC_28938"  "BC_245600" "BC_145103" "BC_81579"  "BC_10190"  "BC_248582"
-## [487] "BC_142112" "BC_576338" "BC_288976" "BC_90599"  "BC_255928" "BC_454109"
-## [493] "BC_220827" "BC_35633"  "BC_33035"  "BC_62970"  "BC_381050" "BC_14452" 
-## [499] "BC_189854" "BC_329679" "BC_66948"  "BC_82758"  "BC_84350"  "BC_228451"
-## [505] "BC_186267" "BC_257523" "BC_20281"  "BC_170233" "BC_15652"  "BC_41280" 
-## [511] "BC_276301" "BC_25609"  "BC_182092" "BC_186401" "BC_37116"  "BC_14380" 
-## [517] "BC_259621" "BC_171345" "BC_278096" "BC_567514" "BC_44350"  "BC_131019"
-## [523] "BC_297704" "BC_165975" "BC_451991" "BC_370968" "BC_80020"  "BC_212837"
-## [529] "BC_4173"   "BC_97874"  "BC_2762"   "BC_143062" "BC_35459"  "BC_115038"
-## [535] "BC_6453"   "BC_163063" "BC_28077"  "BC_23918"  "BC_389378" "BC_222891"
-## [541] "BC_250471" "BC_56792"  "BC_229036" "BC_170407" "BC_154806" "BC_38465" 
-## [547] "BC_337697" "BC_230643" "BC_164579" "BC_146760" "BC_59243"  "BC_557872"
-## [553] "BC_652798" "BC_127999" "BC_4061"   "BC_22263"  "BC_22981"  "BC_66747" 
-## [559] "BC_8838"   "BC_23213"  "BC_49060"  "BC_29053"  "BC_535770" "BC_345175"
-## [565] "BC_7076"   "BC_29984"  "BC_153858" "BC_5615"   "BC_26097"  "BC_147873"
-## [571] "BC_19557"  "BC_19612"  "BC_14131"  "BC_100070" "BC_190202" "BC_125776"
-## [577] "BC_388103" "BC_29222"  "BC_509872" "BC_561471" "BC_57889"  "BC_168163"
-## [583] "BC_18218"  "BC_294271" "BC_65912"  "BC_4165"   "BC_345888" "BC_8159"  
-## [589] "BC_1534"   "BC_15172"  "BC_78760"  "BC_23363"  "BC_427925" "BC_65621" 
-## [595] "BC_41997"  "BC_109658"
-## 
-## 
-## $TopBarcodeCounts
-## $TopBarcodeCounts$`1_High_dose`
-##           1_High_dose
-## BC_79755      1107678
-## BC_102160      761271
-## BC_59493       435329
-## BC_23361       232003
-## BC_53234       222954
-## BC_400391      192477
-## BC_142112      187155
-## BC_94511       128591
-## BC_68847        62744
-## BC_142024       61392
-## BC_23241        49611
-## BC_47310        34916
-## BC_9141         34887
-## BC_5472         32110
-## BC_65104        31160
-## 
-## $TopBarcodeCounts$`10_High_dose`
-##           10_High_dose
-## BC_8419        1993924
-## BC_124796      1680405
-## BC_102160       206154
-## 
-## $TopBarcodeCounts$`11_Vehicle`
-##           11_Vehicle
-## BC_53234      676539
-## BC_205581     486994
-## BC_90135      325817
-## BC_172626     241604
-## BC_58978      182366
-## BC_79755      111942
-## BC_159570     105098
-## BC_118640      65182
-## BC_31610       63582
-## BC_635211      63026
-## BC_257382      60037
-## BC_324711      55954
-## BC_1607        55929
-## BC_4531        54568
-## BC_327422      32704
-## BC_297704      25038
-## BC_194823      24130
-## BC_415460      20811
-## BC_140320      17939
-## 
-## $TopBarcodeCounts$`12_Vehicle`
-##           12_Vehicle
-## BC_389078    1405728
-## BC_159570     586842
-## BC_135438     260286
-## BC_500780     240675
-## BC_79755      227341
-## BC_31610      178619
-## BC_49629      137913
-## BC_118640     126942
-## BC_47726      123364
-## BC_78349      122342
-## BC_58978      106717
-## BC_245016     103055
-## BC_1607       101604
-## BC_329679      76624
-## 
-## $TopBarcodeCounts$`13_Low_dose`
-##           13_Low_dose
-## BC_8419       1104437
-## BC_124796     1074874
-## BC_102160      790394
-## BC_257382      746244
-## BC_172626      109387
-## BC_345889       81047
-## BC_53234        69197
-## BC_5472         53875
-## BC_79755        53474
-## BC_205581       40089
-## BC_17608        39223
-## 
-## $TopBarcodeCounts$`14_Low_dose`
-##           14_Low_dose
-## BC_90135      1914931
-## BC_388103     1415098
-## BC_31610       305126
-## BC_58978       173933
-## BC_292962      126389
-## BC_159570       79752
-## 
-## $TopBarcodeCounts$`15_Low_dose`
-##           15_Low_dose
-## BC_1478       1163226
-## BC_94579       953114
-## BC_124796      492547
-## BC_8419        483327
-## BC_316204       95554
-## BC_400391       64143
-## BC_23241        43129
-## 
-## $TopBarcodeCounts$`16_Low_dose`
-##           16_Low_dose
-## BC_53234       961560
-## BC_79755       689442
-## BC_140320      613980
-## BC_31610       353626
-## BC_248880      304109
-## BC_1478        286142
-## BC_17608       217539
-## BC_257382      191984
-## BC_345889      189430
-## BC_94511       141895
-## BC_142112      139280
-## BC_142024      111530
-## BC_91412        96804
-## BC_58978        76756
-## BC_190202       66260
-## BC_4198         51301
-## BC_68847        50082
-## BC_389078       40573
-## BC_47726        37815
-## 
-## $TopBarcodeCounts$`17_High_dose`
-##           17_High_dose
-## BC_183691      2717363
-## BC_90135       1592156
-## BC_400391       596410
-## BC_31610        211787
-## BC_68847        152083
-## 
-## $TopBarcodeCounts$`18_Vehicle`
-##           18_Vehicle
-## BC_49397      790751
-## BC_91412      431176
-## BC_159570     426867
-## BC_62602      276110
-## BC_93485      261381
-## BC_70225      254975
-## BC_79755      209422
-## BC_4564       153377
-## BC_124796     138207
-## BC_58978      129417
-## BC_8419       129121
-## BC_149935     116170
-## BC_68847      115439
-## BC_90135      110019
-## BC_4173       104414
-## BC_4469       100734
-## BC_172626      95167
-## BC_31610       91203
-## BC_383329      79083
-## BC_55960       77254
-## BC_62970       60999
-## BC_102160      54950
-## BC_244320      41598
-## BC_400391      37075
-## BC_135438      32576
-## BC_203989      23509
-## BC_133178      20108
-## 
-## $TopBarcodeCounts$`2_High_dose`
-##           2_High_dose
-## BC_248880     1596729
-## BC_65104      1180121
-## BC_9141       1139782
-## BC_1478        706185
-## BC_31610       644411
-## BC_79755       385647
-## BC_388103      175121
-## BC_257382      160815
-## BC_68618        87664
-## BC_102160       62632
-## BC_4531         43062
-## BC_90135        35085
-## 
-## $TopBarcodeCounts$`3_High_dose`
-##           3_High_dose
-## BC_79755      2325114
-## BC_142024      631337
-## BC_102160      500221
-## BC_183691      230313
-## BC_59493       179949
-## BC_345889       98351
-## BC_23361        87369
-## BC_70225        61458
-## BC_30946        55306
-## BC_159570       53497
-## 
-## $TopBarcodeCounts$`4_Vehicle`
-##           4_Vehicle
-## BC_53234     600113
-## BC_58978     283022
-## BC_62602     231169
-## BC_68847     191550
-## BC_140320    184096
-## BC_79755     173832
-## BC_327422    164339
-## BC_36103     150193
-## BC_102160    122425
-## BC_81579     118058
-## BC_118640    110948
-## BC_135438     95015
-## BC_205581     82397
-## BC_1607       78418
-## BC_4564       76796
-## BC_91412      75495
-## BC_229324     51708
-## BC_560839     51599
-## BC_159570     51597
-## BC_468335     50798
-## BC_49397      41726
-## BC_203989     38296
-## BC_124796     31384
-## BC_8419       26856
-## BC_190202     22301
-## 
-## $TopBarcodeCounts$`5_Low_dose`
-##           5_Low_dose
-## BC_561471    2049491
-## BC_118640     306629
-## BC_59493      257272
-## BC_1607       242144
-## BC_31610      176241
-## BC_23361      128368
-## BC_159570      45318
-## BC_93485       30275
-## BC_468335      25238
-## BC_81579       24984
-## BC_257382      24269
-## 
-## $TopBarcodeCounts$`6_High_dose`
-##           6_High_dose
-## BC_79755      1313507
-## BC_8419       1120457
-## BC_124796     1013931
-## BC_4564        461928
-## BC_400391       93601
-## BC_31610        43251
-## BC_1478         33933
-## BC_90135        32480
-## BC_388103       29467
-## 
-## $TopBarcodeCounts$`7_Vehicle`
-##           7_Vehicle
-## BC_53234     903928
-## BC_54442     695015
-## BC_142024    453623
-## BC_140320    340812
-## BC_93485     313117
-## BC_124796    187546
-## BC_23544     176555
-## BC_8419      171530
-## BC_324711    157126
-## BC_159570    110265
-## BC_58978      96686
-## BC_90135      88718
-## BC_345889     57693
-## BC_4564       56267
-## BC_102160     50723
-## BC_400391     40763
-## 
-## $TopBarcodeCounts$`8_Vehicle`
-##           8_Vehicle
-## BC_142024   1166326
-## BC_90135     662506
-## BC_58978     308475
-## BC_93485     241349
-## BC_363879    186351
-## BC_4531      138174
-## BC_205581    128351
-## BC_118640    125295
-## BC_79755     112885
-## BC_31610      90763
-## BC_1607       89905
-## BC_82758      70527
-## BC_194823     48711
-## BC_389078     45367
-## BC_251167     45299
-## BC_68847      41855
-## BC_77209      39896
-## BC_102160     31578
-## BC_4173       25818
-## BC_4564       22920
-## BC_94579      20012
-## 
-## $TopBarcodeCounts$`9_Low_dose`
-##           9_Low_dose
-## BC_79755      681795
-## BC_4198       416270
-## BC_81579      227482
-## BC_118640     194256
-## BC_560839     189001
-## BC_345889     175817
-## BC_400391     171811
-## BC_1607       128762
-## BC_319105     107294
-## BC_35704       99211
-## BC_205581      92979
-## BC_90135       56017
-## BC_124796      52960
-## BC_142024      44150
-## BC_8419        41231
-## BC_561471      35822
-## BC_19146       29471
-## BC_68847       28059
-## BC_78912       19369
-## 
-## $TopBarcodeCounts$T0
-##               T0
-## BC_245016 229791
-## BC_121651 217035
-## BC_420967 135919
-## BC_108059 104092
-## BC_323670  88710
-## BC_9141    71948
-## BC_331193  58746
-## BC_119418  50353
-## BC_65104   44928
-## BC_17570   44319
-## BC_35704   42046
-## BC_92942   40055
-## BC_383329  38560
-## BC_1396    36561
-## BC_83680   36463
-## BC_24510   35588
-## BC_55858   32109
-## BC_38589   30544
-## BC_137495  27922
-## BC_446739  26850
-## BC_69627   26818
-## BC_313129  23810
-## BC_171385  23135
-## BC_280320  22617
-## BC_12367   22481
-## BC_221975  21723
-## BC_15782   21679
-## BC_310257  19781
-## BC_3618    17819
-## BC_67201   17468
-## BC_107789  17251
-## BC_36103   15445
-## BC_2596    15022
-## BC_215831  14165
-## BC_43535   13541
-## BC_69641   13517
-## BC_13611   13429
-## BC_16618   13410
-## BC_588849  13050
-## BC_294188  12892
-## BC_22306   12639
-## BC_141060  12371
-## BC_249402  12341
-## BC_428803  11779
-## BC_344050  11654
-## BC_25084   11583
-## BC_46538   11346
-## BC_175651  11293
-## BC_206193  11236
-## BC_205581  10974
-## BC_403084  10962
-## BC_56819   10940
-## BC_400391  10850
-## BC_16956   10763
-## BC_219379  10737
-## BC_19146   10234
-## BC_172626   9948
-## BC_18278    9561
-## BC_43128    9537
-## BC_47439    9501
-## BC_148563   9476
-## BC_114027   9237
-## BC_325065   9181
-## BC_62602    9167
-## BC_58978    9000
-## BC_252374   8738
-## BC_68847    8599
-## BC_262618   8455
-## BC_111272   8364
-## BC_224300   8275
-## BC_476380   8220
-## BC_289383   8174
-## BC_50136    8154
-## BC_202891   8124
-## BC_276866   8112
-## BC_521149   8104
-## BC_21018    7902
-## BC_550404   7724
-## BC_162898   7650
-## BC_255436   7647
-## BC_458543   7621
-## BC_168818   7611
-## BC_176946   7577
-## BC_425129   7376
-## BC_204266   7372
-## BC_103528   7202
-## BC_175871   7145
-## BC_83707    7076
-## BC_64584    7057
-## BC_105387   6847
-## BC_11643    6519
-## BC_34617    6449
-## BC_227720   6426
-## BC_72619    6404
-## BC_516753   6327
-## BC_168530   6204
-## BC_2040     6176
-## BC_556149   6128
-## BC_376665   6061
-## BC_18345    5987
-## BC_249161   5846
-## BC_345717   5778
-## BC_114040   5767
-## BC_24159    5744
-## BC_41692    5676
-## BC_248159   5671
-## BC_236596   5595
-## BC_166467   5526
-## BC_54442    5514
-## BC_206263   5499
-## BC_363714   5403
-## BC_56338    5073
-## BC_16754    5025
-## BC_30946    5005
-## BC_503475   4938
-## BC_11277    4875
-## BC_612467   4863
-## BC_67854    4809
-## BC_43673    4805
-## BC_160057   4794
-## BC_32325    4782
-## BC_7405     4719
-## BC_118831   4679
-## BC_418545   4661
-## BC_120596   4648
-## BC_5256     4643
-## BC_39281    4628
-## BC_258799   4591
-## BC_29284    4553
-## BC_85441    4534
-## BC_103714   4532
-## BC_37062    4505
-## BC_140320   4505
-## BC_20263    4464
-## BC_46524    4425
-## BC_321677   4409
-## BC_172412   4394
-## BC_139928   4370
-## BC_203520   4366
-## BC_105732   4315
-## BC_173736   4310
-## BC_13395    4244
-## BC_11391    4182
-## BC_142999   4102
-## BC_172238   4071
-## BC_79026    4055
-## BC_43123    4027
-## BC_128266   4010
-## BC_355420   3989
-## BC_415373   3989
-## BC_129519   3975
-## BC_3733     3965
-## BC_93485    3963
-## BC_40856    3941
-## BC_42612    3934
-## BC_289568   3933
-## BC_10454    3890
-## BC_38398    3876
-## BC_160158   3828
-## BC_32680    3822
-## BC_135438   3785
-## BC_346124   3761
-## BC_146666   3735
-## BC_42178    3703
-## BC_53234    3698
-## BC_356531   3677
-## BC_389078   3674
-## BC_635211   3668
-## BC_183401   3652
-## BC_132225   3650
-## BC_229995   3579
-## BC_102205   3564
-## BC_47726    3537
-## BC_564039   3502
-## BC_40516    3499
-## BC_45955    3495
-## BC_113839   3469
-## BC_102803   3468
-## BC_42310    3435
-## BC_8835     3425
-## BC_91412    3394
-## BC_15740    3388
-## BC_173674   3386
-## BC_44599    3373
-## BC_324711   3368
-## BC_519985   3294
-## BC_77030    3285
-## BC_350464   3253
-## BC_235128   3226
-## BC_151544   3214
-## BC_33699    3181
-## BC_474982   3168
-## BC_210529   3162
-## BC_217211   3130
-## BC_221056   3130
-## BC_187025   3128
-## BC_320233   3119
-## BC_7122     3112
-## BC_87047    3102
-## BC_76947    3053
-## BC_63846    3009
-## BC_560839   2990
-## BC_65130    2989
-## BC_2294     2986
-## BC_102160   2954
-## BC_273124   2945
-## BC_161563   2940
-## BC_389742   2920
-## BC_521485   2920
-## BC_145719   2911
-## BC_373901   2905
-## BC_162119   2891
-## BC_189813   2890
-## BC_17608    2878
-## BC_324953   2871
-## BC_320752   2863
-## BC_283335   2856
-## BC_67965    2840
-## BC_2865     2822
-## BC_242383   2817
-## BC_227308   2815
-## BC_346627   2779
-## BC_67233    2767
-## BC_672202   2748
-## BC_8324     2739
-## BC_211828   2737
-## BC_410172   2726
-## BC_495859   2711
-## BC_35659    2695
-## BC_199881   2675
-## BC_44600    2666
-## BC_377226   2649
-## BC_147006   2639
-## BC_41523    2631
-## BC_336794   2621
-## BC_26769    2607
-## BC_244320   2607
-## BC_183112   2600
-## BC_14377    2590
-## BC_7299     2571
-## BC_53380    2558
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    -
    -plotDetectedBarcodes(dge.filtered.collapsed, percentile = 1, plot = T, group = "Treatment")
    -

    -
    -plotDetectedBarcodes(dge.filtered.collapsed, percentile = 0.95, plot = T, group = "Treatment")
    -

    -

    These plots show that there are few clones that comprise the majority -of the dataset per mouse. Also, there are generally fewer clones present -in the high dose group compared to the vehicle or low dose groups.

    -
    -
    -

    Diversity analysis -

    -

    We can examine diversity in a few different ways. The most common are -Shannon, Simpson, Inverse Simpson and Gini. Each will be applicable in -different circumstances, however the Shannon diversity index is more -widely used to compare global diversity amongst populations of barcoded -cells.

    -
    -diversity <- calcDivIndexes(dge.filtered.collapsed$counts)
    -
    ## Joining with `by = join_by(name)`
    -
    -diversity
    -
    ##            name  shannon   simpson invsimpson      gini  group
-## 1   1_High_dose 2.348595 0.8444003   6.426747 0.9944996 Group1
-## 2  10_High_dose 1.225780 0.6032511   2.520486 0.9979181 Group1
-## 3    11_Vehicle 2.651924 0.8814260   8.433552 0.9924681 Group1
-## 4    12_Vehicle 2.414033 0.8399949   6.249800 0.9938575 Group1
-## 5   13_Low_dose 2.019197 0.8131725   5.352531 0.9958395 Group1
-## 6   14_Low_dose 1.519874 0.6814167   3.138896 0.9975312 Group1
-## 7   15_Low_dose 1.832136 0.7746698   4.437931 0.9965920 Group1
-## 8   16_Low_dose 2.778791 0.9048391  10.508519 0.9918608 Group1
-## 9  17_High_dose 1.534413 0.6789847   3.115116 0.9974705 Group1
-## 10   18_Vehicle 3.153732 0.9327037  14.859650 0.9879949 Group1
-## 11  2_High_dose 2.256218 0.8510409   6.713251 0.9949786 Group1
-## 12  3_High_dose 1.798233 0.6916675   3.243252 0.9964293 Group1
-## 13    4_Vehicle 3.122387 0.9318059  14.664035 0.9884237 Group1
-## 14   5_Low_dose 1.704952 0.6350916   2.740414 0.9964065 Group1
-## 15  6_High_dose 1.855001 0.7799122   4.543641 0.9963006 Group1
-## 16    7_Vehicle 2.605078 0.8891045   9.017500 0.9931725 Group1
-## 17    8_Vehicle 2.599803 0.8583799   7.061143 0.9924983 Group1
-## 18   9_Low_dose 2.747539 0.8980367   9.807451 0.9919927 Group1
-## 19           T0 5.503885 0.9851023  67.124565 0.8199223 Group1
    -
    -qplot(diversity$name, diversity$shannon) + theme_bw() + coord_flip()
    -
    ## Warning: `qplot()` was deprecated in ggplot2 3.4.0.
-## This warning is displayed once every 8 hours.
-## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
-## generated.
    -

    -
    -
    -

    Comparing abundance -

    -

    We can also statistically test for barcodes / tags that are over / -underrepresented in a group of samples relative to another using the -internal edgeR framework. bartools contains a convenience wrapper for -this functionality

    -
    -diff.bc <- compareAbundance(dge.filtered.collapsed, 
-                 condition = dge.filtered.collapsed$samples$Treatment, 
-                 condition_names = c("Vehicle", "High_dose"))
    -
    ## Error in compareAbundance(dge.filtered.collapsed, condition = dge.filtered.collapsed$samples$Treatment, : argument 2 matches multiple formal arguments
    -
    -diff.bc
    -
    ## Error in eval(expr, envir, enclos): object 'diff.bc' not found
    -
    -
    -

    Linegraphs of barcode abundance - NOT WORKING -

    -
    -plotAbundanceLines(dge.filtered.collapsed, condition = dge.filtered.collapsed$samples$group, condition_names = c("Vehicle","High_dose"), plot_type = 'counts')
    -
    -
    -
    -

    5. Session Info -

    - -
    ## R version 4.2.2 (2022-10-31)
-## Platform: aarch64-apple-darwin20 (64-bit)
-## Running under: macOS Ventura 13.1
-## 
-## Matrix products: default
-## BLAS:   /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRblas.0.dylib
-## LAPACK: /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRlapack.dylib
-## 
-## locale:
-## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
-## 
-## attached base packages:
-## [1] stats     graphics  grDevices utils     datasets  methods   base     
-## 
-## other attached packages:
-## [1] bartools_0.2.3 ggplot2_3.4.2  edgeR_3.40.2   limma_3.54.2  
-## 
-## loaded via a namespace (and not attached):
-##  [1] nlme_3.1-162          matrixStats_1.0.0     fs_1.6.2             
-##  [4] RColorBrewer_1.1-3    doParallel_1.0.17     rprojroot_2.0.3      
-##  [7] ggsci_3.0.0           tools_4.2.2           backports_1.4.1      
-## [10] bslib_0.5.0           utf8_1.2.3            R6_2.5.1             
-## [13] vegan_2.6-4           BiocGenerics_0.44.0   mgcv_1.9-0           
-## [16] colorspace_2.1-0      permute_0.9-7         GetoptLong_1.0.5     
-## [19] withr_2.5.0           tidyselect_1.2.0      compiler_4.2.2       
-## [22] textshaping_0.3.6     cli_3.6.1             desc_1.4.2           
-## [25] labeling_0.4.2        sass_0.4.6            scales_1.2.1         
-## [28] pkgdown_2.0.7         systemfonts_1.0.4     stringr_1.5.0        
-## [31] digest_0.6.33         rmarkdown_2.23        pkgconfig_2.0.3      
-## [34] htmltools_0.5.5       fastmap_1.1.1         highr_0.10           
-## [37] rlang_1.1.1           GlobalOptions_0.1.2   rstudioapi_0.15.0    
-## [40] shape_1.4.6           jquerylib_0.1.4       farver_2.1.1         
-## [43] generics_0.1.3        jsonlite_1.8.7        dplyr_1.1.2          
-## [46] car_3.1-2             magrittr_2.0.3        Matrix_1.6-0         
-## [49] S4Vectors_0.36.2      Rcpp_1.0.11           munsell_0.5.0        
-## [52] fansi_1.0.4           abind_1.4-5           lifecycle_1.0.3      
-## [55] stringi_1.7.12        yaml_2.3.7            carData_3.0-5        
-## [58] MASS_7.3-60           plyr_1.8.8            grid_4.2.2           
-## [61] parallel_4.2.2        forcats_1.0.0         crayon_1.5.2         
-## [64] lattice_0.21-8        splines_4.2.2         circlize_0.4.15      
-## [67] magick_2.7.4          locfit_1.5-9.8        knitr_1.43           
-## [70] ComplexHeatmap_2.14.0 pillar_1.9.0          ggpubr_0.6.0         
-## [73] rjson_0.2.21          ggsignif_0.6.4        stats4_4.2.2         
-## [76] reshape2_1.4.4        codetools_0.2-19      glue_1.6.2           
-## [79] evaluate_0.21         vctrs_0.6.3           png_0.1-8            
-## [82] foreach_1.5.2         gtable_0.3.3          purrr_1.0.1          
-## [85] tidyr_1.3.0           clue_0.3-64           cachem_1.0.8         
-## [88] xfun_0.39             broom_1.0.5           rstatix_0.7.2        
-## [91] ragg_1.2.5            tibble_3.2.1          iterators_1.0.14     
-## [94] ineq_0.2-13           memoise_2.0.1         IRanges_2.32.0       
-## [97] cluster_2.1.4
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  • - bartools: preprocessing datasets - bartools: quickstart guide + bartools: quickstart guide
  • diff --git a/docs/index.html b/docs/index.html deleted file mode 100644 index afe43e8..0000000 --- a/docs/index.html +++ /dev/null @@ -1,158 +0,0 @@ - - - - - - - - -A toolkit for the analysis and visualisation of synthetic DNA barcodes in high thorughput sequencing datasets • bartools - - - - - - - - - - Skip to contents - - -
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    bartools -

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    Tools for the analysis of cellular barcoding datasets -

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    Introduction -

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    Cellular barcoding is a powerful and widespread method to accurately track the progeny of a clone within a population of cells, enabling the dissection of biological phenomena at single cell resolution. However there remains a need for scalable and standardised open-source tools to pre-process and visualise cellular barcoding datasets. The bartools package is an R-based toolkit for the analysis of cellular barcoding information from high throughput sequencing datasets. The package consists of a suite of functions to annotate, analyse and plot DNA barcodes that are read out using common high throughput sequencing methodologies such as from Illumina machines. The bartools package is optimised for use with SPLINTR lineage barcode libraries however the functions within can be adapted to any cellular barcoding methodology that utiilses random DNA barcodes.

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    Installation -

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    You can install the development version of bartools from GitHub:

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    -if (!requireNamespace("devtools", quietly = TRUE)) {
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-devtools::install_github("DaneVass/bartools", dependencies = TRUE)
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    Getting started -

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    See vignette(bartools) for more details and usage examples.

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    Documentation -

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    See the Docs for full package documentation.

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    Looking for a dataset preprocessing pipeline? -

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    We have also developed BARtab, a pre-processing pipeline to automate the extraction and enumeration of barcode reads from raw sequence files. See the github for

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    Contact -

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    The bartools package was developed by Dane Vassiliadis. Please post any issues at https://github.com/DaneVass/bartools/issues

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    Developed by Dane Vassiliadis.

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    Site built with pkgdown 2.0.7.

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    - - - - - - - diff --git a/docs/news/index.html b/docs/news/index.html deleted file mode 100644 index 7be1f6f..0000000 --- a/docs/news/index.html +++ /dev/null @@ -1,85 +0,0 @@ - -Changelog • bartools - Skip to contents - - -
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    Changelog

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    bartools 0.2.1

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    • Minor updates to DESCRIPTION and imports. README.md fixed, .gitignore updated
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    bartools 0.2.0

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    • Updated plotDetectedBarcodes to color and reorder factors according to user input.
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    • Updated quickstart vignette to detail a complete analysis workflow.
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    bartools 0.1.0

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    • Updated plotBarcodeTimeseries to take top n barcodes. Speeds up plotting by only focusing on barcodes of interest
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    bartools 0.1.0

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    Developed by Dane Vassiliadis.

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    Site built with pkgdown 2.0.7.

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    Annotate Barcode Reads

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    Matches cells in a single cell experiment to detected DNA barcodes. -Dataframe returned will have all cells matched to a barcode -If there is no barcode matchable to a cell "not.detected" is returned -For cells that have multiple detected barcodes each barcode is returned separated by ';'

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    Usage

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    annotateBarcodeReads(all.cells, barcoded.cells)
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    list of all cells in the experiment to be annotated

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    dataframe a dataframe with cell id and barcode id as columns

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    Value

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    Returns a data-frame containing the the 10X cell ID and the DNA barcode ID.

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    Developed by Dane Vassiliadis.

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    Site built with pkgdown 2.0.7.

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    - - - - - - - diff --git a/docs/reference/calcDivIndexes.html b/docs/reference/calcDivIndexes.html deleted file mode 100644 index aa9056a..0000000 --- a/docs/reference/calcDivIndexes.html +++ /dev/null @@ -1,107 +0,0 @@ - -Calculate diversity indices for barcode samples — calcDivIndexes • bartools - Skip to contents - - -
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    Calculate diversity indices for barcode samples

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    Takes a dataframe of barcode counts and computes shannon, -simpson, inverse simpson and gini coefficients for each -sample

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    dataframe of raw or normalised counts with samples as columns and observations/barcodes as rows

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    Details

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    calcDivIndexes

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    Calculates common diversity indices per sample in a dataframe of counts

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    Examples

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    indexes <- calcDivIndexes(dat = test.counts)
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    Developed by Dane Vassiliadis.

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    - - - - - - - diff --git a/docs/reference/calcPercentileBarcodes.html b/docs/reference/calcPercentileBarcodes.html deleted file mode 100644 index 28926fa..0000000 --- a/docs/reference/calcPercentileBarcodes.html +++ /dev/null @@ -1,2317 +0,0 @@ - -calcPercentileBarcodes — calcPercentileBarcodes • bartools - Skip to contents - - -
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    calcPercentileBarcodes

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    Calculate barcodes comprising the Nth percentile for each sample & generate cumulative sum plots

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    DGEList object containing raw or normalised barcode counts

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    desired percentile value

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    Examples

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    data(test.dge)
-calcPercentileBarcodes(test.dge, percentile = .95)
-#> $NumBarcodes
-#>    Sample NumBarcodes
-#> 1    T0-1         593
-#> 2    T0-2         590
-#> 3   S10-1           3
-#> 4   S10-2           4
-#> 5   S11-1          19
-#> 6   S11-2          20
-#> 7   S12-1          14
-#> 8   S12-2          14
-#> 9   S13-1          10
-#> 10  S13-2          11
-#> 11  S14-1           6
-#> 12  S14-2           6
-#> 13  S15-1           7
-#> 14  S15-2           7
-#> 15  S16-1          19
-#> 16  S16-2          20
-#> 17  S17-1           5
-#> 18  S17-2           6
-#> 19  S18-1          27
-#> 20  S18-2          27
-#> 21   S1-1          14
-#> 22   S1-2          15
-#> 23   S2-1          12
-#> 24   S2-2          11
-#> 25   S3-1          10
-#> 26   S3-2          10
-#> 27   S4-1          25
-#> 28   S4-2          26
-#> 29   S5-1          11
-#> 30   S5-2          12
-#> 31   S6-1           9
-#> 32   S6-2          10
-#> 33   S7-1          16
-#> 34   S7-2          16
-#> 35   S8-1          20
-#> 36   S8-2          21
-#> 37   S9-1          19
-#> 38   S9-2          19
-#> 
-#> $TopBarcodes
-#> $TopBarcodes$`T0-1`
-#>   [1] "BC_245016" "BC_121651" "BC_420967" "BC_108059" "BC_323670" "BC_9141"  
-#>   [7] "BC_331193" "BC_119418" "BC_65104"  "BC_17570"  "BC_35704"  "BC_383329"
-#>  [13] "BC_92942"  "BC_24510"  "BC_1396"   "BC_83680"  "BC_55858"  "BC_38589" 
-#>  [19] "BC_137495" "BC_69627"  "BC_446739" "BC_171385" "BC_12367"  "BC_280320"
-#>  [25] "BC_15782"  "BC_310257" "BC_221975" "BC_313129" "BC_67201"  "BC_3618"  
-#>  [31] "BC_107789" "BC_36103"  "BC_2596"   "BC_69641"  "BC_215831" "BC_16618" 
-#>  [37] "BC_249402" "BC_141060" "BC_13611"  "BC_22306"  "BC_588849" "BC_43535" 
-#>  [43] "BC_16956"  "BC_175651" "BC_294188" "BC_46538"  "BC_344050" "BC_25084" 
-#>  [49] "BC_56819"  "BC_206193" "BC_428803" "BC_219379" "BC_400391" "BC_19146" 
-#>  [55] "BC_205581" "BC_148563" "BC_114027" "BC_172626" "BC_403084" "BC_62602" 
-#>  [61] "BC_224300" "BC_47439"  "BC_18278"  "BC_43128"  "BC_68847"  "BC_111272"
-#>  [67] "BC_58978"  "BC_162898" "BC_476380" "BC_262618" "BC_276866" "BC_252374"
-#>  [73] "BC_550404" "BC_325065" "BC_202891" "BC_50136"  "BC_255436" "BC_103528"
-#>  [79] "BC_425129" "BC_168818" "BC_204266" "BC_176946" "BC_521149" "BC_105387"
-#>  [85] "BC_227720" "BC_83707"  "BC_64584"  "BC_21018"  "BC_458543" "BC_11643" 
-#>  [91] "BC_175871" "BC_556149" "BC_34617"  "BC_166467" "BC_41692"  "BC_72619" 
-#>  [97] "BC_168530" "BC_289383" "BC_2040"   "BC_516753" "BC_24159"  "BC_249161"
-#> [103] "BC_345717" "BC_54442"  "BC_236596" "BC_248159" "BC_206263" "BC_376665"
-#> [109] "BC_18345"  "BC_114040" "BC_612467" "BC_56338"  "BC_363714" "BC_39281" 
-#> [115] "BC_30946"  "BC_32325"  "BC_418545" "BC_103714" "BC_140320" "BC_120596"
-#> [121] "BC_503475" "BC_160057" "BC_16754"  "BC_43673"  "BC_173736" "BC_46524" 
-#> [127] "BC_142999" "BC_11277"  "BC_67854"  "BC_258799" "BC_13395"  "BC_37062" 
-#> [133] "BC_38398"  "BC_105732" "BC_139928" "BC_355420" "BC_40856"  "BC_128266"
-#> [139] "BC_321677" "BC_203520" "BC_29284"  "BC_172412" "BC_85441"  "BC_20263" 
-#> [145] "BC_118831" "BC_10454"  "BC_5256"   "BC_129519" "BC_11391"  "BC_172238"
-#> [151] "BC_43123"  "BC_42178"  "BC_102205" "BC_346124" "BC_389078" "BC_42612" 
-#> [157] "BC_93485"  "BC_415373" "BC_3733"   "BC_32680"  "BC_474982" "BC_7405"  
-#> [163] "BC_289568" "BC_229995" "BC_79026"  "BC_45955"  "BC_146666" "BC_183401"
-#> [169] "BC_356531" "BC_187025" "BC_42310"  "BC_221056" "BC_132225" "BC_235128"
-#> [175] "BC_15740"  "BC_160158" "BC_320233" "BC_8835"   "BC_173674" "BC_53234" 
-#> [181] "BC_91412"  "BC_135438" "BC_373901" "BC_44599"  "BC_87047"  "BC_350464"
-#> [187] "BC_324711" "BC_40516"  "BC_113839" "BC_162119" "BC_33699"  "BC_635211"
-#> [193] "BC_346627" "BC_7122"   "BC_806"    "BC_63846"  "BC_283335" "BC_151544"
-#> [199] "BC_273124" "BC_227308" "BC_47726"  "BC_102803" "BC_244320" "BC_10347" 
-#> [205] "BC_389742" "BC_2865"   "BC_77030"  "BC_65130"  "BC_564039" "BC_519985"
-#> [211] "BC_76947"  "BC_4564"   "BC_521485" "BC_495859" "BC_35659"  "BC_14377" 
-#> [217] "BC_199881" "BC_147006" "BC_26769"  "BC_100344" "BC_672202" "BC_146146"
-#> [223] "BC_320752" "BC_324953" "BC_217211" "BC_67965"  "BC_210529" "BC_189813"
-#> [229] "BC_242383" "BC_560839" "BC_228994" "BC_389087" "BC_44600"  "BC_20557" 
-#> [235] "BC_10808"  "BC_138900" "BC_81309"  "BC_145719" "BC_58144"  "BC_7299"  
-#> [241] "BC_78349"  "BC_102160" "BC_164378" "BC_211828" "BC_336794" "BC_53380" 
-#> [247] "BC_14492"  "BC_23241"  "BC_57621"  "BC_11270"  "BC_41523"  "BC_33764" 
-#> [253] "BC_12191"  "BC_17608"  "BC_8324"   "BC_183112" "BC_167638" "BC_2206"  
-#> [259] "BC_123791" "BC_377226" "BC_195246" "BC_161563" "BC_67233"  "BC_421415"
-#> [265] "BC_150291" "BC_408928" "BC_410172" "BC_121663" "BC_201293" "BC_156353"
-#> [271] "BC_45652"  "BC_357846" "BC_199093" "BC_214878" "BC_6093"   "BC_214604"
-#> [277] "BC_302238" "BC_521264" "BC_672021" "BC_2294"   "BC_587139" "BC_638624"
-#> [283] "BC_86358"  "BC_1764"   "BC_366637" "BC_218093" "BC_201372" "BC_19353" 
-#> [289] "BC_130431" "BC_22999"  "BC_49044"  "BC_154519" "BC_324042" "BC_37363" 
-#> [295] "BC_154990" "BC_350335" "BC_194823" "BC_23522"  "BC_141246" "BC_4531"  
-#> [301] "BC_4733"   "BC_654016" "BC_147380" "BC_196282" "BC_52081"  "BC_207651"
-#> [307] "BC_380559" "BC_527469" "BC_353112" "BC_426662" "BC_655329" "BC_73098" 
-#> [313] "BC_146322" "BC_596629" "BC_64945"  "BC_462254" "BC_261055" "BC_145403"
-#> [319] "BC_61489"  "BC_617554" "BC_228018" "BC_249649" "BC_211811" "BC_101581"
-#> [325] "BC_43206"  "BC_326596" "BC_514247" "BC_88464"  "BC_203989" "BC_206798"
-#> [331] "BC_19610"  "BC_9626"   "BC_116921" "BC_181554" "BC_79755"  "BC_77101" 
-#> [337] "BC_55675"  "BC_83816"  "BC_138737" "BC_322542" "BC_318282" "BC_444573"
-#> [343] "BC_9340"   "BC_49226"  "BC_57692"  "BC_93884"  "BC_181957" "BC_217320"
-#> [349] "BC_296283" "BC_3583"   "BC_11055"  "BC_95934"  "BC_88099"  "BC_35283" 
-#> [355] "BC_67374"  "BC_175957" "BC_266451" "BC_283541" "BC_14484"  "BC_14810" 
-#> [361] "BC_157906" "BC_354350" "BC_539228" "BC_61500"  "BC_45402"  "BC_234390"
-#> [367] "BC_415903" "BC_12418"  "BC_238160" "BC_29208"  "BC_91989"  "BC_92584" 
-#> [373] "BC_351660" "BC_381245" "BC_31196"  "BC_47310"  "BC_20573"  "BC_64931" 
-#> [379] "BC_2430"   "BC_15448"  "BC_94579"  "BC_171953" "BC_9322"   "BC_587343"
-#> [385] "BC_50222"  "BC_53219"  "BC_77187"  "BC_7691"   "BC_225019" "BC_238577"
-#> [391] "BC_6304"   "BC_13093"  "BC_154696" "BC_275321" "BC_75688"  "BC_10293" 
-#> [397] "BC_13"     "BC_90135"  "BC_357977" "BC_4198"   "BC_147556" "BC_231962"
-#> [403] "BC_175198" "BC_144061" "BC_26258"  "BC_114257" "BC_194092" "BC_31779" 
-#> [409] "BC_268745" "BC_67247"  "BC_82595"  "BC_103862" "BC_149347" "BC_346761"
-#> [415] "BC_473006" "BC_49397"  "BC_302454" "BC_207333" "BC_31185"  "BC_115436"
-#> [421] "BC_124624" "BC_491991" "BC_309303" "BC_12410"  "BC_228333" "BC_528062"
-#> [427] "BC_363879" "BC_70909"  "BC_563624" "BC_11152"  "BC_446649" "BC_129768"
-#> [433] "BC_129559" "BC_169627" "BC_21535"  "BC_516611" "BC_57582"  "BC_159570"
-#> [439] "BC_130861" "BC_48168"  "BC_66948"  "BC_257382" "BC_179919" "BC_15707" 
-#> [445] "BC_17509"  "BC_444761" "BC_57794"  "BC_147063" "BC_52856"  "BC_14452" 
-#> [451] "BC_316483" "BC_186401" "BC_90599"  "BC_245600" "BC_1977"   "BC_99"    
-#> [457] "BC_176231" "BC_472019" "BC_292962" "BC_221691" "BC_170233" "BC_182320"
-#> [463] "BC_381050" "BC_28938"  "BC_67370"  "BC_110945" "BC_195887" "BC_257523"
-#> [469] "BC_2762"   "BC_255928" "BC_355439" "BC_33035"  "BC_125634" "BC_430"   
-#> [475] "BC_146760" "BC_164579" "BC_345889" "BC_142112" "BC_515934" "BC_141114"
-#> [481] "BC_20281"  "BC_322219" "BC_248582" "BC_1593"   "BC_9747"   "BC_82758" 
-#> [487] "BC_329007" "BC_567519" "BC_229036" "BC_50311"  "BC_576338" "BC_189854"
-#> [493] "BC_25609"  "BC_56489"  "BC_10190"  "BC_41280"  "BC_370968" "BC_162322"
-#> [499] "BC_337697" "BC_6453"   "BC_186267" "BC_222891" "BC_297704" "BC_145103"
-#> [505] "BC_81579"  "BC_35633"  "BC_170407" "BC_62970"  "BC_120505" "BC_66747" 
-#> [511] "BC_413466" "BC_329679" "BC_454109" "BC_37116"  "BC_389378" "BC_4061"  
-#> [517] "BC_163063" "BC_125776" "BC_38465"  "BC_278096" "BC_288976" "BC_171345"
-#> [523] "BC_557872" "BC_127999" "BC_259621" "BC_314343" "BC_80020"  "BC_228451"
-#> [529] "BC_282868" "BC_182092" "BC_56792"  "BC_143062" "BC_219703" "BC_235649"
-#> [535] "BC_131019" "BC_561471" "BC_22981"  "BC_567514" "BC_345175" "BC_345888"
-#> [541] "BC_7076"   "BC_220827" "BC_23213"  "BC_73196"  "BC_2142"   "BC_41997" 
-#> [547] "BC_147873" "BC_19557"  "BC_29053"  "BC_230643" "BC_28077"  "BC_22263" 
-#> [553] "BC_29984"  "BC_215667" "BC_69571"  "BC_8838"   "BC_275938" "BC_413636"
-#> [559] "BC_2791"   "BC_26462"  "BC_29222"  "BC_4173"   "BC_89937"  "BC_8159"  
-#> [565] "BC_413271" "BC_14131"  "BC_59243"  "BC_190202" "BC_294271" "BC_23918" 
-#> [571] "BC_154806" "BC_214774" "BC_26384"  "BC_57471"  "BC_1534"   "BC_451991"
-#> [577] "BC_15172"  "BC_109658" "BC_483925" "BC_71820"  "BC_330625" "BC_153858"
-#> [583] "BC_319910" "BC_459545" "BC_53645"  "BC_84350"  "BC_427925" "BC_75310" 
-#> [589] "BC_5615"   "BC_142652" "BC_50626"  "BC_107381" "BC_8715"  
-#> 
-#> $TopBarcodes$`T0-2`
-#>   [1] "BC_245016" "BC_121651" "BC_420967" "BC_108059" "BC_323670" "BC_9141"  
-#>   [7] "BC_331193" "BC_119418" "BC_65104"  "BC_17570"  "BC_35704"  "BC_92942" 
-#>  [13] "BC_383329" "BC_83680"  "BC_1396"   "BC_24510"  "BC_55858"  "BC_38589" 
-#>  [19] "BC_313129" "BC_446739" "BC_137495" "BC_69627"  "BC_171385" "BC_280320"
-#>  [25] "BC_221975" "BC_12367"  "BC_15782"  "BC_3618"   "BC_310257" "BC_107789"
-#>  [31] "BC_67201"  "BC_36103"  "BC_215831" "BC_43535"  "BC_2596"   "BC_13611" 
-#>  [37] "BC_294188" "BC_588849" "BC_16618"  "BC_69641"  "BC_22306"  "BC_428803"
-#>  [43] "BC_403084" "BC_141060" "BC_205581" "BC_249402" "BC_206193" "BC_25084" 
-#>  [49] "BC_344050" "BC_400391" "BC_325065" "BC_219379" "BC_46538"  "BC_289383"
-#>  [55] "BC_56819"  "BC_43128"  "BC_175651" "BC_18278"  "BC_172626" "BC_19146" 
-#>  [61] "BC_47439"  "BC_252374" "BC_58978"  "BC_21018"  "BC_521149" "BC_16956" 
-#>  [67] "BC_262618" "BC_50136"  "BC_202891" "BC_148563" "BC_62602"  "BC_458543"
-#>  [73] "BC_68847"  "BC_114027" "BC_476380" "BC_276866" "BC_111272" "BC_176946"
-#>  [79] "BC_255436" "BC_168818" "BC_550404" "BC_175871" "BC_204266" "BC_64584" 
-#>  [85] "BC_425129" "BC_83707"  "BC_162898" "BC_224300" "BC_103528" "BC_34617" 
-#>  [91] "BC_72619"  "BC_516753" "BC_105387" "BC_376665" "BC_18345"  "BC_2040"  
-#>  [97] "BC_168530" "BC_11643"  "BC_114040" "BC_227720" "BC_556149" "BC_7405"  
-#> [103] "BC_249161" "BC_345717" "BC_363714" "BC_248159" "BC_24159"  "BC_236596"
-#> [109] "BC_16754"  "BC_206263" "BC_118831" "BC_54442"  "BC_41692"  "BC_503475"
-#> [115] "BC_5256"   "BC_11277"  "BC_67854"  "BC_30946"  "BC_43673"  "BC_29284" 
-#> [121] "BC_85441"  "BC_160057" "BC_166467" "BC_56338"  "BC_20263"  "BC_258799"
-#> [127] "BC_172412" "BC_32325"  "BC_321677" "BC_79026"  "BC_37062"  "BC_120596"
-#> [133] "BC_203520" "BC_418545" "BC_135438" "BC_139928" "BC_160158" "BC_635211"
-#> [139] "BC_612467" "BC_11391"  "BC_564039" "BC_47726"  "BC_105732" "BC_46524" 
-#> [145] "BC_53234"  "BC_103714" "BC_140320" "BC_289568" "BC_415373" "BC_102803"
-#> [151] "BC_39281"  "BC_3733"   "BC_172238" "BC_93485"  "BC_13395"  "BC_43123" 
-#> [157] "BC_2294"   "BC_146666" "BC_132225" "BC_40516"  "BC_173736" "BC_42612" 
-#> [163] "BC_356531" "BC_113839" "BC_519985" "BC_129519" "BC_77030"  "BC_32680" 
-#> [169] "BC_183401" "BC_128266" "BC_210529" "BC_161563" "BC_355420" "BC_217211"
-#> [175] "BC_324711" "BC_10454"  "BC_91412"  "BC_44599"  "BC_40856"  "BC_8835"  
-#> [181] "BC_142999" "BC_173674" "BC_151544" "BC_229995" "BC_346124" "BC_42310" 
-#> [187] "BC_102160" "BC_45955"  "BC_15740"  "BC_17608"  "BC_350464" "BC_560839"
-#> [193] "BC_389078" "BC_76947"  "BC_33699"  "BC_145719" "BC_42178"  "BC_67233" 
-#> [199] "BC_38398"  "BC_7122"   "BC_410172" "BC_65130"  "BC_189813" "BC_8324"  
-#> [205] "BC_324953" "BC_521485" "BC_235128" "BC_63846"  "BC_320752" "BC_87047" 
-#> [211] "BC_67965"  "BC_102205" "BC_242383" "BC_377226" "BC_389742" "BC_211828"
-#> [217] "BC_273124" "BC_23522"  "BC_154990" "BC_320233" "BC_41523"  "BC_183112"
-#> [223] "BC_221056" "BC_2865"   "BC_672202" "BC_187025" "BC_326596" "BC_283335"
-#> [229] "BC_336794" "BC_44600"  "BC_227308" "BC_156353" "BC_207651" "BC_408928"
-#> [235] "BC_495859" "BC_162119" "BC_64931"  "BC_7299"   "BC_53380"  "BC_35659" 
-#> [241] "BC_101581" "BC_4733"   "BC_199881" "BC_57621"  "BC_373901" "BC_164378"
-#> [247] "BC_147006" "BC_214604" "BC_45652"  "BC_154519" "BC_64945"  "BC_346627"
-#> [253] "BC_201293" "BC_366637" "BC_26769"  "BC_474982" "BC_206798" "BC_9340"  
-#> [259] "BC_19353"  "BC_95934"  "BC_20557"  "BC_14377"  "BC_6093"   "BC_33764" 
-#> [265] "BC_389087" "BC_130861" "BC_157906" "BC_462254" "BC_244320" "BC_1764"  
-#> [271] "BC_123791" "BC_37363"  "BC_86358"  "BC_194823" "BC_211811" "BC_53219" 
-#> [277] "BC_421415" "BC_100344" "BC_2206"   "BC_78349"  "BC_221691" "BC_116921"
-#> [283] "BC_228994" "BC_10347"  "BC_514247" "BC_175198" "BC_266451" "BC_638624"
-#> [289] "BC_4531"   "BC_10808"  "BC_88464"  "BC_228018" "BC_196282" "BC_81309" 
-#> [295] "BC_167638" "BC_130431" "BC_346761" "BC_77101"  "BC_672021" "BC_446649"
-#> [301] "BC_12418"  "BC_22999"  "BC_350335" "BC_14492"  "BC_806"    "BC_141114"
-#> [307] "BC_138737" "BC_10293"  "BC_175957" "BC_14810"  "BC_444573" "BC_521264"
-#> [313] "BC_147380" "BC_380559" "BC_539228" "BC_162322" "BC_91989"  "BC_145403"
-#> [319] "BC_57692"  "BC_61489"  "BC_58144"  "BC_4564"   "BC_77187"  "BC_121663"
-#> [325] "BC_73098"  "BC_141246" "BC_195246" "BC_49226"  "BC_171953" "BC_181957"
-#> [331] "BC_214878" "BC_182320" "BC_114257" "BC_146146" "BC_6304"   "BC_181554"
-#> [337] "BC_13093"  "BC_528062" "BC_587139" "BC_55675"  "BC_23241"  "BC_11270" 
-#> [343] "BC_20573"  "BC_29208"  "BC_218093" "BC_654016" "BC_150291" "BC_292962"
-#> [349] "BC_217320" "BC_199093" "BC_138900" "BC_225019" "BC_12191"  "BC_354350"
-#> [355] "BC_115436" "BC_99"     "BC_93884"  "BC_234390" "BC_527469" "BC_351660"
-#> [361] "BC_201372" "BC_3583"   "BC_19610"  "BC_587343" "BC_353112" "BC_15448" 
-#> [367] "BC_357977" "BC_2430"   "BC_617554" "BC_11152"  "BC_31779"  "BC_330625"
-#> [373] "BC_147556" "BC_88099"  "BC_2791"   "BC_67247"  "BC_110945" "BC_50222" 
-#> [379] "BC_147063" "BC_9626"   "BC_1593"   "BC_426662" "BC_413271" "BC_49044" 
-#> [385] "BC_4198"   "BC_164652" "BC_125634" "BC_322542" "BC_203989" "BC_67374" 
-#> [391] "BC_45402"  "BC_14484"  "BC_195887" "BC_9322"   "BC_415903" "BC_82595" 
-#> [397] "BC_329007" "BC_146322" "BC_261055" "BC_90135"  "BC_26258"  "BC_363879"
-#> [403] "BC_515934" "BC_11055"  "BC_49397"  "BC_381245" "BC_309303" "BC_12410" 
-#> [409] "BC_249649" "BC_318282" "BC_57794"  "BC_67370"  "BC_316483" "BC_31196" 
-#> [415] "BC_357846" "BC_9747"   "BC_83816"  "BC_26384"  "BC_52856"  "BC_302454"
-#> [421] "BC_50311"  "BC_282868" "BC_61500"  "BC_238577" "BC_47310"  "BC_31185" 
-#> [427] "BC_283541" "BC_79755"  "BC_231962" "BC_56489"  "BC_120505" "BC_94579" 
-#> [433] "BC_444761" "BC_70909"  "BC_296283" "BC_15652"  "BC_276301" "BC_322219"
-#> [439] "BC_169627" "BC_84350"  "BC_596629" "BC_21535"  "BC_52081"  "BC_7691"  
-#> [445] "BC_302238" "BC_165975" "BC_14380"  "BC_144061" "BC_129559" "BC_491991"
-#> [451] "BC_43206"  "BC_35459"  "BC_228333" "BC_176231" "BC_75688"  "BC_516611"
-#> [457] "BC_149347" "BC_220827" "BC_207333" "BC_159570" "BC_288976" "BC_81579" 
-#> [463] "BC_44350"  "BC_145103" "BC_35283"  "BC_250471" "BC_97874"  "BC_567519"
-#> [469] "BC_454109" "BC_355439" "BC_430"    "BC_57582"  "BC_48168"  "BC_10190" 
-#> [475] "BC_19612"  "BC_92584"  "BC_212837" "BC_129768" "BC_345889" "BC_115038"
-#> [481] "BC_228451" "BC_451991" "BC_179919" "BC_576338" "BC_655329" "BC_472019"
-#> [487] "BC_168163" "BC_35633"  "BC_257382" "BC_535770" "BC_329679" "BC_248582"
-#> [493] "BC_62970"  "BC_324042" "BC_18218"  "BC_182092" "BC_142112" "BC_4173"  
-#> [499] "BC_100070" "BC_259621" "BC_567514" "BC_103862" "BC_28938"  "BC_325871"
-#> [505] "BC_49060"  "BC_275321" "BC_186267" "BC_652798" "BC_1977"   "BC_238160"
-#> [511] "BC_189854" "BC_37116"  "BC_255928" "BC_268745" "BC_23918"  "BC_131019"
-#> [517] "BC_171345" "BC_278096" "BC_13"     "BC_82758"  "BC_26097"  "BC_65912" 
-#> [523] "BC_41280"  "BC_245600" "BC_509872" "BC_15707"  "BC_17509"  "BC_154696"
-#> [529] "BC_194092" "BC_28077"  "BC_4165"   "BC_154806" "BC_25609"  "BC_33035" 
-#> [535] "BC_1139"   "BC_80020"  "BC_388103" "BC_59243"  "BC_20281"  "BC_57889" 
-#> [541] "BC_143062" "BC_218837" "BC_179997" "BC_90599"  "BC_5615"   "BC_23363" 
-#> [547] "BC_352645" "BC_292621" "BC_153858" "BC_230643" "BC_297704" "BC_381050"
-#> [553] "BC_203210" "BC_8838"   "BC_183115" "BC_257523" "BC_22263"  "BC_56792" 
-#> [559] "BC_163063" "BC_184732" "BC_563624" "BC_77131"  "BC_78760"  "BC_370968"
-#> [565] "BC_307924" "BC_331889" "BC_389378" "BC_29053"  "BC_23213"  "BC_428204"
-#> [571] "BC_500299" "BC_14452"  "BC_124624" "BC_22981"  "BC_98171"  "BC_170233"
-#> [577] "BC_38465"  "BC_47704"  "BC_198370" "BC_14131"  "BC_190202" "BC_278655"
-#> [583] "BC_6453"   "BC_557872" "BC_31562"  "BC_128748" "BC_29984"  "BC_294176"
-#> [589] "BC_127999" "BC_796"   
-#> 
-#> $TopBarcodes$`S10-1`
-#> [1] "BC_8419"   "BC_124796" "BC_102160"
-#> 
-#> $TopBarcodes$`S10-2`
-#> [1] "BC_8419"   "BC_124796" "BC_102160" "BC_68847" 
-#> 
-#> $TopBarcodes$`S11-1`
-#>  [1] "BC_53234"  "BC_205581" "BC_90135"  "BC_172626" "BC_58978"  "BC_79755" 
-#>  [7] "BC_159570" "BC_118640" "BC_31610"  "BC_257382" "BC_1607"   "BC_635211"
-#> [13] "BC_4531"   "BC_324711" "BC_327422" "BC_297704" "BC_194823" "BC_415460"
-#> [19] "BC_140320"
-#> 
-#> $TopBarcodes$`S11-2`
-#>  [1] "BC_53234"  "BC_205581" "BC_90135"  "BC_172626" "BC_58978"  "BC_79755" 
-#>  [7] "BC_159570" "BC_635211" "BC_118640" "BC_324711" "BC_31610"  "BC_257382"
-#> [13] "BC_4531"   "BC_1607"   "BC_327422" "BC_297704" "BC_194823" "BC_415460"
-#> [19] "BC_62602"  "BC_140320"
-#> 
-#> $TopBarcodes$`S12-1`
-#>  [1] "BC_389078" "BC_159570" "BC_135438" "BC_500780" "BC_79755"  "BC_31610" 
-#>  [7] "BC_49629"  "BC_78349"  "BC_118640" "BC_47726"  "BC_58978"  "BC_1607"  
-#> [13] "BC_245016" "BC_329679"
-#> 
-#> $TopBarcodes$`S12-2`
-#>  [1] "BC_389078" "BC_159570" "BC_135438" "BC_500780" "BC_79755"  "BC_31610" 
-#>  [7] "BC_49629"  "BC_118640" "BC_47726"  "BC_78349"  "BC_245016" "BC_58978" 
-#> [13] "BC_1607"   "BC_329679"
-#> 
-#> $TopBarcodes$`S13-1`
-#>  [1] "BC_8419"   "BC_124796" "BC_102160" "BC_257382" "BC_172626" "BC_345889"
-#>  [7] "BC_53234"  "BC_79755"  "BC_5472"   "BC_205581"
-#> 
-#> $TopBarcodes$`S13-2`
-#>  [1] "BC_8419"   "BC_124796" "BC_102160" "BC_257382" "BC_172626" "BC_345889"
-#>  [7] "BC_53234"  "BC_5472"   "BC_79755"  "BC_17608"  "BC_205581"
-#> 
-#> $TopBarcodes$`S14-1`
-#> [1] "BC_90135"  "BC_388103" "BC_31610"  "BC_58978"  "BC_292962" "BC_159570"
-#> 
-#> $TopBarcodes$`S14-2`
-#> [1] "BC_90135"  "BC_388103" "BC_31610"  "BC_58978"  "BC_292962" "BC_159570"
-#> 
-#> $TopBarcodes$`S15-1`
-#> [1] "BC_1478"   "BC_94579"  "BC_124796" "BC_8419"   "BC_316204" "BC_400391"
-#> [7] "BC_23241" 
-#> 
-#> $TopBarcodes$`S15-2`
-#> [1] "BC_1478"   "BC_94579"  "BC_124796" "BC_8419"   "BC_316204" "BC_400391"
-#> [7] "BC_23241" 
-#> 
-#> $TopBarcodes$`S16-1`
-#>  [1] "BC_53234"  "BC_79755"  "BC_140320" "BC_31610"  "BC_248880" "BC_1478"  
-#>  [7] "BC_17608"  "BC_257382" "BC_345889" "BC_94511"  "BC_142112" "BC_142024"
-#> [13] "BC_91412"  "BC_58978"  "BC_190202" "BC_4198"   "BC_68847"  "BC_389078"
-#> [19] "BC_47726" 
-#> 
-#> $TopBarcodes$`S16-2`
-#>  [1] "BC_53234"  "BC_79755"  "BC_140320" "BC_31610"  "BC_248880" "BC_1478"  
-#>  [7] "BC_17608"  "BC_257382" "BC_345889" "BC_94511"  "BC_142112" "BC_142024"
-#> [13] "BC_91412"  "BC_58978"  "BC_190202" "BC_68847"  "BC_4198"   "BC_389078"
-#> [19] "BC_47726"  "BC_205581"
-#> 
-#> $TopBarcodes$`S17-1`
-#> [1] "BC_183691" "BC_90135"  "BC_400391" "BC_31610"  "BC_68847" 
-#> 
-#> $TopBarcodes$`S17-2`
-#> [1] "BC_183691" "BC_90135"  "BC_400391" "BC_31610"  "BC_68847"  "BC_53234" 
-#> 
-#> $TopBarcodes$`S18-1`
-#>  [1] "BC_49397"  "BC_91412"  "BC_159570" "BC_62602"  "BC_93485"  "BC_70225" 
-#>  [7] "BC_79755"  "BC_4564"   "BC_124796" "BC_58978"  "BC_8419"   "BC_149935"
-#> [13] "BC_68847"  "BC_90135"  "BC_4173"   "BC_4469"   "BC_172626" "BC_31610" 
-#> [19] "BC_383329" "BC_55960"  "BC_62970"  "BC_102160" "BC_244320" "BC_400391"
-#> [25] "BC_135438" "BC_203989" "BC_81579" 
-#> 
-#> $TopBarcodes$`S18-2`
-#>  [1] "BC_49397"  "BC_91412"  "BC_159570" "BC_62602"  "BC_70225"  "BC_93485" 
-#>  [7] "BC_79755"  "BC_4564"   "BC_124796" "BC_8419"   "BC_58978"  "BC_149935"
-#> [13] "BC_68847"  "BC_90135"  "BC_4173"   "BC_4469"   "BC_172626" "BC_31610" 
-#> [19] "BC_383329" "BC_55960"  "BC_62970"  "BC_102160" "BC_244320" "BC_400391"
-#> [25] "BC_135438" "BC_203989" "BC_133178"
-#> 
-#> $TopBarcodes$`S1-1`
-#>  [1] "BC_79755"  "BC_102160" "BC_59493"  "BC_23361"  "BC_53234"  "BC_400391"
-#>  [7] "BC_142112" "BC_94511"  "BC_68847"  "BC_142024" "BC_23241"  "BC_47310" 
-#> [13] "BC_9141"   "BC_65104" 
-#> 
-#> $TopBarcodes$`S1-2`
-#>  [1] "BC_79755"  "BC_102160" "BC_59493"  "BC_23361"  "BC_53234"  "BC_142112"
-#>  [7] "BC_400391" "BC_94511"  "BC_68847"  "BC_142024" "BC_23241"  "BC_9141"  
-#> [13] "BC_47310"  "BC_5472"   "BC_65104" 
-#> 
-#> $TopBarcodes$`S2-1`
-#>  [1] "BC_248880" "BC_65104"  "BC_9141"   "BC_1478"   "BC_31610"  "BC_79755" 
-#>  [7] "BC_388103" "BC_257382" "BC_68618"  "BC_102160" "BC_4531"   "BC_90135" 
-#> 
-#> $TopBarcodes$`S2-2`
-#>  [1] "BC_248880" "BC_65104"  "BC_9141"   "BC_1478"   "BC_31610"  "BC_79755" 
-#>  [7] "BC_388103" "BC_257382" "BC_68618"  "BC_102160" "BC_4531"  
-#> 
-#> $TopBarcodes$`S3-1`
-#>  [1] "BC_79755"  "BC_142024" "BC_102160" "BC_183691" "BC_59493"  "BC_345889"
-#>  [7] "BC_23361"  "BC_70225"  "BC_30946"  "BC_159570"
-#> 
-#> $TopBarcodes$`S3-2`
-#>  [1] "BC_79755"  "BC_142024" "BC_102160" "BC_183691" "BC_59493"  "BC_345889"
-#>  [7] "BC_23361"  "BC_70225"  "BC_159570" "BC_30946" 
-#> 
-#> $TopBarcodes$`S4-1`
-#>  [1] "BC_53234"  "BC_58978"  "BC_62602"  "BC_68847"  "BC_140320" "BC_79755" 
-#>  [7] "BC_327422" "BC_36103"  "BC_102160" "BC_81579"  "BC_118640" "BC_135438"
-#> [13] "BC_205581" "BC_91412"  "BC_4564"   "BC_1607"   "BC_159570" "BC_468335"
-#> [19] "BC_560839" "BC_229324" "BC_49397"  "BC_203989" "BC_124796" "BC_8419"  
-#> [25] "BC_190202"
-#> 
-#> $TopBarcodes$`S4-2`
-#>  [1] "BC_53234"  "BC_58978"  "BC_62602"  "BC_68847"  "BC_140320" "BC_79755" 
-#>  [7] "BC_327422" "BC_36103"  "BC_81579"  "BC_102160" "BC_118640" "BC_135438"
-#> [13] "BC_1607"   "BC_205581" "BC_4564"   "BC_91412"  "BC_229324" "BC_560839"
-#> [19] "BC_159570" "BC_468335" "BC_49397"  "BC_203989" "BC_124796" "BC_8419"  
-#> [25] "BC_190202" "BC_23241" 
-#> 
-#> $TopBarcodes$`S5-1`
-#>  [1] "BC_561471" "BC_118640" "BC_59493"  "BC_1607"   "BC_31610"  "BC_23361" 
-#>  [7] "BC_159570" "BC_93485"  "BC_468335" "BC_81579"  "BC_257382"
-#> 
-#> $TopBarcodes$`S5-2`
-#>  [1] "BC_561471" "BC_118640" "BC_59493"  "BC_1607"   "BC_31610"  "BC_23361" 
-#>  [7] "BC_159570" "BC_93485"  "BC_81579"  "BC_468335" "BC_257382" "BC_21013" 
-#> 
-#> $TopBarcodes$`S6-1`
-#> [1] "BC_79755"  "BC_8419"   "BC_124796" "BC_4564"   "BC_400391" "BC_31610" 
-#> [7] "BC_1478"   "BC_90135"  "BC_388103"
-#> 
-#> $TopBarcodes$`S6-2`
-#>  [1] "BC_79755"  "BC_8419"   "BC_124796" "BC_4564"   "BC_400391" "BC_31610" 
-#>  [7] "BC_1478"   "BC_90135"  "BC_54442"  "BC_388103"
-#> 
-#> $TopBarcodes$`S7-1`
-#>  [1] "BC_53234"  "BC_54442"  "BC_142024" "BC_140320" "BC_93485"  "BC_124796"
-#>  [7] "BC_23544"  "BC_8419"   "BC_324711" "BC_159570" "BC_58978"  "BC_90135" 
-#> [13] "BC_345889" "BC_4564"   "BC_102160" "BC_400391"
-#> 
-#> $TopBarcodes$`S7-2`
-#>  [1] "BC_53234"  "BC_54442"  "BC_142024" "BC_140320" "BC_93485"  "BC_124796"
-#>  [7] "BC_23544"  "BC_8419"   "BC_324711" "BC_159570" "BC_58978"  "BC_90135" 
-#> [13] "BC_4564"   "BC_345889" "BC_102160" "BC_400391"
-#> 
-#> $TopBarcodes$`S8-1`
-#>  [1] "BC_142024" "BC_90135"  "BC_58978"  "BC_93485"  "BC_363879" "BC_4531"  
-#>  [7] "BC_205581" "BC_118640" "BC_79755"  "BC_31610"  "BC_1607"   "BC_82758" 
-#> [13] "BC_389078" "BC_194823" "BC_251167" "BC_77209"  "BC_68847"  "BC_102160"
-#> [19] "BC_4173"   "BC_4564"  
-#> 
-#> $TopBarcodes$`S8-2`
-#>  [1] "BC_142024" "BC_90135"  "BC_58978"  "BC_93485"  "BC_363879" "BC_4531"  
-#>  [7] "BC_205581" "BC_118640" "BC_79755"  "BC_1607"   "BC_31610"  "BC_82758" 
-#> [13] "BC_194823" "BC_251167" "BC_389078" "BC_68847"  "BC_77209"  "BC_102160"
-#> [19] "BC_4173"   "BC_4564"   "BC_94579" 
-#> 
-#> $TopBarcodes$`S9-1`
-#>  [1] "BC_79755"  "BC_4198"   "BC_81579"  "BC_560839" "BC_118640" "BC_345889"
-#>  [7] "BC_400391" "BC_1607"   "BC_319105" "BC_35704"  "BC_205581" "BC_90135" 
-#> [13] "BC_124796" "BC_8419"   "BC_142024" "BC_561471" "BC_68847"  "BC_19146" 
-#> [19] "BC_78912" 
-#> 
-#> $TopBarcodes$`S9-2`
-#>  [1] "BC_79755"  "BC_4198"   "BC_81579"  "BC_118640" "BC_560839" "BC_345889"
-#>  [7] "BC_400391" "BC_1607"   "BC_319105" "BC_35704"  "BC_205581" "BC_90135" 
-#> [13] "BC_124796" "BC_142024" "BC_8419"   "BC_561471" "BC_19146"  "BC_68847" 
-#> [19] "BC_78912" 
-#> 
-#> 
-#> $TopBarcodeCounts
-#> $TopBarcodeCounts$`T0-1`
-#>             T0-1
-#> BC_245016 235792
-#> BC_121651 229674
-#> BC_420967 140576
-#> BC_108059 107746
-#> BC_323670  93902
-#> BC_9141    75789
-#> BC_331193  61209
-#> BC_119418  51910
-#> BC_65104   45136
-#> BC_17570   44290
-#> BC_35704   41752
-#> BC_383329  40346
-#> BC_92942   39154
-#> BC_24510   38403
-#> BC_1396    38215
-#> BC_83680   36945
-#> BC_55858   31659
-#> BC_38589   31330
-#> BC_137495  30929
-#> BC_69627   29428
-#> BC_446739  27684
-#> BC_171385  24017
-#> BC_12367   23205
-#> BC_280320  23170
-#> BC_15782   22725
-#> BC_310257  21578
-#> BC_221975  21486
-#> BC_313129  21335
-#> BC_67201   18479
-#> BC_3618    17186
-#> BC_107789  17104
-#> BC_36103   16025
-#> BC_2596    15901
-#> BC_69641   14140
-#> BC_215831  13831
-#> BC_16618   13795
-#> BC_249402  13610
-#> BC_141060  13535
-#> BC_13611   13328
-#> BC_22306   13090
-#> BC_588849  13063
-#> BC_43535   12925
-#> BC_16956   12924
-#> BC_175651  12903
-#> BC_294188  12701
-#> BC_46538   12667
-#> BC_344050  12651
-#> BC_25084   12454
-#> BC_56819   12186
-#> BC_206193  11681
-#> BC_428803  11596
-#> BC_219379  11334
-#> BC_400391  11240
-#> BC_19146   10980
-#> BC_205581  10868
-#> BC_148563  10683
-#> BC_114027  10338
-#> BC_172626  10332
-#> BC_403084  10243
-#> BC_62602   10088
-#> BC_224300   9814
-#> BC_47439    9520
-#> BC_18278    9480
-#> BC_43128    9389
-#> BC_68847    9033
-#> BC_111272   9008
-#> BC_58978    8725
-#> BC_162898   8480
-#> BC_476380   8338
-#> BC_262618   8324
-#> BC_276866   8296
-#> BC_252374   8167
-#> BC_550404   8046
-#> BC_325065   7949
-#> BC_202891   7930
-#> BC_50136    7895
-#> BC_255436   7874
-#> BC_103528   7849
-#> BC_425129   7823
-#> BC_168818   7818
-#> BC_204266   7756
-#> BC_176946   7643
-#> BC_521149   7583
-#> BC_105387   7326
-#> BC_227720   7284
-#> BC_83707    7266
-#> BC_64584    7171
-#> BC_21018    7056
-#> BC_458543   7033
-#> BC_11643    7000
-#> BC_175871   6893
-#> BC_556149   6772
-#> BC_34617    6462
-#> BC_166467   6460
-#> BC_41692    6409
-#> BC_72619    6392
-#> BC_168530   6363
-#> BC_289383   6347
-#> BC_2040     6290
-#> BC_516753   6259
-#> BC_24159    6246
-#> BC_249161   6223
-#> BC_345717   6187
-#> BC_54442    6053
-#> BC_236596   6052
-#> BC_248159   6033
-#> BC_206263   5911
-#> BC_376665   5867
-#> BC_18345    5820
-#> BC_114040   5760
-#> BC_612467   5614
-#> BC_56338    5560
-#> BC_363714   5450
-#> BC_39281    5389
-#> BC_30946    5292
-#> BC_32325    5202
-#> BC_418545   5107
-#> BC_103714   5093
-#> BC_140320   5043
-#> BC_120596   4965
-#> BC_503475   4946
-#> BC_160057   4936
-#> BC_16754    4925
-#> BC_43673    4922
-#> BC_173736   4913
-#> BC_46524    4874
-#> BC_142999   4871
-#> BC_11277    4832
-#> BC_67854    4812
-#> BC_258799   4694
-#> BC_13395    4689
-#> BC_37062    4673
-#> BC_38398    4658
-#> BC_105732   4622
-#> BC_139928   4582
-#> BC_355420   4539
-#> BC_40856    4515
-#> BC_128266   4473
-#> BC_321677   4464
-#> BC_203520   4459
-#> BC_29284    4430
-#> BC_172412   4416
-#> BC_85441    4400
-#> BC_20263    4392
-#> BC_118831   4382
-#> BC_10454    4369
-#> BC_5256     4362
-#> BC_129519   4338
-#> BC_11391    4303
-#> BC_172238   4298
-#> BC_43123    4290
-#> BC_42178    4287
-#> BC_102205   4287
-#> BC_346124   4265
-#> BC_389078   4171
-#> BC_42612    4162
-#> BC_93485    4085
-#> BC_415373   4064
-#> BC_3733     4063
-#> BC_32680    4060
-#> BC_474982   4011
-#> BC_7405     3964
-#> BC_289568   3919
-#> BC_229995   3867
-#> BC_79026    3769
-#> BC_45955    3742
-#> BC_146666   3727
-#> BC_183401   3720
-#> BC_356531   3656
-#> BC_187025   3625
-#> BC_42310    3618
-#> BC_221056   3618
-#> BC_132225   3585
-#> BC_235128   3561
-#> BC_15740    3530
-#> BC_160158   3525
-#> BC_320233   3524
-#> BC_8835     3492
-#> BC_173674   3443
-#> BC_53234    3423
-#> BC_91412    3390
-#> BC_135438   3390
-#> BC_373901   3388
-#> BC_44599    3349
-#> BC_87047    3338
-#> BC_350464   3324
-#> BC_324711   3320
-#> BC_40516    3289
-#> BC_113839   3281
-#> BC_162119   3281
-#> BC_33699    3222
-#> BC_635211   3216
-#> BC_346627   3214
-#> BC_7122     3142
-#> BC_806      3135
-#> BC_63846    3135
-#> BC_283335   3111
-#> BC_151544   3104
-#> BC_273124   3101
-#> BC_227308   3075
-#> BC_47726    3060
-#> BC_102803   3056
-#> BC_244320   3036
-#> BC_10347    3032
-#> BC_389742   3009
-#> BC_2865     3004
-#> BC_77030    2974
-#> BC_65130    2970
-#> BC_564039   2950
-#> BC_519985   2943
-#> BC_76947    2937
-#> BC_4564     2934
-#> BC_521485   2933
-#> BC_495859   2921
-#> BC_35659    2918
-#> BC_14377    2908
-#> BC_199881   2905
-#> BC_147006   2887
-#> BC_26769    2885
-#> BC_100344   2869
-#> BC_672202   2856
-#> BC_146146   2848
-#> BC_320752   2845
-#> BC_324953   2834
-#> BC_217211   2830
-#> BC_67965    2821
-#> BC_210529   2810
-#> BC_189813   2805
-#> BC_242383   2801
-#> BC_560839   2798
-#> BC_228994   2788
-#> BC_389087   2775
-#> BC_44600    2762
-#> BC_20557    2757
-#> BC_10808    2724
-#> BC_138900   2720
-#> BC_81309    2702
-#> BC_145719   2690
-#> BC_58144    2674
-#> BC_7299     2667
-#> BC_78349    2666
-#> BC_102160   2658
-#> BC_164378   2656
-#> BC_211828   2653
-#> BC_336794   2649
-#> BC_53380    2641
-#> BC_14492    2636
-#> BC_23241    2613
-#> BC_57621    2609
-#> BC_11270    2605
-#> BC_41523    2589
-#> BC_33764    2583
-#> BC_12191    2564
-#> BC_17608    2564
-#> BC_8324     2549
-#> BC_183112   2531
-#> BC_167638   2509
-#> BC_2206     2504
-#> BC_123791   2482
-#> BC_377226   2467
-#> BC_195246   2447
-#> BC_161563   2433
-#> BC_67233    2430
-#> BC_421415   2428
-#> BC_150291   2424
-#> BC_408928   2410
-#> BC_410172   2406
-#> BC_121663   2393
-#> BC_201293   2368
-#> BC_156353   2338
-#> BC_45652    2320
-#> BC_357846   2320
-#> BC_199093   2308
-#> BC_214878   2300
-#> BC_6093     2287
-#> BC_214604   2266
-#> BC_302238   2250
-#> BC_521264   2244
-#> BC_672021   2223
-#> BC_2294     2218
-#> BC_587139   2212
-#> BC_638624   2206
-#> BC_86358    2199
-#> BC_1764     2174
-#> BC_366637   2171
-#> BC_218093   2168
-#> BC_201372   2155
-#> BC_19353    2139
-#> BC_130431   2134
-#> BC_22999    2112
-#> BC_49044    2112
-#> BC_154519   2111
-#> BC_324042   2110
-#> BC_37363    2103
-#> BC_154990   2098
-#> BC_350335   2098
-#> BC_194823   2051
-#> BC_23522    2047
-#> BC_141246   2046
-#> BC_4531     2036
-#> BC_4733     2025
-#> BC_654016   2009
-#> BC_147380   1995
-#> BC_196282   1994
-#> BC_52081    1980
-#> BC_207651   1977
-#> BC_380559   1974
-#> BC_527469   1961
-#> BC_353112   1959
-#> BC_426662   1958
-#> BC_655329   1949
-#> BC_73098    1947
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-#> BC_49044    1376
-#> BC_4198     1373
-#> BC_164652   1373
-#> BC_125634   1369
-#> BC_322542   1367
-#> BC_203989   1353
-#> BC_67374    1351
-#> BC_45402    1343
-#> BC_14484    1337
-#> BC_195887   1334
-#> BC_9322     1332
-#> BC_415903   1331
-#> BC_82595    1324
-#> BC_329007   1324
-#> BC_146322   1318
-#> BC_261055   1311
-#> BC_90135    1307
-#> BC_26258    1298
-#> BC_363879   1296
-#> BC_515934   1290
-#> BC_11055    1269
-#> BC_49397    1269
-#> BC_381245   1266
-#> BC_309303   1253
-#> BC_12410    1249
-#> BC_249649   1249
-#> BC_318282   1244
-#> BC_57794    1243
-#> BC_67370    1239
-#> BC_316483   1238
-#> BC_31196    1237
-#> BC_357846   1230
-#> BC_9747     1223
-#> BC_83816    1223
-#> BC_26384    1221
-#> BC_52856    1219
-#> BC_302454   1203
-#> BC_50311    1196
-#> BC_282868   1192
-#> BC_61500    1190
-#> BC_238577   1185
-#> BC_47310    1181
-#> BC_31185    1180
-#> BC_283541   1179
-#> BC_79755    1165
-#> BC_231962   1162
-#> BC_56489    1154
-#> BC_120505   1153
-#> BC_94579    1150
-#> BC_444761   1146
-#> BC_70909    1143
-#> BC_296283   1140
-#> BC_15652    1129
-#> BC_276301   1124
-#> BC_322219   1124
-#> BC_169627   1121
-#> BC_84350    1120
-#> BC_596629   1118
-#> BC_21535    1110
-#> BC_52081    1108
-#> BC_7691     1104
-#> BC_302238   1103
-#> BC_165975   1102
-#> BC_14380    1101
-#> BC_144061   1099
-#> BC_129559   1093
-#> BC_491991   1091
-#> BC_43206    1083
-#> BC_35459    1081
-#> BC_228333   1070
-#> BC_176231   1069
-#> BC_75688    1063
-#> BC_516611   1057
-#> BC_149347   1055
-#> BC_220827   1051
-#> BC_207333   1049
-#> BC_159570   1046
-#> BC_288976   1044
-#> BC_81579    1027
-#> BC_44350    1025
-#> BC_145103   1024
-#> BC_35283    1012
-#> BC_250471   1004
-#> BC_97874    1000
-#> BC_567519    989
-#> BC_454109    988
-#> BC_355439    986
-#> BC_430       984
-#> BC_57582     984
-#> BC_48168     983
-#> BC_10190     979
-#> BC_19612     978
-#> BC_92584     976
-#> BC_212837    976
-#> BC_129768    970
-#> BC_345889    956
-#> BC_115038    955
-#> BC_228451    955
-#> BC_451991    955
-#> BC_179919    948
-#> BC_576338    944
-#> BC_655329    940
-#> BC_472019    939
-#> BC_168163    936
-#> BC_35633     935
-#> BC_257382    935
-#> BC_535770    934
-#> BC_329679    932
-#> BC_248582    931
-#> BC_62970     925
-#> BC_324042    919
-#> BC_18218     912
-#> BC_182092    908
-#> BC_142112    907
-#> BC_4173      892
-#> BC_100070    892
-#> BC_259621    883
-#> BC_567514    880
-#> BC_103862    878
-#> BC_28938     874
-#> BC_325871    872
-#> BC_49060     871
-#> BC_275321    871
-#> BC_186267    868
-#> BC_652798    865
-#> BC_1977      863
-#> BC_238160    862
-#> BC_189854    858
-#> BC_37116     855
-#> BC_255928    854
-#> BC_268745    852
-#> BC_23918     848
-#> BC_131019    847
-#> BC_171345    844
-#> BC_278096    840
-#> BC_13        834
-#> BC_82758     833
-#> BC_26097     831
-#> BC_65912     831
-#> BC_41280     826
-#> BC_245600    824
-#> BC_509872    823
-#> BC_15707     821
-#> BC_17509     821
-#> BC_154696    821
-#> BC_194092    820
-#> BC_28077     816
-#> BC_4165      815
-#> BC_154806    813
-#> BC_25609     810
-#> BC_33035     807
-#> BC_1139      802
-#> BC_80020     802
-#> BC_388103    801
-#> BC_59243     795
-#> BC_20281     788
-#> BC_57889     788
-#> BC_143062    788
-#> BC_218837    784
-#> BC_179997    778
-#> BC_90599     776
-#> BC_5615      775
-#> BC_23363     774
-#> BC_352645    772
-#> BC_292621    762
-#> BC_153858    760
-#> BC_230643    759
-#> BC_297704    754
-#> BC_381050    751
-#> BC_203210    746
-#> BC_8838      738
-#> BC_183115    738
-#> BC_257523    737
-#> BC_22263     734
-#> BC_56792     733
-#> BC_163063    729
-#> BC_184732    723
-#> BC_563624    718
-#> BC_77131     713
-#> BC_78760     706
-#> BC_370968    706
-#> BC_307924    704
-#> BC_331889    704
-#> BC_389378    704
-#> BC_29053     702
-#> BC_23213     700
-#> BC_428204    700
-#> BC_500299    697
-#> BC_14452     696
-#> BC_124624    696
-#> BC_22981     694
-#> BC_98171     693
-#> BC_170233    692
-#> BC_38465     691
-#> BC_47704     691
-#> BC_198370    686
-#> BC_14131     683
-#> BC_190202    683
-#> BC_278655    683
-#> BC_6453      682
-#> BC_557872    681
-#> BC_31562     677
-#> BC_128748    676
-#> BC_29984     675
-#> BC_294176    673
-#> BC_127999    668
-#> BC_796       664
-#> 
-#> $TopBarcodeCounts$`S10-1`
-#>             S10-1
-#> BC_8419   1991893
-#> BC_124796 1649985
-#> BC_102160  203430
-#> 
-#> $TopBarcodeCounts$`S10-2`
-#>             S10-2
-#> BC_8419   1995956
-#> BC_124796 1710825
-#> BC_102160  208878
-#> BC_68847    68155
-#> 
-#> $TopBarcodeCounts$`S11-1`
-#>            S11-1
-#> BC_53234  680240
-#> BC_205581 493308
-#> BC_90135  332038
-#> BC_172626 257083
-#> BC_58978  196664
-#> BC_79755  116795
-#> BC_159570 108699
-#> BC_118640  73041
-#> BC_31610   71654
-#> BC_257382  64909
-#> BC_1607    61994
-#> BC_635211  58710
-#> BC_4531    58679
-#> BC_324711  55178
-#> BC_327422  30934
-#> BC_297704  27654
-#> BC_194823  25858
-#> BC_415460  21413
-#> BC_140320  19073
-#> 
-#> $TopBarcodeCounts$`S11-2`
-#>            S11-2
-#> BC_53234  672838
-#> BC_205581 480681
-#> BC_90135  319597
-#> BC_172626 226125
-#> BC_58978  168069
-#> BC_79755  107090
-#> BC_159570 101497
-#> BC_635211  67342
-#> BC_118640  57323
-#> BC_324711  56730
-#> BC_31610   55511
-#> BC_257382  55165
-#> BC_4531    50457
-#> BC_1607    49865
-#> BC_327422  34475
-#> BC_297704  22422
-#> BC_194823  22402
-#> BC_415460  20209
-#> BC_62602   17792
-#> BC_140320  16805
-#> 
-#> $TopBarcodeCounts$`S12-1`
-#>             S12-1
-#> BC_389078 1413816
-#> BC_159570  615742
-#> BC_135438  272292
-#> BC_500780  246148
-#> BC_79755   235092
-#> BC_31610   185291
-#> BC_49629   144164
-#> BC_78349   134743
-#> BC_118640  132924
-#> BC_47726   128882
-#> BC_58978   115558
-#> BC_1607    108011
-#> BC_245016  107080
-#> BC_329679   79183
-#> 
-#> $TopBarcodeCounts$`S12-2`
-#>             S12-2
-#> BC_389078 1397641
-#> BC_159570  557943
-#> BC_135438  248280
-#> BC_500780  235202
-#> BC_79755   219591
-#> BC_31610   171947
-#> BC_49629   131663
-#> BC_118640  120960
-#> BC_47726   117847
-#> BC_78349   109942
-#> BC_245016   99030
-#> BC_58978    97876
-#> BC_1607     95198
-#> BC_329679   74065
-#> 
-#> $TopBarcodeCounts$`S13-1`
-#>             S13-1
-#> BC_8419   1132876
-#> BC_124796 1113617
-#> BC_102160  813971
-#> BC_257382  768188
-#> BC_172626  112559
-#> BC_345889   84734
-#> BC_53234    72801
-#> BC_79755    55138
-#> BC_5472     54808
-#> BC_205581   41262
-#> 
-#> $TopBarcodeCounts$`S13-2`
-#>             S13-2
-#> BC_8419   1075999
-#> BC_124796 1036131
-#> BC_102160  766818
-#> BC_257382  724300
-#> BC_172626  106215
-#> BC_345889   77361
-#> BC_53234    65594
-#> BC_5472     52942
-#> BC_79755    51810
-#> BC_17608    39408
-#> BC_205581   38917
-#> 
-#> $TopBarcodeCounts$`S14-1`
-#>             S14-1
-#> BC_90135  2015353
-#> BC_388103 1491990
-#> BC_31610   325001
-#> BC_58978   186583
-#> BC_292962  127737
-#> BC_159570   82869
-#> 
-#> $TopBarcodeCounts$`S14-2`
-#>             S14-2
-#> BC_90135  1814510
-#> BC_388103 1338206
-#> BC_31610   285251
-#> BC_58978   161284
-#> BC_292962  125041
-#> BC_159570   76636
-#> 
-#> $TopBarcodeCounts$`S15-1`
-#>             S15-1
-#> BC_1478   1137019
-#> BC_94579   930125
-#> BC_124796  490802
-#> BC_8419    480465
-#> BC_316204   95059
-#> BC_400391   61062
-#> BC_23241    42520
-#> 
-#> $TopBarcodeCounts$`S15-2`
-#>             S15-2
-#> BC_1478   1189434
-#> BC_94579   976104
-#> BC_124796  494292
-#> BC_8419    486189
-#> BC_316204   96050
-#> BC_400391   67224
-#> BC_23241    43739
-#> 
-#> $TopBarcodeCounts$`S16-1`
-#>            S16-1
-#> BC_53234  967214
-#> BC_79755  699557
-#> BC_140320 622182
-#> BC_31610  355389
-#> BC_248880 310481
-#> BC_1478   286098
-#> BC_17608  219959
-#> BC_257382 191551
-#> BC_345889 186661
-#> BC_94511  140502
-#> BC_142112 136360
-#> BC_142024 111299
-#> BC_91412   96497
-#> BC_58978   76133
-#> BC_190202  65758
-#> BC_4198    51449
-#> BC_68847   48868
-#> BC_389078  39963
-#> BC_47726   37946
-#> 
-#> $TopBarcodeCounts$`S16-2`
-#>            S16-2
-#> BC_53234  955906
-#> BC_79755  679328
-#> BC_140320 605778
-#> BC_31610  351863
-#> BC_248880 297737
-#> BC_1478   286187
-#> BC_17608  215120
-#> BC_257382 192418
-#> BC_345889 192200
-#> BC_94511  143289
-#> BC_142112 142201
-#> BC_142024 111762
-#> BC_91412   97112
-#> BC_58978   77379
-#> BC_190202  66762
-#> BC_68847   51297
-#> BC_4198    51154
-#> BC_389078  41183
-#> BC_47726   37684
-#> BC_205581  34755
-#> 
-#> $TopBarcodeCounts$`S17-1`
-#>             S17-1
-#> BC_183691 2648948
-#> BC_90135  1584269
-#> BC_400391  588642
-#> BC_31610   216675
-#> BC_68847   146098
-#> 
-#> $TopBarcodeCounts$`S17-2`
-#>             S17-2
-#> BC_183691 2785778
-#> BC_90135  1600044
-#> BC_400391  604178
-#> BC_31610   206900
-#> BC_68847   158068
-#> BC_53234   139785
-#> 
-#> $TopBarcodeCounts$`S18-1`
-#>            S18-1
-#> BC_49397  763504
-#> BC_91412  419115
-#> BC_159570 417080
-#> BC_62602  261522
-#> BC_93485  252666
-#> BC_70225  234477
-#> BC_79755  204363
-#> BC_4564   141545
-#> BC_124796 133198
-#> BC_58978  124347
-#> BC_8419   120698
-#> BC_149935 111889
-#> BC_68847  110884
-#> BC_90135  103890
-#> BC_4173    99281
-#> BC_4469    94003
-#> BC_172626  92447
-#> BC_31610   86277
-#> BC_383329  74354
-#> BC_55960   71818
-#> BC_62970   55812
-#> BC_102160  51865
-#> BC_244320  38874
-#> BC_400391  33062
-#> BC_135438  31484
-#> BC_203989  21276
-#> BC_81579   19900
-#> 
-#> $TopBarcodeCounts$`S18-2`
-#>            S18-2
-#> BC_49397  817999
-#> BC_91412  443237
-#> BC_159570 436654
-#> BC_62602  290698
-#> BC_70225  275474
-#> BC_93485  270096
-#> BC_79755  214481
-#> BC_4564   165210
-#> BC_124796 143216
-#> BC_8419   137545
-#> BC_58978  134487
-#> BC_149935 120452
-#> BC_68847  119995
-#> BC_90135  116149
-#> BC_4173   109548
-#> BC_4469   107465
-#> BC_172626  97888
-#> BC_31610   96129
-#> BC_383329  83812
-#> BC_55960   82691
-#> BC_62970   66186
-#> BC_102160  58036
-#> BC_244320  44322
-#> BC_400391  41088
-#> BC_135438  33668
-#> BC_203989  25743
-#> BC_133178  20613
-#> 
-#> $TopBarcodeCounts$`S1-1`
-#>             S1-1
-#> BC_79755  993343
-#> BC_102160 714207
-#> BC_59493  406903
-#> BC_23361  207760
-#> BC_53234  201367
-#> BC_400391 189577
-#> BC_142112 174047
-#> BC_94511  113449
-#> BC_68847   58203
-#> BC_142024  56214
-#> BC_23241   44498
-#> BC_47310   31114
-#> BC_9141    30245
-#> BC_65104   28467
-#> 
-#> $TopBarcodeCounts$`S1-2`
-#>              S1-2
-#> BC_79755  1222013
-#> BC_102160  808336
-#> BC_59493   463756
-#> BC_23361   256247
-#> BC_53234   244541
-#> BC_142112  200263
-#> BC_400391  195378
-#> BC_94511   143734
-#> BC_68847    67285
-#> BC_142024   66571
-#> BC_23241    54725
-#> BC_9141     39529
-#> BC_47310    38718
-#> BC_5472     35839
-#> BC_65104    33854
-#> 
-#> $TopBarcodeCounts$`S2-1`
-#>              S2-1
-#> BC_248880 2011324
-#> BC_65104  1426536
-#> BC_9141   1346962
-#> BC_1478    850892
-#> BC_31610   820495
-#> BC_79755   462821
-#> BC_388103  216599
-#> BC_257382  200090
-#> BC_68618   105753
-#> BC_102160   76111
-#> BC_4531     54809
-#> BC_90135    45785
-#> 
-#> $TopBarcodeCounts$`S2-2`
-#>              S2-2
-#> BC_248880 1182134
-#> BC_65104   933706
-#> BC_9141    932603
-#> BC_1478    561478
-#> BC_31610   468327
-#> BC_79755   308473
-#> BC_388103  133643
-#> BC_257382  121541
-#> BC_68618    69576
-#> BC_102160   49153
-#> BC_4531     31316
-#> 
-#> $TopBarcodeCounts$`S3-1`
-#>              S3-1
-#> BC_79755  2163553
-#> BC_142024  565889
-#> BC_102160  448714
-#> BC_183691  203408
-#> BC_59493   163823
-#> BC_345889   89546
-#> BC_23361    81236
-#> BC_70225    56521
-#> BC_30946    50953
-#> BC_159570   45613
-#> 
-#> $TopBarcodeCounts$`S3-2`
-#>              S3-2
-#> BC_79755  2486675
-#> BC_142024  696785
-#> BC_102160  551728
-#> BC_183691  257218
-#> BC_59493   196076
-#> BC_345889  107156
-#> BC_23361    93503
-#> BC_70225    66396
-#> BC_159570   61382
-#> BC_30946    59659
-#> 
-#> $TopBarcodeCounts$`S4-1`
-#>             S4-1
-#> BC_53234  692353
-#> BC_58978  342379
-#> BC_62602  280987
-#> BC_68847  237132
-#> BC_140320 223426
-#> BC_79755  208335
-#> BC_327422 190392
-#> BC_36103  181458
-#> BC_102160 151606
-#> BC_81579  139375
-#> BC_118640 132880
-#> BC_135438 119051
-#> BC_205581 102204
-#> BC_91412   92210
-#> BC_4564    91814
-#> BC_1607    90610
-#> BC_159570  66307
-#> BC_468335  66180
-#> BC_560839  64927
-#> BC_229324  62954
-#> BC_49397   49062
-#> BC_203989  43358
-#> BC_124796  38389
-#> BC_8419    31905
-#> BC_190202  26816
-#> 
-#> $TopBarcodeCounts$`S4-2`
-#>             S4-2
-#> BC_53234  507873
-#> BC_58978  223666
-#> BC_62602  181352
-#> BC_68847  145969
-#> BC_140320 144767
-#> BC_79755  139330
-#> BC_327422 138287
-#> BC_36103  118928
-#> BC_81579   96742
-#> BC_102160  93245
-#> BC_118640  89017
-#> BC_135438  70979
-#> BC_1607    66226
-#> BC_205581  62591
-#> BC_4564    61778
-#> BC_91412   58781
-#> BC_229324  40462
-#> BC_560839  38271
-#> BC_159570  36888
-#> BC_468335  35416
-#> BC_49397   34391
-#> BC_203989  33235
-#> BC_124796  24380
-#> BC_8419    21807
-#> BC_190202  17786
-#> BC_23241   16442
-#> 
-#> $TopBarcodeCounts$`S5-1`
-#>              S5-1
-#> BC_561471 2123044
-#> BC_118640  322753
-#> BC_59493   268377
-#> BC_1607    259730
-#> BC_31610   180017
-#> BC_23361   133885
-#> BC_159570   48021
-#> BC_93485    30112
-#> BC_468335   25518
-#> BC_81579    24169
-#> BC_257382   24078
-#> 
-#> $TopBarcodeCounts$`S5-2`
-#>              S5-2
-#> BC_561471 1975938
-#> BC_118640  290505
-#> BC_59493   246168
-#> BC_1607    224559
-#> BC_31610   172466
-#> BC_23361   122852
-#> BC_159570   42615
-#> BC_93485    30438
-#> BC_81579    25799
-#> BC_468335   24958
-#> BC_257382   24460
-#> BC_21013    23108
-#> 
-#> $TopBarcodeCounts$`S6-1`
-#>              S6-1
-#> BC_79755  1282572
-#> BC_8419   1123900
-#> BC_124796  996596
-#> BC_4564    434228
-#> BC_400391   84518
-#> BC_31610    44921
-#> BC_1478     31005
-#> BC_90135    30404
-#> BC_388103   28921
-#> 
-#> $TopBarcodeCounts$`S6-2`
-#>              S6-2
-#> BC_79755  1344442
-#> BC_8419   1117015
-#> BC_124796 1031266
-#> BC_4564    489628
-#> BC_400391  102685
-#> BC_31610    41582
-#> BC_1478     36862
-#> BC_90135    34556
-#> BC_54442    31930
-#> BC_388103   30013
-#> 
-#> $TopBarcodeCounts$`S7-1`
-#>             S7-1
-#> BC_53234  921435
-#> BC_54442  710031
-#> BC_142024 472357
-#> BC_140320 350076
-#> BC_93485  324557
-#> BC_124796 197590
-#> BC_23544  186585
-#> BC_8419   179578
-#> BC_324711 166985
-#> BC_159570 116366
-#> BC_58978  106125
-#> BC_90135   92285
-#> BC_345889  60307
-#> BC_4564    56816
-#> BC_102160  52674
-#> BC_400391  42639
-#> 
-#> $TopBarcodeCounts$`S7-2`
-#>             S7-2
-#> BC_53234  886421
-#> BC_54442  680000
-#> BC_142024 434889
-#> BC_140320 331549
-#> BC_93485  301678
-#> BC_124796 177503
-#> BC_23544  166526
-#> BC_8419   163482
-#> BC_324711 147268
-#> BC_159570 104165
-#> BC_58978   87248
-#> BC_90135   85151
-#> BC_4564    55719
-#> BC_345889  55079
-#> BC_102160  48773
-#> BC_400391  38887
-#> 
-#> $TopBarcodeCounts$`S8-1`
-#>              S8-1
-#> BC_142024 1234236
-#> BC_90135   711120
-#> BC_58978   347497
-#> BC_93485   266947
-#> BC_363879  206203
-#> BC_4531    151017
-#> BC_205581  141328
-#> BC_118640  139500
-#> BC_79755   119797
-#> BC_31610   102078
-#> BC_1607     99159
-#> BC_82758    77715
-#> BC_389078   49526
-#> BC_194823   49489
-#> BC_251167   47114
-#> BC_77209    43860
-#> BC_68847    43534
-#> BC_102160   35811
-#> BC_4173     27438
-#> BC_4564     23411
-#> 
-#> $TopBarcodeCounts$`S8-2`
-#>              S8-2
-#> BC_142024 1098417
-#> BC_90135   613893
-#> BC_58978   269454
-#> BC_93485   215752
-#> BC_363879  166500
-#> BC_4531    125332
-#> BC_205581  115375
-#> BC_118640  111091
-#> BC_79755   105974
-#> BC_1607     80651
-#> BC_31610    79449
-#> BC_82758    63339
-#> BC_194823   47934
-#> BC_251167   43484
-#> BC_389078   41208
-#> BC_68847    40176
-#> BC_77209    35932
-#> BC_102160   27346
-#> BC_4173     24198
-#> BC_4564     22430
-#> BC_94579    18367
-#> 
-#> $TopBarcodeCounts$`S9-1`
-#>             S9-1
-#> BC_79755  684599
-#> BC_4198   412438
-#> BC_81579  222908
-#> BC_560839 190175
-#> BC_118640 190072
-#> BC_345889 174338
-#> BC_400391 173891
-#> BC_1607   124662
-#> BC_319105 106683
-#> BC_35704   97669
-#> BC_205581  96776
-#> BC_90135   55050
-#> BC_124796  54709
-#> BC_8419    41751
-#> BC_142024  41208
-#> BC_561471  36418
-#> BC_68847   26528
-#> BC_19146   26470
-#> BC_78912   18539
-#> 
-#> $TopBarcodeCounts$`S9-2`
-#>             S9-2
-#> BC_79755  678991
-#> BC_4198   420103
-#> BC_81579  232056
-#> BC_118640 198440
-#> BC_560839 187828
-#> BC_345889 177297
-#> BC_400391 169731
-#> BC_1607   132863
-#> BC_319105 107906
-#> BC_35704  100753
-#> BC_205581  89183
-#> BC_90135   56985
-#> BC_124796  51211
-#> BC_142024  47093
-#> BC_8419    40711
-#> BC_561471  35227
-#> BC_19146   32473
-#> BC_68847   29591
-#> BC_78912   20199
-#> 
-#> 
-
    -
    -
    - - -
    -

    Developed by Dane Vassiliadis.

    -
    - -
    -

    Site built with pkgdown 2.0.7.

    -
    - -
    - - - - - - - diff --git a/docs/reference/calcReplicateCorr.html b/docs/reference/calcReplicateCorr.html deleted file mode 100644 index 031bbb4..0000000 --- a/docs/reference/calcReplicateCorr.html +++ /dev/null @@ -1,116 +0,0 @@ - -Calculate correlation between technical replicates — calcReplicateCorr • bartools - Skip to contents - - -
    -
    -
    -

    Calculate correlation between technical replicates

    - -
    calcReplicateCorr.Rd
    -
    - -
    -

    Calculate and return list of correlation between paired technical replicates in a dataset

    -
    - -
    -

    Usage

    - 
    calcReplicateCorr(dge, group, corr.thresh = 0.8, return = "all")
    -
    - -
    -

    Arguments

    -
    dge
    -

    DGEList object containing technical replicates to be compared

    - - -
    group
    -

    grouping variable from metadata containing technical replicate information

    - - -
    corr.thresh
    -

    threshold distinguishing good vs bad correlation between technical replicates

    - - -
    return
    -

    which values to return. One of "good", "bad", "all"

    - -
    -
    -

    Value

    - - -

    Returns a plot of the read counts per barcode (row) in a data frame

    -
    - -
    -

    Examples

    - 
    data(test.dge)
-calcReplicateCorr(test.dge, group = "group")
-#> 10_High_dose   11_Vehicle   12_Vehicle  13_Low_dose  14_Low_dose  15_Low_dose 
-#>    0.9998575    0.9988328    0.9992805    0.9999569    0.9999779    0.9998939 
-#>  16_Low_dose 17_High_dose   18_Vehicle  1_High_dose  2_High_dose  3_High_dose 
-#>    0.9998965    0.9998268    0.9995628    0.9990491    0.9975029    0.9997352 
-#>    4_Vehicle   5_Low_dose  6_High_dose    7_Vehicle    8_Vehicle   9_Low_dose 
-#>    0.9975620    0.9999389    0.9994985    0.9997088    0.9992879    0.9997057 
-#>           T0 
-#>    0.9983838 
-
    -
    -
    - - -
    -

    Developed by Dane Vassiliadis.

    -
    - -
    -

    Site built with pkgdown 2.0.7.

    -
    - -
    - - - - - - - diff --git a/docs/reference/cluster_cormat.html b/docs/reference/cluster_cormat.html deleted file mode 100644 index d57f3ca..0000000 --- a/docs/reference/cluster_cormat.html +++ /dev/null @@ -1,90 +0,0 @@ - -Cluster correlation matrix — cluster_cormat • bartools - Skip to contents - - -
    -
    -
    -

    Cluster correlation matrix

    - -
    cluster_cormat.Rd
    -
    - -
    -

    cluster a correlation matrix using hierarchical clustering

    -
    - -
    -

    Usage

    - 
    cluster_cormat(cormat)
    -
    - -
    -

    Arguments

    -
    cormat
    -

    matrix of correlation values

    - -
    -
    -

    Value

    - - -

    Returns a matrix of correlation values with columns and rows hierarchically clustered

    -
    - -
    - - -
    -

    Developed by Dane Vassiliadis.

    -
    - -
    -

    Site built with pkgdown 2.0.7.

    -
    - -
    - - - - - - - diff --git a/docs/reference/collapseReplicates.html b/docs/reference/collapseReplicates.html deleted file mode 100644 index 610b737..0000000 --- a/docs/reference/collapseReplicates.html +++ /dev/null @@ -1,231 +0,0 @@ - -Collapse technical replicates — collapseReplicates • bartools - Skip to contents - - -
    -
    -
    -

    Collapse technical replicates

    - -
    collapseReplicates.Rd
    -
    - -
    -

    Collapse technical replicates in a DGEList object by mean or sum. -Modified from collapseReplicates function from DESeq2 package to accept DGEList objects and to allow collapsing replicated by mean or sum.

    -
    - -
    -

    Usage

    - 
    collapseReplicates(
-  object,
-  groupby,
-  run,
-  renameCols = TRUE,
-  show_reps = TRUE,
-  by = "mean"
-)
    -
    - -
    -

    Arguments

    -
    object
    -

    DGEList object containing raw or normalised barcode counts with replicate grouping info in object metadata

    - - -
    groupby
    -

    a character vector of containing grouping information. Must be as long as the columns of object. Can pass a metadata column from object.

    - - -
    run
    -

    optional, the names of each unique column in object. if provided, a new column runsCollapsed will be added to the colData which pastes together the names of run

    - - -
    renameCols
    -

    boolean. whether to rename the columns of the returned object using the levels of the grouping factor

    - - -
    show_reps
    -

    boolean. print replicate column names to console?

    - - -
    by
    -

    collapse replicates by mean or sum

    - -
    -
    -

    Value

    - - -

    Returns a dataframe of normalised counts for a sample

    -
    - -
    -

    Examples

    - 
    data(test.dge)
-collapseReplicates(test.dge, groupby = test.dge$samples$group, by = "mean")
-#> $`10_High_dose`
-#> [1] 3 4
-#> 
-#> $`11_Vehicle`
-#> [1] 5 6
-#> 
-#> $`12_Vehicle`
-#> [1] 7 8
-#> 
-#> $`13_Low_dose`
-#> [1]  9 10
-#> 
-#> $`14_Low_dose`
-#> [1] 11 12
-#> 
-#> $`15_Low_dose`
-#> [1] 13 14
-#> 
-#> $`16_Low_dose`
-#> [1] 15 16
-#> 
-#> $`17_High_dose`
-#> [1] 17 18
-#> 
-#> $`18_Vehicle`
-#> [1] 19 20
-#> 
-#> $`1_High_dose`
-#> [1] 21 22
-#> 
-#> $`2_High_dose`
-#> [1] 23 24
-#> 
-#> $`3_High_dose`
-#> [1] 25 26
-#> 
-#> $`4_Vehicle`
-#> [1] 27 28
-#> 
-#> $`5_Low_dose`
-#> [1] 29 30
-#> 
-#> $`6_High_dose`
-#> [1] 31 32
-#> 
-#> $`7_Vehicle`
-#> [1] 33 34
-#> 
-#> $`8_Vehicle`
-#> [1] 35 36
-#> 
-#> $`9_Low_dose`
-#> [1] 37 38
-#> 
-#> $T0
-#> [1] 1 2
-#> 
-#> An object of class "DGEList"
-#> $samples
-#>              Sample Experiment        Group PCR_Replicate Treatment
-#> 10_High_dose  S10-1    test_01 10_High_dose             1 High_dose
-#> 11_Vehicle    S11-1    test_01   11_Vehicle             1   Vehicle
-#> 12_Vehicle    S12-1    test_01   12_Vehicle             1   Vehicle
-#> 13_Low_dose   S13-1    test_01  13_Low_dose             1  Low_dose
-#> 14_Low_dose   S14-1    test_01  14_Low_dose             1  Low_dose
-#>                     group lib.size norm.factors
-#> 10_High_dose 10_High_dose  4114186            1
-#> 11_Vehicle     11_Vehicle  2907500            1
-#> 12_Vehicle     12_Vehicle  4202337            1
-#> 13_Low_dose   13_Low_dose  4513559            1
-#> 14_Low_dose   14_Low_dose  4503490            1
-#> 14 more rows ...
-#> 
-#> $counts
-#>         Samples
-#> Tags     10_High_dose 11_Vehicle 12_Vehicle 13_Low_dose 14_Low_dose 15_Low_dose
-#>   BC_1              0          0          0           0           0           0
-#>   BC_13             0          0          0           0           0           0
-#>   BC_99             0          0          0           0           0           0
-#>   BC_120            0          0          0           0           0           0
-#>   BC_351            0          0          0           0           0           0
-#>         Samples
-#> Tags     16_Low_dose 17_High_dose 18_Vehicle 1_High_dose 2_High_dose
-#>   BC_1             0            0          0           0           0
-#>   BC_13            0            0          0           0           0
-#>   BC_99            0            0          0         155           0
-#>   BC_120           0            0          0           0           0
-#>   BC_351           0            0          0           0           0
-#>         Samples
-#> Tags     3_High_dose 4_Vehicle 5_Low_dose 6_High_dose 7_Vehicle 8_Vehicle
-#>   BC_1             0         0          0           0         0         0
-#>   BC_13            0         0          0           0         0         0
-#>   BC_99            0         0          0           0         0         0
-#>   BC_120           0         0          0           0         0         0
-#>   BC_351           0         0          0           0         0         0
-#>         Samples
-#> Tags     9_Low_dose   T0
-#>   BC_1            0  127
-#>   BC_13           0 1146
-#>   BC_99           0 1354
-#>   BC_120          0  234
-#>   BC_351          0    0
-#> 1634 more rows ...
-#> 
-
-
    -
    -
    - - -
    -

    Developed by Dane Vassiliadis.

    -
    - -
    -

    Site built with pkgdown 2.0.7.

    -
    - -
    - - - - - - - diff --git a/docs/reference/compareAbundance.html b/docs/reference/compareAbundance.html deleted file mode 100644 index 8e68370..0000000 --- a/docs/reference/compareAbundance.html +++ /dev/null @@ -1,116 +0,0 @@ - -Barcode abundance comparison — compareAbundance • bartools - Skip to contents - - -
    -
    -
    -

    Barcode abundance comparison

    - -
    compareAbundance.Rd
    -
    - -
    -

    Takes a dataframe of barcode counts, -computes the mean abundance of each barcode for two specific conditions, -then do a line plot for both conditions

    -
    - -
    -

    Usage

    - 
    compareAbundance(counts, condition, condition_names)
    -
    - -
    -

    Arguments

    -
    counts
    -

    dataframe containing raw counts of barcodes

    - - -
    condition
    -

    sample condition of interest

    - - -
    condition_names
    -

    names of possible conditions in experiment

    - -
    -
    -

    Value

    - - -

    Returns a text file

    -
    -
    -

    Details

    -

    compareAbundance

    -

    Compare barcode abundance between 2 conditions using EdgeR

    -
    - -
    -

    Examples

    - 
    data(test.dge)
-compareAbundance(test.dge, test.dge$samples$group, c("T0","10_High_dose"))
-#> Error in .subset2(x, i, exact = exact): no such index at level 1
-
    -
    -
    - - -
    -

    Developed by Dane Vassiliadis.

    -
    - -
    -

    Site built with pkgdown 2.0.7.

    -
    - -
    - - - - - - - diff --git a/docs/reference/countBarcodeReads.html b/docs/reference/countBarcodeReads.html deleted file mode 100644 index b100a77..0000000 --- a/docs/reference/countBarcodeReads.html +++ /dev/null @@ -1,102 +0,0 @@ - -Count Barcode Reads — countBarcodeReads • bartools - Skip to contents - - -
    -
    -
    -

    Count Barcode Reads

    - -
    countBarcodeReads.Rd
    -
    - -
    -

    Count number of occurences of each barcode in the reference library

    -
    - -
    -

    Usage

    - 
    countBarcodeReads(bam, bam_index, outdir = ".", prefix = "map_barcode_reads")
    -
    - -
    -

    Arguments

    -
    bam
    -

    BAM file from mapBarcodeReads containing mapped barcode reads

    - - -
    bam_index
    -

    Path to bowtie index of barcode reference library. Include index prefix.

    - - -
    outdir
    -

    Optional. Save output to directory

    - - -
    prefix
    -

    Optional. Prefix to add to output file

    - -
    -
    -

    Value

    - - -

    Returns a data-frame containing the Barcode ID, and the count per sample.

    -
    - -
    - - -
    -

    Developed by Dane Vassiliadis.

    -
    - -
    -

    Site built with pkgdown 2.0.7.

    -
    - -
    - - - - - - - diff --git a/docs/reference/createBarcodeObj.html b/docs/reference/createBarcodeObj.html deleted file mode 100644 index 3872d5b..0000000 --- a/docs/reference/createBarcodeObj.html +++ /dev/null @@ -1,94 +0,0 @@ - -createBarcodeObj — createBarcodeObj • bartools - Skip to contents - - -
    -
    -
    -

    createBarcodeObj

    - -
    createBarcodeObj.Rd
    -
    - -
    -

    generate a DGEList object containing barcode counts and sample metadata from raw counts files

    -
    - -
    -

    Usage

    - 
    createBarcodeObj(counts.dir = "counts/", sampleanno = NULL)
    -
    - -
    -

    Arguments

    -
    counts.dir
    -

    directory containing counts files per sample

    - - -
    sampleanno
    -

    annotation file detailing sample ID and groups

    - -
    -
    -

    Value

    - - -

    A data frame containing barcode counts in rows per sample in columns.

    -
    - -
    - - -
    -

    Developed by Dane Vassiliadis.

    -
    - -
    -

    Site built with pkgdown 2.0.7.

    -
    - -
    - - - - - - - diff --git a/docs/reference/extractBAM.html b/docs/reference/extractBAM.html deleted file mode 100644 index 5ca2a61..0000000 --- a/docs/reference/extractBAM.html +++ /dev/null @@ -1,115 +0,0 @@ - -extractBAM — extractBAM • bartools - Skip to contents - - -
    -
    -
    -

    extractBAM

    - -
    extractBAM.Rd
    -
    - -
    -

    Barcode extraction function for BAM alignment files

    -
    - -
    -

    Usage

    - 
    extractBAM(
-  bam,
-  barcode_pattern,
-  constant = "",
-  yieldSize = 1e+06,
-  outfile = NULL
-)
    -
    - -
    -

    Arguments

    -
    bam
    -

    BAM file containing barcoded reads

    - - -
    barcode_pattern
    -

    Pattern to search for in the reads

    - - -
    constant
    -

    The constant region flanking the barcode, two constant regions can be input as a -character vector. This should be set if the constant region is part of the barcode_pattern. -Up to two constant regions can be set e.g. c("ACTG","TACG")

    - - -
    yieldSize
    -

    How many lines of input file to stream at each pass

    - - -
    outfile
    -

    Optional. path to save parsed reads as output file

    - -
    -
    -

    Value

    - - -

    Returns a data-frame containing the read ID, the 10X cell ID, 10X umi, -barcode DNA sequence and barcode ID.

    -
    - -
    - - -
    -

    Developed by Dane Vassiliadis.

    -
    - -
    -

    Site built with pkgdown 2.0.7.

    -
    - -
    - - - - - - - diff --git a/docs/reference/extractBarcodeReads.html b/docs/reference/extractBarcodeReads.html deleted file mode 100644 index 72dde6a..0000000 --- a/docs/reference/extractBarcodeReads.html +++ /dev/null @@ -1,140 +0,0 @@ - -extractBarcodeReads — extractBarcodeReads • bartools - Skip to contents - - -
    -
    -
    -

    extractBarcodeReads

    - -
    extractBarcodeReads.Rd
    -
    - -
    -

    Identify and extract reads containing DNA barcodes from a FASTQ or BAM alignment file

    -
    - -
    -

    Usage

    - 
    extractBarcodeReads(
-  infile,
-  barcode_pattern = "[ACTG][ACTG][GC][AT][GC][ACTG][ACTG][AT][GC][AT]",
-  constant = NULL,
-  yieldSize = 1e+06,
-  outfile = NULL,
-  clean = TRUE,
-  verbose = FALSE,
-  overwrite = TRUE
-)
    -
    - -
    -

    Arguments

    -
    infile
    -

    Input fastq or bam file containing barcoded reads

    - - -
    barcode_pattern
    -

    Pattern to search for in the reads

    - - -
    constant
    -

    The constant region flanking the barcode, two constant regions can be input as a -character vector. This should be set if the constant region is part of the barcode_pattern. -Up to two constant regions can be set e.g. c("ACTG","TACG")

    - - -
    yieldSize
    -

    How many lines of input file to stream at each pass

    - - -
    outfile
    -

    Optional. path to save parsed reads as output file

    - - -
    clean
    -

    Logical. remove previous output files generated by extractBarcodeReads before current run

    - - -
    verbose
    -

    Logical. print detailed output to console

    - - -
    overwrite
    -

    Logical. Overwrite previous output files generated by extractBarcodeReads

    - -
    -
    -

    Value

    - - -

    Returns a data-frame containing the read ID, the 10X cell ID, 10X umi, -barcode DNA sequence and barcode ID.

    -
    - -
    -

    Examples

    - 
    test.fq <- system.file("extdata", "test_extract.fastq.gz", package = "bartools")
-extractBarcodeReads(infile = test.fq, barcode_pattern = "ACTG")
-#> Error in extractBarcodeReads(infile = test.fq, barcode_pattern = "ACTG"): could not find function "extractBarcodeReads"
-test.bam <- system.file("extdata", "test_extract_anno.bam", package = "bartools")
-extractBarcodeReads(infile = test.bam, barcode_pattern = "ACTG")
-#> Error in extractBarcodeReads(infile = test.bam, barcode_pattern = "ACTG"): could not find function "extractBarcodeReads"
-
    -
    -
    - - -
    -

    Developed by Dane Vassiliadis.

    -
    - -
    -

    Site built with pkgdown 2.0.7.

    -
    - -
    - - - - - - - diff --git a/docs/reference/extractFASTQ.html b/docs/reference/extractFASTQ.html deleted file mode 100644 index 93ce28e..0000000 --- a/docs/reference/extractFASTQ.html +++ /dev/null @@ -1,125 +0,0 @@ - -extractFASTQ — extractFASTQ • bartools - Skip to contents - - -
    -
    -
    -

    extractFASTQ

    - -
    extractFASTQ.Rd
    -
    - -
    -

    Barcode extraction function for raw FASTQ files

    -
    - -
    -

    Usage

    - 
    extractFASTQ(
-  fastq,
-  barcode_pattern,
-  constant = NULL,
-  outfile = NULL,
-  block_size = 1e+06,
-  verbose = FALSE,
-  overwrite = FALSE
-)
    -
    - -
    -

    Arguments

    -
    fastq
    -

    REQUIRED. Path to input fastq file

    - - -
    barcode_pattern
    -

    REQUIRED. Pattern to search for in the reads.

    - - -
    constant
    -

    The constant region(s) flanking the barcode. This should be set if the -constant region is part of the barcode_pattern. -Up to two constant regions can be set e.g. constant = c("ACTG","TACG")

    - - -
    outfile
    -

    OPTIONAL. path to save filtered reads as output fasta file with read name as sequence ID

    - - -
    block_size
    -

    Number of reads to load into memory per chunk. Default = 5e5

    - - -
    verbose
    -

    Boolean. Show verbose output from ShortRead? Default = FALSE.

    - - -
    overwrite
    -

    Boolean. Overwrite output files? Default = FALSE.

    - -
    -
    -

    Value

    - - -

    Returns a data-frame containing the read ID and the extracted DNA -barcode sequence

    -
    - -
    - - -
    -

    Developed by Dane Vassiliadis.

    -
    - -
    -

    Site built with pkgdown 2.0.7.

    -
    - -
    - - - - - - - diff --git a/docs/reference/figures/bartools_logo.png b/docs/reference/figures/bartools_logo.png deleted file mode 100644 index 7fcfddb..0000000 Binary files a/docs/reference/figures/bartools_logo.png and /dev/null differ diff --git a/docs/reference/figures/favicon.png b/docs/reference/figures/favicon.png deleted file mode 100644 index 3781f49..0000000 Binary files a/docs/reference/figures/favicon.png and /dev/null differ diff --git a/docs/reference/getDominantBarcodes.html b/docs/reference/getDominantBarcodes.html deleted file mode 100644 index 72a50d0..0000000 --- a/docs/reference/getDominantBarcodes.html +++ /dev/null @@ -1,229 +0,0 @@ - -Get dominant barcodes per sample — getDominantBarcodes • bartools - Skip to contents - - -
    -
    -
    -

    Get dominant barcodes per sample

    - -
    getDominantBarcodes.Rd
    -
    - -
    -

    Takes a DGEList or dataframe of barcode counts, -computes the percentage abundance of each barcode within each sample -then returns a list of barcodes meeting a threshold abundance per sample

    -
    - -
    -

    Usage

    - 
    getDominantBarcodes(counts.obj, pct.thresh = 5)
    -
    - -
    -

    Arguments

    -
    counts.obj
    -

    DGEList object or dataframe containing raw / normalised counts of barcodes

    - - -
    pct.thresh
    -

    percentage abundance threshold to call dominant barcodes

    - -
    -
    -

    Value

    - - -

    Returns a named list containing vectors of dominant barcodes per sample

    -
    -
    -

    Details

    -

    getDominantBarcodes

    -

    get names of barcodes over a given percentage frequency per sample

    -
    - -
    -

    Examples

    - 
    data(test.dge)
-getDominantBarcodes(test.dge, pct.thresh = 5)
-#> $`T0-1`
-#> [1] "BC_245016" "BC_121651"
-#> 
-#> $`T0-2`
-#> [1] "BC_245016" "BC_121651"
-#> 
-#> $`S10-1`
-#> [1] "BC_8419"   "BC_124796"
-#> 
-#> $`S10-2`
-#> [1] "BC_8419"   "BC_124796"
-#> 
-#> $`S11-1`
-#> [1] "BC_53234"  "BC_205581" "BC_90135"  "BC_172626" "BC_58978" 
-#> 
-#> $`S11-2`
-#> [1] "BC_53234"  "BC_205581" "BC_90135"  "BC_172626" "BC_58978" 
-#> 
-#> $`S12-1`
-#> [1] "BC_389078" "BC_159570" "BC_135438" "BC_500780" "BC_79755" 
-#> 
-#> $`S12-2`
-#> [1] "BC_389078" "BC_159570" "BC_135438" "BC_500780" "BC_79755" 
-#> 
-#> $`S13-1`
-#> [1] "BC_8419"   "BC_124796" "BC_102160" "BC_257382"
-#> 
-#> $`S13-2`
-#> [1] "BC_8419"   "BC_124796" "BC_102160" "BC_257382"
-#> 
-#> $`S14-1`
-#> [1] "BC_90135"  "BC_388103" "BC_31610" 
-#> 
-#> $`S14-2`
-#> [1] "BC_90135"  "BC_388103" "BC_31610" 
-#> 
-#> $`S15-1`
-#> [1] "BC_1478"   "BC_94579"  "BC_124796" "BC_8419"  
-#> 
-#> $`S15-2`
-#> [1] "BC_1478"   "BC_94579"  "BC_124796" "BC_8419"  
-#> 
-#> $`S16-1`
-#> [1] "BC_53234"  "BC_79755"  "BC_140320" "BC_31610"  "BC_248880" "BC_1478"  
-#> 
-#> $`S16-2`
-#> [1] "BC_53234"  "BC_79755"  "BC_140320" "BC_31610"  "BC_248880" "BC_1478"  
-#> 
-#> $`S17-1`
-#> [1] "BC_183691" "BC_90135"  "BC_400391"
-#> 
-#> $`S17-2`
-#> [1] "BC_183691" "BC_90135"  "BC_400391"
-#> 
-#> $`S18-1`
-#> [1] "BC_49397"  "BC_91412"  "BC_159570" "BC_62602"  "BC_93485"  "BC_70225" 
-#> 
-#> $`S18-2`
-#> [1] "BC_49397"  "BC_91412"  "BC_159570" "BC_62602"  "BC_70225"  "BC_93485" 
-#> 
-#> $`S1-1`
-#> [1] "BC_79755"  "BC_102160" "BC_59493"  "BC_23361"  "BC_53234"  "BC_400391"
-#> [7] "BC_142112"
-#> 
-#> $`S1-2`
-#> [1] "BC_79755"  "BC_102160" "BC_59493"  "BC_23361"  "BC_53234" 
-#> 
-#> $`S2-1`
-#> [1] "BC_248880" "BC_65104"  "BC_9141"   "BC_1478"   "BC_31610"  "BC_79755" 
-#> 
-#> $`S2-2`
-#> [1] "BC_248880" "BC_65104"  "BC_9141"   "BC_1478"   "BC_31610"  "BC_79755" 
-#> 
-#> $`S3-1`
-#> [1] "BC_79755"  "BC_142024" "BC_102160"
-#> 
-#> $`S3-2`
-#> [1] "BC_79755"  "BC_142024" "BC_102160" "BC_183691"
-#> 
-#> $`S4-1`
-#> [1] "BC_53234"  "BC_58978"  "BC_62602"  "BC_68847"  "BC_140320" "BC_79755" 
-#> 
-#> $`S4-2`
-#> [1] "BC_53234"  "BC_58978"  "BC_62602"  "BC_68847"  "BC_140320" "BC_79755" 
-#> [7] "BC_327422"
-#> 
-#> $`S5-1`
-#> [1] "BC_561471" "BC_118640" "BC_59493"  "BC_1607"  
-#> 
-#> $`S5-2`
-#> [1] "BC_561471" "BC_118640" "BC_59493"  "BC_1607"   "BC_31610" 
-#> 
-#> $`S6-1`
-#> [1] "BC_79755"  "BC_8419"   "BC_124796" "BC_4564"  
-#> 
-#> $`S6-2`
-#> [1] "BC_79755"  "BC_8419"   "BC_124796" "BC_4564"  
-#> 
-#> $`S7-1`
-#> [1] "BC_53234"  "BC_54442"  "BC_142024" "BC_140320" "BC_93485" 
-#> 
-#> $`S7-2`
-#> [1] "BC_53234"  "BC_54442"  "BC_142024" "BC_140320" "BC_93485" 
-#> 
-#> $`S8-1`
-#> [1] "BC_142024" "BC_90135"  "BC_58978"  "BC_93485" 
-#> 
-#> $`S8-2`
-#> [1] "BC_142024" "BC_90135"  "BC_58978"  "BC_93485" 
-#> 
-#> $`S9-1`
-#> [1] "BC_79755"  "BC_4198"   "BC_81579"  "BC_560839" "BC_118640" "BC_345889"
-#> [7] "BC_400391"
-#> 
-#> $`S9-2`
-#> [1] "BC_79755"  "BC_4198"   "BC_81579"  "BC_118640" "BC_560839" "BC_345889"
-#> [7] "BC_400391"
-#> 
-
    -
    -
    - - -
    -

    Developed by Dane Vassiliadis.

    -
    - -
    -

    Site built with pkgdown 2.0.7.

    -
    - -
    - - - - - - - diff --git a/docs/reference/get_upper_tri.html b/docs/reference/get_upper_tri.html deleted file mode 100644 index 5ac332f..0000000 --- a/docs/reference/get_upper_tri.html +++ /dev/null @@ -1,90 +0,0 @@ - -Get upper triangle — get_upper_tri • bartools - Skip to contents - - -
    -
    -
    -

    Get upper triangle

    - -
    get_upper_tri.Rd
    -
    - -
    -

    change lower triangle values in a mirrored matrix to NA

    -
    - -
    -

    Usage

    - 
    get_upper_tri(cormat)
    -
    - -
    -

    Arguments

    -
    cormat
    -

    matrix of correlation values

    - -
    -
    -

    Value

    - - -

    Returns a matrix of correlation values with lower triangle values changed to NA

    -
    - -
    - - -
    -

    Developed by Dane Vassiliadis.

    -
    - -
    -

    Site built with pkgdown 2.0.7.

    -
    - -
    - - - - - - - diff --git a/docs/reference/index.html b/docs/reference/index.html deleted file mode 100644 index 28156c9..0000000 --- a/docs/reference/index.html +++ /dev/null @@ -1,272 +0,0 @@ - -Function reference • bartools - Skip to contents - - -
    -
    -
    -

    Function reference

    -
    - -
    -

    All functions

    - - - - -
    - - - - -
    - - annotateBarcodeReads() -
    -
    Annotate Barcode Reads
    -
    - - calcDivIndexes() -
    -
    Calculate diversity indices for barcode samples
    -
    - - calcPercentileBarcodes() -
    -
    calcPercentileBarcodes
    -
    - - calcReplicateCorr() -
    -
    Calculate correlation between technical replicates
    -
    - - cluster_cormat() -
    -
    Cluster correlation matrix
    -
    - - collapseReplicates() -
    -
    Collapse technical replicates
    -
    - - compareAbundance() -
    -
    Barcode abundance comparison
    -
    - - countBarcodeReads() -
    -
    Count Barcode Reads
    -
    - - createBarcodeObj() -
    -
    createBarcodeObj
    -
    - - extractBAM() -
    -
    extractBAM
    -
    - - extractBarcodeReads() -
    -
    extractBarcodeReads
    -
    - - extractFASTQ() -
    -
    extractFASTQ
    -
    - - getDominantBarcodes() -
    -
    Get dominant barcodes per sample
    -
    - - get_upper_tri() -
    -
    Get upper triangle
    -
    - - mapBarcodeReads() -
    -
    Map Barcode Reads
    -
    - - normaliseCounts() -
    -
    Normalise barcode counts
    -
    - - plotAbundanceLines() -
    -
    Lineplot of barcode abundances in two conditions
    -
    - - plotBARtabFilterQC() -
    -
    plotBARtabFilterQC
    -
    - - plotBARtabMapQC() -
    -
    plotBARtabMapQC
    -
    - - plotBarcodeBoxplot() -
    -
    Boxplot of selected barcodes
    -
    - - plotBarcodeBubble() -
    -
    plotBarcodeBubble
    -
    - - plotBarcodeCorrelation() -
    -
    Sample to sample correlation
    -
    - - plotBarcodeCounts() -
    -
    Plot total counts per barcode in library
    -
    - - plotBarcodeCumSum() -
    -
    Barcode cumulative sum plot of a list of samples against and ordered by sample1
    -
    - - plotBarcodeDistance() -
    -
    Sample to sample distances
    -
    - - plotBarcodeHeatmap() -
    -
    Plot barcode heatmap
    -
    - - plotBarcodeHistogram() -
    -
    plotBarcodeHistogram
    -
    - - plotBarcodePCA() -
    -
    Barcode PCA plot
    -
    - - plotBarcodeRegression() -
    -
    plotBarcodeRegression
    -
    - - plotBarcodeTimeseries() -
    -
    plotBarcodeTimeseries
    -
    - - plotDetectedBarcodes() -
    -
    plotDetectedBarcodes
    -
    - - plotLibraryCumSum() -
    -
    Cumulative sum plot of barcode abundances in a reference library
    -
    - - plotLibraryDiversity() -
    -
    Lineplot of barcode abundances in two conditions
    -
    - - plotOrderedBubble() -
    -
    plotOrderedBubble
    -
    - - plotReadCounts() -
    -
    Plot sample read counts
    -
    - - processBarcodeLibrary() -
    -
    processBarcodeLibrary
    -
    - - test.counts -
    -
    Test barcode counts
    -
    - - test.dge -
    -
    Test barcode DGEList object
    -
    - - thresholdCounts() -
    -
    Threshold counts
    -
    -
    - - -
    -

    Developed by Dane Vassiliadis.

    -
    - -
    -

    Site built with pkgdown 2.0.7.

    -
    - -
    - - - - - - - diff --git a/docs/reference/mapBarcodeReads.html b/docs/reference/mapBarcodeReads.html deleted file mode 100644 index 958ef45..0000000 --- a/docs/reference/mapBarcodeReads.html +++ /dev/null @@ -1,138 +0,0 @@ - -Map Barcode Reads — mapBarcodeReads • bartools - Skip to contents - - -
    -
    -
    -

    Map Barcode Reads

    - -
    mapBarcodeReads.Rd
    -
    - -
    -

    Map reads containing a lintraceR DNA barcode to a barcode reference library/fasta and return CellID:lintracer barcode ID annotations

    -
    - -
    -

    Usage

    - 
    mapBarcodeReads(
-  reads,
-  bowtie_index = NULL,
-  mismatches = 2,
-  threads = 4,
-  outdir = getwd(),
-  prefix = "map_barcode_reads",
-  cleanup = TRUE,
-  reference_fasta = NULL
-)
    -
    - -
    -

    Arguments

    -
    reads
    -

    Dataframe from extractBarcodeReads containing barcode reads

    - - -
    bowtie_index
    -

    Path to bowtie index of barcode reference library. Include index prefix.

    - - -
    mismatches
    -

    Number of mismatches allowed in bowtie for read mapping

    - - -
    threads
    -

    Number of CPU threads for bowtie to use

    - - -
    outdir
    -

    Optional. save output to directory

    - - -
    prefix
    -

    Optional. prefix to add to output file

    - - -
    cleanup
    -

    Logical. Remove intermediate files generated by mapBarcodeReads

    - - -
    reference_fasta
    -

    Optional. Fasta file of reference barcode library for alignment. Use instead of bowtie_index.

    - -
    -
    -

    Value

    - - -

    Returns a data-frame containing the read ID, the 10X cell ID, 10X umi, lintraceR DNA -barcode sequence mapped to the lintraceR DNA barcode ID.

    -
    - -
    -

    Examples

    - 
    load(system.file("extdata", "test_map.rda", package = "bartools"))
-#> Warning: cannot open compressed file '', probable reason 'No such file or directory'
-#> Error in readChar(con, 5L, useBytes = TRUE): cannot open the connection
-test.ref <- system.file("extdata", "barcode_lib_reference_test.fasta", package = "bartools")
-mapBarcodeReads(reads = test.map, reference_fasta = test.ref, outdir = tempdir(), prefix = "map")
-#> Error in mapBarcodeReads(reads = test.map, reference_fasta = test.ref,     outdir = tempdir(), prefix = "map"): could not find function "mapBarcodeReads"
-
    -
    -
    - - -
    -

    Developed by Dane Vassiliadis.

    -
    - -
    -

    Site built with pkgdown 2.0.7.

    -
    - -
    - - - - - - - diff --git a/docs/reference/normaliseCounts.html b/docs/reference/normaliseCounts.html deleted file mode 100644 index 2340908..0000000 --- a/docs/reference/normaliseCounts.html +++ /dev/null @@ -1,106 +0,0 @@ - -Normalise barcode counts — normaliseCounts • bartools - Skip to contents - - -
    -
    -
    -

    Normalise barcode counts

    - -
    normaliseCounts.Rd
    -
    - -
    -

    Normalise barcode counts within a DGEList object

    -
    - -
    -

    Usage

    - 
    normaliseCounts(dge, method = "cpm", threshold = 0)
    -
    - -
    -

    Arguments

    -
    dge
    -

    DGEList object containing raw barcode counts to be normalised

    - - -
    method
    -

    method of normalisation

    - - -
    threshold
    -

    Threshold to apply to counts before normalisation

    - -
    -
    -

    Value

    - - -

    Returns a data.frame object containing normalised barcode counts

    -
    - -
    -

    Examples

    - 
    data(test.dge)
-normaliseCounts(test.dge, method = "cpm", threshold = 0)
-#> Error in normaliseCounts(test.dge, method = "cpm", threshold = 0): method argument must be one of CPM, RLE, TMM, upperquartile or TMMwsp
-
-
    -
    -
    - - -
    -

    Developed by Dane Vassiliadis.

    -
    - -
    -

    Site built with pkgdown 2.0.7.

    -
    - -
    - - - - - - - diff --git a/docs/reference/plotAbundanceLines-1.png b/docs/reference/plotAbundanceLines-1.png deleted file mode 100644 index 2dc228e..0000000 Binary files a/docs/reference/plotAbundanceLines-1.png and /dev/null differ diff --git a/docs/reference/plotAbundanceLines.html b/docs/reference/plotAbundanceLines.html deleted file mode 100644 index 430b5bf..0000000 --- a/docs/reference/plotAbundanceLines.html +++ /dev/null @@ -1,143 +0,0 @@ - -Lineplot of barcode abundances in two conditions — plotAbundanceLines • bartools - Skip to contents - - -
    -
    -
    -

    Lineplot of barcode abundances in two conditions

    - -
    plotAbundanceLines.Rd
    -
    - -
    -

    Takes a dataframe of barcode counts, -computes the median abundance of each barcode for two specific conditions, -then do a line plot for both conditions

    -
    - -
    -

    Usage

    - 
    plotAbundanceLines(
-  dge,
-  condition,
-  condition_names,
-  plot_type = "DEG",
-  keep = 0.9,
-  nbarcode = 10,
-  title = ""
-)
    -
    - -
    -

    Arguments

    -
    dge
    -

    DGEList object containing raw counts of barcodes

    - - -
    condition
    -

    sample condition of interest

    - - -
    condition_names
    -

    vector of size 2. Gives the name of the two conditions in conditions to plot

    - - -
    plot_type
    -

    which barcodes are plotted. 'DEG' plot the 10 most differentially abundant barcodes. -'counts' plots the 5 top highest abundant barcodes for each condition. -'log2FC' plots the barcodes with the highest absolute log2FC (number of barcodes to plot is given by nbarcode)

    - - -
    keep
    -

    percentage of highest abundant barcode to keep. Rest of barcodes is filtered and not used in plots.

    - - -
    nbarcode
    -

    number of barcodes to plot when using 'log2FC' plot type.

    - - -
    title
    -

    desired plot title

    - -
    -
    -

    Value

    - - -

    Returns a lineplot

    -
    -
    -

    Details

    -

    plotAbundanceLines

    -

    Lineplot of the barcode abundances in two different experimental settings

    -
    - -
    -

    Examples

    - 
    data(test.dge)
-plotAbundanceLines(dge = test.dge, condition = test.dge$samples$group,
-condition_names = c("T0","10_High_dose"), plot_type = 'counts', title = 'test')
-
-
    -
    -
    - - -
    -

    Developed by Dane Vassiliadis.

    -
    - -
    -

    Site built with pkgdown 2.0.7.

    -
    - -
    - - - - - - - diff --git a/docs/reference/plotBARtabFilterQC.html b/docs/reference/plotBARtabFilterQC.html deleted file mode 100644 index 25cb60f..0000000 --- a/docs/reference/plotBARtabFilterQC.html +++ /dev/null @@ -1,127 +0,0 @@ - -plotBARtabFilterQC — plotBARtabFilterQC • bartools - Skip to contents - - -
    -
    -
    -

    plotBARtabFilterQC

    - -
    plotBARtabFilterQC.Rd
    -
    - -
    -

    Plot filtered read percentages from a BARtab run folder

    -
    - -
    -

    Usage

    - 
    plotBARtabFilterQC(
-  dir = NULL,
-  recursive = T,
-  pattern.log = "*filter.log",
-  pattern.value = "reads",
-  full.names = T,
-  normalised = F,
-  plot = T,
-  title = "BARtab Filter QC"
-)
    -
    - -
    -

    Arguments

    -
    dir
    -

    directory where BARtab was successfuly run on barcode count datasets

    - - -
    recursive
    -

    Logical. TRUE will recurse regex search into subdirectories#'

    - - -
    pattern.log
    -

    regex string to specify filter stage log files

    - - -
    pattern.value
    -

    grep on this string in log files

    - - -
    full.names
    -

    Logical. Return full names of files detected by regex search

    - - -
    normalised
    -

    Logical. log10 normalise counts

    - - -
    plot
    -

    Logical. Generate plots. False returns raw data

    - - -
    title
    -

    Optional. title of plots.

    - -
    -
    -

    Value

    - - -

    Returns a plot / dataset containing BARtab Filtering stage QC data

    -
    - -
    - - -
    -

    Developed by Dane Vassiliadis.

    -
    - -
    -

    Site built with pkgdown 2.0.7.

    -
    - -
    - - - - - - - diff --git a/docs/reference/plotBARtabMapQC.html b/docs/reference/plotBARtabMapQC.html deleted file mode 100644 index 4124601..0000000 --- a/docs/reference/plotBARtabMapQC.html +++ /dev/null @@ -1,117 +0,0 @@ - -plotBARtabMapQC — plotBARtabMapQC • bartools - Skip to contents - - -
    -
    -
    -

    plotBARtabMapQC

    - -
    plotBARtabMapQC.Rd
    -
    - -
    -

    Plot mapped read percentages from a BARtab run folder

    -
    - -
    -

    Usage

    - 
    plotBARtabMapQC(
-  dir = NULL,
-  recursive = T,
-  pattern.log = "*bowtie.log",
-  full.names = T,
-  plot = T,
-  title = "BARtab Mapping QC"
-)
    -
    - -
    -

    Arguments

    -
    dir
    -

    directory where BARtab was successfuly run on barcode count datasets

    - - -
    recursive
    -

    Logical. TRUE will recurse regex search into subdirectories#'

    - - -
    pattern.log
    -

    regex string to specify filter stage log files

    - - -
    full.names
    -

    Logical. Return full names of files detected by regex search

    - - -
    plot
    -

    Logical. Generate plots. False returns raw data

    - - -
    title
    -

    Optional. title of plots.

    - -
    -
    -

    Value

    - - -

    Returns a plot / dataset containing BARtab Filtering stage QC data

    -
    - -
    - - -
    -

    Developed by Dane Vassiliadis.

    -
    - -
    -

    Site built with pkgdown 2.0.7.

    -
    - -
    - - - - - - - diff --git a/docs/reference/plotBarcodeBoxplot-1.png b/docs/reference/plotBarcodeBoxplot-1.png deleted file mode 100644 index 6f03b0f..0000000 Binary files a/docs/reference/plotBarcodeBoxplot-1.png and /dev/null differ diff --git a/docs/reference/plotBarcodeBoxplot-2.png b/docs/reference/plotBarcodeBoxplot-2.png deleted file mode 100644 index 6f03b0f..0000000 Binary files a/docs/reference/plotBarcodeBoxplot-2.png and /dev/null differ diff --git a/docs/reference/plotBarcodeBoxplot.html b/docs/reference/plotBarcodeBoxplot.html deleted file mode 100644 index 04156c6..0000000 --- a/docs/reference/plotBarcodeBoxplot.html +++ /dev/null @@ -1,121 +0,0 @@ - -Boxplot of selected barcodes — plotBarcodeBoxplot • bartools - Skip to contents - - -
    -
    -
    -

    Boxplot of selected barcodes

    - -
    plotBarcodeBoxplot.Rd
    -
    - -
    -

    Takes a dataframe of barcode counts, -select specific barcodes in the dataframe -and boxplot their counts

    -
    - -
    -

    Usage

    - 
    plotBarcodeBoxplot(dge.obj, barcodes, condition = NULL)
    -
    - -
    -

    Arguments

    -
    dge.obj
    -

    DGE object containing counts and sample information

    - - -
    barcodes
    -

    vector of barcodes to be plotted

    - - -
    condition
    -

    specific metadata condition to be plotted

    - -
    -
    -

    Value

    - - -

    Returns a boxplot

    -
    -
    -

    Details

    -

    plotBCBoxplot

    -

    Boxplot of selected barcodes

    -
    - -
    -

    Examples

    - 
    data(test.dge)
-barcodes <- sample(rownames(test.dge), 10)
-plotBarcodeBoxplot(test.dge, barcodes)
-#> multiple barcodes given. No metadata group
-
-plotBarcodeBoxplot(test.dge, barcodes, condition = c("T0", "T0b"))
-#> multiple barcodes given. No metadata group
-
-
    -
    -
    - - -
    -

    Developed by Dane Vassiliadis.

    -
    - -
    -

    Site built with pkgdown 2.0.7.

    -
    - -
    - - - - - - - diff --git a/docs/reference/plotBarcodeBubble.html b/docs/reference/plotBarcodeBubble.html deleted file mode 100644 index 9af245d..0000000 --- a/docs/reference/plotBarcodeBubble.html +++ /dev/null @@ -1,117 +0,0 @@ - -plotBarcodeBubble — plotBarcodeBubble • bartools - Skip to contents - - -
    -
    -
    -

    plotBarcodeBubble

    - -
    plotBarcodeBubble.Rd
    -
    - -
    -

    Generate proportional bubbleplots from raw count object with barcodes labelled above a specified threshold

    -
    - -
    -

    Usage

    - 
    plotBarcodeBubble(
-  counts.obj,
-  labels = T,
-  proportion.cutoff = 10,
-  name = "Proportional Bubble Plot"
-)
    -
    - -
    -

    Arguments

    -
    counts.obj
    -

    dataframe containing raw counts of barcodes

    - - -
    labels
    -

    Boolean. print barcode labels?

    - - -
    proportion.cutoff
    -

    barcodes represented at a percentage within any sample above this threshold will be labelled

    - - -
    name
    -

    desired plot title

    - -
    -
    -

    Value

    - - -

    Returns a bubbleplot of barcodes represented by proportion of total pool

    -
    - -
    -

    Examples

    - 
    data(test.counts)
-#> Warning: data set ‘test.counts’ not found
-plotBarcodeBubble(test.counts, labels = TRUE, proportion.cutoff = 10)
-#> Error in as.data.frame(counts.obj): object 'test.counts' not found
-plotBarcodeBubble(test.counts, labels = FALSE, proportion.cutoff = 10)
-#> Error in as.data.frame(counts.obj): object 'test.counts' not found
-
    -
    -
    - - -
    -

    Developed by Dane Vassiliadis.

    -
    - -
    -

    Site built with pkgdown 2.0.7.

    -
    - -
    - - - - - - - diff --git a/docs/reference/plotBarcodeCorrelation-1.png b/docs/reference/plotBarcodeCorrelation-1.png deleted file mode 100644 index c2cfeb9..0000000 Binary files a/docs/reference/plotBarcodeCorrelation-1.png and /dev/null differ diff --git a/docs/reference/plotBarcodeCorrelation.html b/docs/reference/plotBarcodeCorrelation.html deleted file mode 100644 index 64a8f13..0000000 --- a/docs/reference/plotBarcodeCorrelation.html +++ /dev/null @@ -1,119 +0,0 @@ - -Sample to sample correlation — plotBarcodeCorrelation • bartools - Skip to contents - - -
    -
    -
    -

    Sample to sample correlation

    - -
    plotBarcodeCorrelation.Rd
    -
    - -
    -

    Plot sample correlation between barcode sets / samples

    -
    - -
    -

    Usage

    - 
    plotBarcodeCorrelation(
-  counts,
-  method = "pearson",
-  upper = T,
-  clustered = T,
-  name = "Sample Correlation"
-)
    -
    - -
    -

    Arguments

    -
    counts
    -

    matrix/dataframe containing raw or normalised counts

    - - -
    method
    -

    correlation metric to use. Refer to stats::cor for available options

    - - -
    upper
    -

    Logical. plot only the upper half of the matrix

    - - -
    clustered
    -

    Logical. cluster rows and columns of matrix

    - - -
    name
    -

    title of plot

    - -
    -
    -

    Value

    - - -

    Returns a heatmap of sample distances using desired clustering

    -
    - -
    -

    Examples

    - 
    test.mat <- matrix(rnorm(10*10,mean=0,sd=2), 10, 10)
-plotBarcodeCorrelation(counts = test.mat, method = "pearson")
-
-
    -
    -
    - - -
    -

    Developed by Dane Vassiliadis.

    -
    - -
    -

    Site built with pkgdown 2.0.7.

    -
    - -
    - - - - - - - diff --git a/docs/reference/plotBarcodeCounts.html b/docs/reference/plotBarcodeCounts.html deleted file mode 100644 index 50507c3..0000000 --- a/docs/reference/plotBarcodeCounts.html +++ /dev/null @@ -1,110 +0,0 @@ - -Plot total counts per barcode in library — plotBarcodeCounts • bartools - Skip to contents - - -
    -
    -
    -

    Plot total counts per barcode in library

    - -
    plotBarcodeCounts.Rd
    -
    - -
    -

    Simple plot of total read counts per barcode in library

    -
    - -
    -

    Usage

    - 
    plotBarcodeCounts(counts, order = F, log10 = F)
    -
    - -
    -

    Arguments

    -
    counts
    -

    data.frame of barcode count x sample

    - - -
    order
    -

    Logical. Order the dataset be decreasing abundance

    - - -
    log10
    -

    Logical. log10 transform the data. Adds a pseudocount of 1

    - -
    -
    -

    Value

    - - -

    Returns a plot of the read counts per barcode (row) in a data frame

    -
    - -
    -

    Examples

    - 
    data(test.counts)
-#> Warning: data set ‘test.counts’ not found
-plotBarcodeCounts(test.counts)
-#> Error in is.data.frame(x): object 'test.counts' not found
-plotBarcodeCounts(test.counts, order = TRUE)
-#> Error in is.data.frame(x): object 'test.counts' not found
-plotBarcodeCounts(test.counts, order = TRUE, log10 = TRUE)
-#> Error in is.data.frame(x): object 'test.counts' not found
-
    -
    -
    - - -
    -

    Developed by Dane Vassiliadis.

    -
    - -
    -

    Site built with pkgdown 2.0.7.

    -
    - -
    - - - - - - - diff --git a/docs/reference/plotBarcodeCumSum.html b/docs/reference/plotBarcodeCumSum.html deleted file mode 100644 index d4759de..0000000 --- a/docs/reference/plotBarcodeCumSum.html +++ /dev/null @@ -1,117 +0,0 @@ - -Barcode cumulative sum plot of a list of samples against and ordered by sample1 — plotBarcodeCumSum • bartools - Skip to contents - - -
    -
    -
    -

    Barcode cumulative sum plot of a list of samples against and ordered by sample1

    - -
    plotBarcodeCumSum.Rd
    -
    - -
    -

    Takes a dataframe of the barcode cpm counts, calculate the relative abundance of each barcode in each sample. -Then, barcodes of all samples are ordered for sample1 in decreasing order and the cumulative sum is calculated for -each sample. Cumulative sum is then plotted against sample1.

    -
    - -
    -

    Usage

    - 
    plotBarcodeCumSum(counts, sample1 = NULL, samples = NULL)
    -
    - -
    -

    Arguments

    -
    counts
    -

    Dataframe containing cpm counts

    - - -
    sample1
    -

    sample to compare others against

    - - -
    samples
    -

    vector of sample names to be plotted against sample1

    - -
    -
    -

    Value

    - - -

    Returns a plot of the cumulative sum of read counts per barcode

    -
    -
    -

    Details

    -

    plotBarcodeCumSum

    -

    Plot the cumulative sum of barcode cpm counts for a list of samples against and ordered by sample1.

    -
    - -
    -

    Examples

    - 
    data(test.counts)
-#> Warning: data set ‘test.counts’ not found
-plotBarcodeCumSum(counts = test.counts, sample1 = 'T0-1', samples = c('T0-1','S9-1', 'S8-1'))
-#> Error in plotBarcodeCumSum(counts = test.counts, sample1 = "T0-1", samples = c("T0-1",     "S9-1", "S8-1")): object 'test.counts' not found
-
    -
    -
    - - -
    -

    Developed by Dane Vassiliadis.

    -
    - -
    -

    Site built with pkgdown 2.0.7.

    -
    - -
    - - - - - - - diff --git a/docs/reference/plotBarcodeDistance-1.png b/docs/reference/plotBarcodeDistance-1.png deleted file mode 100644 index c3a9d1e..0000000 Binary files a/docs/reference/plotBarcodeDistance-1.png and /dev/null differ diff --git a/docs/reference/plotBarcodeDistance.html b/docs/reference/plotBarcodeDistance.html deleted file mode 100644 index 5202c3a..0000000 --- a/docs/reference/plotBarcodeDistance.html +++ /dev/null @@ -1,119 +0,0 @@ - -Sample to sample distances — plotBarcodeDistance • bartools - Skip to contents - - -
    -
    -
    -

    Sample to sample distances

    - -
    plotBarcodeDistance.Rd
    -
    - -
    -

    Plot sample distances between barcode sets / samples

    -
    - -
    -

    Usage

    - 
    plotBarcodeDistance(
-  counts,
-  method = "euclidean",
-  upper = T,
-  clustered = T,
-  name = "Sample Distances"
-)
    -
    - -
    -

    Arguments

    -
    counts
    -

    matrix/dataframe containing raw or normalised counts

    - - -
    method
    -

    distance metric to use. Refer to stats::dist for available options

    - - -
    upper
    -

    Logical. plot only the upper half of the matrix

    - - -
    clustered
    -

    Logical. cluster rows and columns of matrix

    - - -
    name
    -

    title of plot

    - -
    -
    -

    Value

    - - -

    Returns a heatmap of sample distances using desired clustering

    -
    - -
    -

    Examples

    - 
    test.mat <- matrix(rnorm(10*10,mean=0,sd=2), 10, 10)
-plotBarcodeDistance(test.mat)
-
-
    -
    -
    - - -
    -

    Developed by Dane Vassiliadis.

    -
    - -
    -

    Site built with pkgdown 2.0.7.

    -
    - -
    - - - - - - - diff --git a/docs/reference/plotBarcodeHeatmap.html b/docs/reference/plotBarcodeHeatmap.html deleted file mode 100644 index 2bfcd1b..0000000 --- a/docs/reference/plotBarcodeHeatmap.html +++ /dev/null @@ -1,121 +0,0 @@ - -Plot barcode heatmap — plotBarcodeHeatmap • bartools - Skip to contents - - -
    -
    -
    -

    Plot barcode heatmap

    - -
    plotBarcodeHeatmap.Rd
    -
    - -
    -

    Takes a dataframe of the barcode counts, and selects the N most abundant barcodes per sample. -Most abundant barcodes are gathered in a list and a heatmap for those barcodes is plotted for all samples. -Stars indicate the most abundant barcodes per sample.

    -
    - -
    -

    Usage

    - 
    plotBarcodeHeatmap(counts, N, name = "CPM", show_bc = FALSE)
    -
    - -
    -

    Arguments

    -
    counts
    -

    Dataframe containing cpm counts

    - - -
    N
    -

    String of sample 1 name (used to ranked barcode abundance)

    - - -
    name
    -

    String of the heatmap scale name

    - - -
    show_bc
    -

    Boolean to show barcode names on the heatmap

    - -
    -
    -

    Value

    - - -

    Returns a heatmap

    -
    -
    -

    Details

    -

    plotBarcodeHeatmap

    -

    Plot a heatmap of the N most abundant barcodes per sample

    -
    - -
    -

    Examples

    - 
    data(test.counts)
-#> Warning: data set ‘test.counts’ not found
-plotBarcodeHeatmap(test.counts, 10,'Counts',FALSE)
-#> Error in is.data.frame(x): object 'test.counts' not found
-
    -
    -
    - - -
    -

    Developed by Dane Vassiliadis.

    -
    - -
    -

    Site built with pkgdown 2.0.7.

    -
    - -
    - - - - - - - diff --git a/docs/reference/plotBarcodeHistogram.html b/docs/reference/plotBarcodeHistogram.html deleted file mode 100644 index 97cd108..0000000 --- a/docs/reference/plotBarcodeHistogram.html +++ /dev/null @@ -1,115 +0,0 @@ - -plotBarcodeHistogram — plotBarcodeHistogram • bartools - Skip to contents - - -
    -
    -
    -

    plotBarcodeHistogram

    - -
    plotBarcodeHistogram.Rd
    -
    - -
    -

    Generate stacked barcode plots showing proportion from raw count object.

    -
    - -
    -

    Usage

    - 
    plotBarcodeHistogram(
-  counts.obj,
-  sample = NULL,
-  top = 10,
-  name = "Barcode Histogram"
-)
    -
    - -
    -

    Arguments

    -
    counts.obj
    -

    dataframe containing raw counts of barcodes

    - - -
    sample
    -

    desired name of sample to order all barcodes against

    - - -
    top
    -

    top n barcodes in sample to color, all other barcodes are shown in grey

    - - -
    name
    -

    desired plot title

    - -
    -
    -

    Value

    - - -

    Returns a barcode histogram plot of barcodes represented by proportion of total pool

    -
    - -
    -

    Examples

    - 
    data(test.counts)
-#> Warning: data set ‘test.counts’ not found
-plotBarcodeHistogram(test.counts, sample = "T0-1", top = 10, name = "Barcode Histogram")
-#> Error in sweep(counts.obj, 2, colSums(counts.obj), `/`): object 'test.counts' not found
-
    -
    -
    - - -
    -

    Developed by Dane Vassiliadis.

    -
    - -
    -

    Site built with pkgdown 2.0.7.

    -
    - -
    - - - - - - - diff --git a/docs/reference/plotBarcodePCA-1.png b/docs/reference/plotBarcodePCA-1.png deleted file mode 100644 index 15304af..0000000 Binary files a/docs/reference/plotBarcodePCA-1.png and /dev/null differ diff --git a/docs/reference/plotBarcodePCA.html b/docs/reference/plotBarcodePCA.html deleted file mode 100644 index b6d31b7..0000000 --- a/docs/reference/plotBarcodePCA.html +++ /dev/null @@ -1,135 +0,0 @@ - -Barcode PCA plot — plotBarcodePCA • bartools - Skip to contents - - -
    -
    -
    -

    Barcode PCA plot

    - -
    plotBarcodePCA.Rd
    -
    - -
    -

    Takes an edgeR DGEList object contining barcode data or a dataframe of the barcode cpm counts and performs PCA. -Then, plots the first two dimensions. -Modified from DESeq2 package plotPCA function

    -
    - -
    -

    Usage

    - 
    plotBarcodePCA(
-  object,
-  intgroup = "condition",
-  col = "group",
-  ntop = 500,
-  returnData = FALSE,
-  batch = NULL
-)
    -
    - -
    -

    Arguments

    -
    object
    -

    DGEList or dataframe containing normalised barcode counts

    - - -
    intgroup
    -

    vector of grouping variables of interest

    - - -
    col
    -

    color group for plot, must be one of intgroup

    - - -
    ntop
    -

    number of top most variable genes to be used in PCA calculation

    - - -
    returnData
    -

    Logical. return a data.frame of PCA calculation?

    - - -
    batch
    -

    metadata category indicating source of batch effects. Will be used in limma removeBatchEffect function prior to PCA

    - -
    -
    -

    Value

    - - -

    Returns a plot of the first two principal components in the dataset

    -
    -
    -

    Details

    -

    plotBarcodePCA

    -

    Plot the first two principal components of the barcode dataset

    -
    - -
    -

    Examples

    - 
    data(test.dge)
-plotBarcodePCA(test.dge, intgroup = "group", ntop = 500, returnData = FALSE, batch = NULL)
-
-
    -
    -
    - - -
    -

    Developed by Dane Vassiliadis.

    -
    - -
    -

    Site built with pkgdown 2.0.7.

    -
    - -
    - - - - - - - diff --git a/docs/reference/plotBarcodeRegression-1.png b/docs/reference/plotBarcodeRegression-1.png deleted file mode 100644 index 6433fe5..0000000 Binary files a/docs/reference/plotBarcodeRegression-1.png and /dev/null differ diff --git a/docs/reference/plotBarcodeRegression.html b/docs/reference/plotBarcodeRegression.html deleted file mode 100644 index d263028..0000000 --- a/docs/reference/plotBarcodeRegression.html +++ /dev/null @@ -1,130 +0,0 @@ - -plotBarcodeRegression — plotBarcodeRegression • bartools - Skip to contents - - -
    -
    -
    -

    plotBarcodeRegression

    - -
    plotBarcodeRegression.Rd
    -
    - -
    -

    Generate a linear regression scatterplot for two sets of sample counts.

    -
    - -
    -

    Usage

    - 
    plotBarcodeRegression(
-  dge,
-  samp1 = NULL,
-  samp2 = NULL,
-  title = NULL,
-  trendline = T,
-  trans = NULL,
-  rug = F
-)
    -
    - -
    -

    Arguments

    -
    dge
    -

    DGEList object containing grouping variable to fit linear model

    - - -
    samp1
    -

    name of sample 1. must be one of colnames(dge)

    - - -
    samp2
    -

    name of sample 2. must be one of colnames(dge)

    - - -
    title
    -

    desired name of output plot

    - - -
    trendline
    -

    Logical. Include linear trendline using stat_smooth()

    - - -
    trans
    -

    From ggplot2. For continuous scales, the name of a transformation object or the object itself.

    - - -
    rug
    -

    Logical. Include geom_rug density information on the axes?

    - -
    -
    -

    Value

    - - -

    Returns a scatterplot of the two samples

    -
    - -
    -

    Examples

    - 
    data(test.dge)
-plotBarcodeRegression(dge = test.dge, samp1 = "T0-1", samp2 = "T0-2")
-#> `geom_smooth()` using formula = 'y ~ x'
-
-
    -
    -
    - - -
    -

    Developed by Dane Vassiliadis.

    -
    - -
    -

    Site built with pkgdown 2.0.7.

    -
    - -
    - - - - - - - diff --git a/docs/reference/plotBarcodeTimeseries.html b/docs/reference/plotBarcodeTimeseries.html deleted file mode 100644 index 2923e70..0000000 --- a/docs/reference/plotBarcodeTimeseries.html +++ /dev/null @@ -1,110 +0,0 @@ - -plotBarcodeTimeseries — plotBarcodeTimeseries • bartools - Skip to contents - - -
    -
    -
    -

    plotBarcodeTimeseries

    - -
    plotBarcodeTimeseries.Rd
    -
    - -
    -

    Generate proportional timeseries plot from raw / normalised barcode count object.

    -
    - -
    -

    Usage

    - 
    plotBarcodeTimeseries(counts.obj, name = "", seed = 5, top = 50)
    -
    - -
    -

    Arguments

    -
    counts.obj
    -

    dataframe containing raw counts of barcodes

    - - -
    name
    -

    desired plot title

    - - -
    seed
    -

    RNG seed

    - - -
    top
    -

    number of top barcodes per sample to plot

    - -
    -
    -

    Value

    - - -

    Returns a bubbleplot of barcodes represented by proportion of total pool

    -
    - -
    -

    Examples

    - 
    data(test.counts)
-#> Warning: data set ‘test.counts’ not found
-plotBarcodeTimeseries(test.counts[,1:4], name = "Proportional Timeseries Plot", seed = 5, top = 10)
-#> Error in as.data.frame(counts.obj): object 'test.counts' not found
-
    -
    -
    - - -
    -

    Developed by Dane Vassiliadis.

    -
    - -
    -

    Site built with pkgdown 2.0.7.

    -
    - -
    - - - - - - - diff --git a/docs/reference/plotDetectedBarcodes-1.png b/docs/reference/plotDetectedBarcodes-1.png deleted file mode 100644 index 5ca852b..0000000 Binary files a/docs/reference/plotDetectedBarcodes-1.png and /dev/null differ diff --git a/docs/reference/plotDetectedBarcodes.html b/docs/reference/plotDetectedBarcodes.html deleted file mode 100644 index 1cbc652..0000000 --- a/docs/reference/plotDetectedBarcodes.html +++ /dev/null @@ -1,174 +0,0 @@ - -plotDetectedBarcodes — plotDetectedBarcodes • bartools - Skip to contents - - -
    -
    -
    -

    plotDetectedBarcodes

    - -
    plotDetectedBarcodes.Rd
    -
    - -
    -

    Plot the total number of barcodes detected in a sample

    -
    - -
    -

    Usage

    - 
    plotDetectedBarcodes(
-  counts,
-  percentile = 0.95,
-  plot = T,
-  sample.order = NULL,
-  group = NULL,
-  title = NULL
-)
    -
    - -
    -

    Arguments

    -
    counts
    -

    DGEList or dataframe containing raw or normalised barcode counts

    - - -
    percentile
    -

    desired percentile value. 95th percentile by default

    - - -
    plot
    -

    Logical. plot data instead of returning counts table.

    - - -
    sample.order
    -

    desired ordering of the samples on the plot

    - - -
    group
    -

    grouping field in dgelist$samples to color samples by

    - - -
    title
    -

    desired plot title

    - -
    -
    -

    Value

    - - -

    Returns a histogram plot of the number of detected barcodes per sample.

    -
    - -
    -

    Examples

    - 
    data(test.dge)
-plotDetectedBarcodes(test.dge, percentile = .95)
-#> Warning: Use of `percentile.df$Sample` is discouraged.
-#> ℹ Use `Sample` instead.
-#> Warning: Use of `percentile.df$Barcodes` is discouraged.
-#> ℹ Use `Barcodes` instead.
-#> Warning: Use of `percentile.df$Barcodes` is discouraged.
-#> ℹ Use `Barcodes` instead.
-#> Warning: Use of `percentile.df$Barcodes` is discouraged.
-#> ℹ Use `Barcodes` instead.
-#> Warning: Use of `percentile.df$Sample` is discouraged.
-#> ℹ Use `Sample` instead.
-
-plotDetectedBarcodes(test.dge, plot = FALSE)
-#>    Sample Barcodes
-#> 1    T0-1      593
-#> 2    T0-2      590
-#> 3   S10-1        3
-#> 4   S10-2        4
-#> 5   S11-1       19
-#> 6   S11-2       20
-#> 7   S12-1       14
-#> 8   S12-2       14
-#> 9   S13-1       10
-#> 10  S13-2       11
-#> 11  S14-1        6
-#> 12  S14-2        6
-#> 13  S15-1        7
-#> 14  S15-2        7
-#> 15  S16-1       19
-#> 16  S16-2       20
-#> 17  S17-1        5
-#> 18  S17-2        6
-#> 19  S18-1       27
-#> 20  S18-2       27
-#> 21   S1-1       14
-#> 22   S1-2       15
-#> 23   S2-1       12
-#> 24   S2-2       11
-#> 25   S3-1       10
-#> 26   S3-2       10
-#> 27   S4-1       25
-#> 28   S4-2       26
-#> 29   S5-1       11
-#> 30   S5-2       12
-#> 31   S6-1        9
-#> 32   S6-2       10
-#> 33   S7-1       16
-#> 34   S7-2       16
-#> 35   S8-1       20
-#> 36   S8-2       21
-#> 37   S9-1       19
-#> 38   S9-2       19
-
    -
    -
    - - -
    -

    Developed by Dane Vassiliadis.

    -
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    -

    Site built with pkgdown 2.0.7.

    -
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    - - - - - - - diff --git a/docs/reference/plotLibraryCumSum.html b/docs/reference/plotLibraryCumSum.html deleted file mode 100644 index ed9140c..0000000 --- a/docs/reference/plotLibraryCumSum.html +++ /dev/null @@ -1,109 +0,0 @@ - -Cumulative sum plot of barcode abundances in a reference library — plotLibraryCumSum • bartools - Skip to contents - - -
    -
    -
    -

    Cumulative sum plot of barcode abundances in a reference library

    - -
    plotLibraryCumSum.Rd
    -
    - -
    -

    Takes a dataframe of barcode counts, -computes the median abundance of each barcode for two specific conditions, -then do a line plot for both conditions

    -
    - -
    -

    Usage

    - 
    plotLibraryCumSum(barcodes, samplename = "Library", cutoff = 10)
    -
    - -
    -

    Arguments

    -
    barcodes
    -

    dataframe containing raw counts of barcodes

    - - -
    samplename
    -

    sample condition of interest

    - - -
    cutoff
    -

    rowsum cutoff defining rows to keep

    - -
    -
    -

    Value

    - - -

    Returns a cumulative sum plot for the desired barcode library sample

    -
    -
    -

    Details

    -

    plotLibraryCumSum

    -

    Plot the cumulative sum of the barcode abundances in a barcode library

    -
    - -
    - - -
    -

    Developed by Dane Vassiliadis.

    -
    - -
    -

    Site built with pkgdown 2.0.7.

    -
    - -
    - - - - - - - diff --git a/docs/reference/plotLibraryDiversity.html b/docs/reference/plotLibraryDiversity.html deleted file mode 100644 index 56b605a..0000000 --- a/docs/reference/plotLibraryDiversity.html +++ /dev/null @@ -1,110 +0,0 @@ - -Lineplot of barcode abundances in two conditions — plotLibraryDiversity • bartools - Skip to contents - - -
    -
    -
    -

    Lineplot of barcode abundances in two conditions

    - -
    plotLibraryDiversity.Rd
    -
    - -
    -

    Takes a dataframe of barcode counts, -computes and plots the abundance of each barcode in the library sample

    -
    - -
    -

    Usage

    - 
    plotLibraryDiversity(barcodes, samplename = "Library", cutoff = 10, skew = F)
    -
    - -
    -

    Arguments

    -
    barcodes
    -

    dataframe containing raw counts of barcodes

    - - -
    samplename
    -

    sample condition of interest

    - - -
    cutoff
    -

    rowsum cutoff defining rows to keep

    - - -
    skew
    -

    Logical. Calculate and plot the library skew ratio (90th percentile / 10th percentile)

    - -
    -
    -

    Value

    - - -

    Returns a barcode library frequency plot

    -
    -
    -

    Details

    -

    plotLibraryDiversity

    -

    Lineplot of the barcode abundances in two different experimental settings

    -
    - -
    - - -
    -

    Developed by Dane Vassiliadis.

    -
    - -
    -

    Site built with pkgdown 2.0.7.

    -
    - -
    - - - - - - - diff --git a/docs/reference/plotOrderedBubble.html b/docs/reference/plotOrderedBubble.html deleted file mode 100644 index 1dcf483..0000000 --- a/docs/reference/plotOrderedBubble.html +++ /dev/null @@ -1,125 +0,0 @@ - -plotOrderedBubble — plotOrderedBubble • bartools - Skip to contents - - -
    -
    -
    -

    plotOrderedBubble

    - -
    plotOrderedBubble.Rd
    -
    - -
    -

    Generate ordered proportional bubbleplots from raw count object with barcodes labelled above a specified threshold

    -
    - -
    -

    Usage

    - 
    plotOrderedBubble(
-  counts.obj,
-  labels = T,
-  name = "Proportional Bubble Plot",
-  orderSample = NULL,
-  proportion.cutoff = 10,
-  colorDominant = FALSE
-)
    -
    - -
    -

    Arguments

    -
    counts.obj
    -

    dataframe containing raw counts of barcodes. assumes barcodes as rownames.

    - - -
    labels
    -

    logical. Print barcode labels

    - - -
    name
    -

    desired plot title

    - - -
    orderSample
    -

    name of sample to order by

    - - -
    proportion.cutoff
    -

    barcodes represented at a percentage within any sample above this threshold will be labelled

    - - -
    colorDominant
    -

    only color clones with frequency above proportion.cutoff. Others colored grey

    - -
    -
    -

    Value

    - - -

    Returns a bubbleplot of barcodes represented by proportion of total pool

    -
    - -
    -

    Examples

    - 
    data(test.counts)
-#> Warning: data set ‘test.counts’ not found
-plotOrderedBubble(test.counts, orderSample = "T0-1")
-#> Error in methods::is(counts): object 'counts' not found
-
    -
    -
    - - -
    -

    Developed by Dane Vassiliadis.

    -
    - -
    -

    Site built with pkgdown 2.0.7.

    -
    - -
    - - - - - - - diff --git a/docs/reference/plotReadCounts-1.png b/docs/reference/plotReadCounts-1.png deleted file mode 100644 index 37356cc..0000000 Binary files a/docs/reference/plotReadCounts-1.png and /dev/null differ diff --git a/docs/reference/plotReadCounts.html b/docs/reference/plotReadCounts.html deleted file mode 100644 index be86a0f..0000000 --- a/docs/reference/plotReadCounts.html +++ /dev/null @@ -1,121 +0,0 @@ - -Plot sample read counts — plotReadCounts • bartools - Skip to contents - - -
    -
    -
    -

    Plot sample read counts

    - -
    plotReadCounts.Rd
    -
    - -
    -

    Simple plot of total read counts per sample

    -
    - -
    -

    Usage

    - 
    plotReadCounts(
-  counts,
-  group = NULL,
-  log10 = FALSE,
-  legend = TRUE,
-  order = TRUE
-)
    -
    - -
    -

    Arguments

    -
    counts
    -

    data.frame of barcode count x sample

    - - -
    group
    -

    a character vector of containing grouping information. Must be equal the number of columns of counts. Can pass a metadata column from DGEList object.

    - - -
    log10
    -

    Boolean. log10 transform output?

    - - -
    legend
    -

    Boolean. Include legend?

    - - -
    order
    -

    Boolean. Order samples by group?

    - -
    -
    -

    Value

    - - -

    Returns a plot of the read counts per column (sample) in a data frame

    -
    - -
    -

    Examples

    - 
    data(test.counts)
-#> Warning: data set ‘test.counts’ not found
-plotReadCounts(test.dge$counts, group = test.dge$samples$Group)
-
-
-
    -
    -
    - - -
    -

    Developed by Dane Vassiliadis.

    -
    - -
    -

    Site built with pkgdown 2.0.7.

    -
    - -
    - - - - - - - diff --git a/docs/reference/processBarcodeLibrary.html b/docs/reference/processBarcodeLibrary.html deleted file mode 100644 index 3c4876f..0000000 --- a/docs/reference/processBarcodeLibrary.html +++ /dev/null @@ -1,121 +0,0 @@ - -processBarcodeLibrary — processBarcodeLibrary • bartools - Skip to contents - - -
    -
    -
    -

    processBarcodeLibrary

    - -
    processBarcodeLibrary.Rd
    -
    - -
    -

    process barcode reference file from raw sequencing datasets

    -
    - -
    -

    Usage

    - 
    processBarcodeLibrary(
-  file = NULL,
-  samplename = "Barcode",
-  cutoff = 10,
-  header = FALSE,
-  outdir = tempdir()
-)
    -
    - -
    -

    Arguments

    -
    file
    -

    path to starcode output for barcode library

    - - -
    samplename
    -

    name of reference library. Will be prefixed to each barcode in the reference

    - - -
    cutoff
    -

    rowsum cutoff defining barcodes to keep in the reference library

    - - -
    header
    -

    Logical. Does the input file contain a header line

    - - -
    outdir
    -

    Desired output directory to save library files

    - -
    -
    -

    Value

    - - -

    returns data frame containing barcode and read count in reference

    -
    - -
    -

    Examples

    - 
    load(system.file("extdata", "test_raw_lib.rda", package = "bartools"))
-#> Warning: cannot open compressed file '', probable reason 'No such file or directory'
-#> Error in readChar(con, 5L, useBytes = TRUE): cannot open the connection
-processBarcodeLibrary(file = test.raw.lib, samplename = "Barcode", outdir = tempdir())
-#> Error in processBarcodeLibrary(file = test.raw.lib, samplename = "Barcode",     outdir = tempdir()): object 'test.raw.lib' not found
-
    -
    -
    - - -
    -

    Developed by Dane Vassiliadis.

    -
    - -
    -

    Site built with pkgdown 2.0.7.

    -
    - -
    - - - - - - - diff --git a/docs/reference/test.counts.html b/docs/reference/test.counts.html deleted file mode 100644 index ac3ace5..0000000 --- a/docs/reference/test.counts.html +++ /dev/null @@ -1,92 +0,0 @@ - -Test barcode counts — test.counts • bartools - Skip to contents - - -
    -
    -
    -

    Test barcode counts

    - -
    test.counts.Rd
    -
    - -
    -

    A dataset containing normalised count information for 1639 hypothetical barcodes -in 38 hypothetical samples

    -
    - -
    -

    Usage

    - 
    test.counts
    -
    - -
    -

    Format

    -

    A data frame with 1639 rows and 38 variables:

    T0-1
    -

    sample T0, replicate 1

    - -
    T0-2
    -

    sample T0, replicate 2

    - - ... - -
    - -
    - - -
    -

    Developed by Dane Vassiliadis.

    -
    - -
    -

    Site built with pkgdown 2.0.7.

    -
    - -
    - - - - - - - diff --git a/docs/reference/test.dge.html b/docs/reference/test.dge.html deleted file mode 100644 index ff6f3cd..0000000 --- a/docs/reference/test.dge.html +++ /dev/null @@ -1,85 +0,0 @@ - -Test barcode DGEList object — test.dge • bartools - Skip to contents - - -
    -
    -
    -

    Test barcode DGEList object

    - -
    test.dge.Rd
    -
    - -
    -

    A DGEList object containing raw count information for 1639 hypothetical barcodes -in 38 hypothetical samples

    -
    - -
    -

    Usage

    - 
    test.dge
    -
    - -
    -

    Format

    -

    A DGEList object containing 1639 rows and 38 variables:

    -
    - -
    - - -
    -

    Developed by Dane Vassiliadis.

    -
    - -
    -

    Site built with pkgdown 2.0.7.

    -
    - -
    - - - - - - - diff --git a/docs/reference/thresholdCounts.html b/docs/reference/thresholdCounts.html deleted file mode 100644 index 8fcd1d0..0000000 --- a/docs/reference/thresholdCounts.html +++ /dev/null @@ -1,107 +0,0 @@ - -Threshold counts — thresholdCounts • bartools - Skip to contents - - -
    -
    -
    -

    Threshold counts

    - -
    thresholdCounts.Rd
    -
    - -
    -

    Threshold dataframe to a given level and return number of barcodes meeting threshold in each -sample.

    -
    - -
    -

    Usage

    - 
    thresholdCounts(df, threshold = 20, plot = FALSE)
    -
    - -
    -

    Arguments

    -
    df
    -

    Dataframe to be thresholded.

    - - -
    threshold
    -

    The threshold to use. Rows with count below this will be removed.

    - - -
    plot
    -

    Logical. Draw plots of dataset?

    - -
    -
    -

    Value

    - - -

    Returns a thresholded data-frame

    -
    - -
    -

    Examples

    - 
    thresholdCounts(test.counts, threshold = 20, plot = FALSE)
-#> Error in thresholdCounts(test.counts, threshold = 20, plot = FALSE): object 'test.counts' not found
-
    -
    -
    - - -
    -

    Developed by Dane Vassiliadis.

    -
    - -
    -

    Site built with pkgdown 2.0.7.

    -
    - -
    - - - - - - - diff --git a/docs/search.json b/docs/search.json deleted file mode 100644 index 7052311..0000000 --- a/docs/search.json +++ /dev/null @@ -1 +0,0 @@ -[{"path":[]},{"path":"https://danevass.github.io/bartools/articles/preprocessing.html","id":"getting-started","dir":"Articles","previous_headings":"Cellular Barcoding Dataset Preprocessing","what":"Getting started","title":"bartools: preprocessing datasets ","text":"first step cellular barcoding analysis extract barcode containing reads high throughput sequencing datasets count number barcode within sample. bartools package optimised use SPLINTR lineage barcode libraries (See Fennell Vassiliadis et al. Nature 2021) however functions within can adapted DNA barcoding methodologies use randomised DNA barcodes.","code":""},{"path":"https://danevass.github.io/bartools/articles/preprocessing.html","id":"the-reference-barcode-library","dir":"Articles","previous_headings":"Cellular Barcoding Dataset Preprocessing","what":"The reference barcode library","title":"bartools: preprocessing datasets ","text":"current version, bartools assumes barcoding experiment uses known reference library barcodes. Since DNA barcodes generally introduced cells using viral vectors, plasmid pool used generate barcode virus represents reference set barcodes can present experiment. primary step barcoding analysis determine barcodes present reference pool proportion. case SPLINTR, barcodes take following form: lower case sequence represents upstream downstream constant regions critical accurately retrieving barcodes genomic DNA cells. Load bartools library","code":"tgaccatgtacgattgactaNNSWSNNWSWNNSWSNNWSWNNSWSNNWSWNNSWSNNWSWNNSWSNNWSWNNSWSNNWSWtgctaatgcgtactg library(bartools) #> Loading required package: edgeR #> Loading required package: limma #> Loading required package: ggplot2"},{"path":"https://danevass.github.io/bartools/articles/preprocessing.html","id":"barcode-pre-processing","dir":"Articles","previous_headings":"Cellular Barcoding Dataset Preprocessing","what":"Barcode pre-processing","title":"bartools: preprocessing datasets ","text":"Barcode information can extracted raw fastq files summarised table counts per barcode follows Firstly define expected barcode pattern upstream downstream constant regions flank true barcodes. extract reads containing barcode pattern using extractBarcodeReads function Following barcode extraction make use previously generated library reference barcodes map extracted sequences reference using mapBarcodeReads. True barcodes align without mismatches. Reads containing sequencing errors false barcode sequences snuck extraction step filtered . bowtie reference yet generated can specify reference_fasta place bowtie_index generated automatically. mapBarcodeReads counts number occurrences barcode saves output file. can easily load examine final count data .","code":"pattern <- \"([ACTG][ACTG][GC][AT][GC][ACTG][ACTG][AT][GC][AT]){3,6}\" fastq <- system.file(\"extdata\", \"test_extract_75bp_single-end.fastq.gz\", package = \"bartools\", mustWork = T) constant <- toupper(\"tgaccatgtacgattgacta\") test.extract <- bartools::extractbartoolseads(infile = fastq, barcode_pattern = pattern, constant = constant, yieldSize = 1e6) test.extract test.map <- bartools::mapbartoolseads(reads = test.extract, bowtie_index = \"../data/bowtie/index\", mismatches = 1, threads = 1, prefix = \"test_map\") test.map reference.fa <- system.file(\"extdata\", \"barcode_lib_reference_test.fasta\", package = \"bartools\", mustWork = T) test.map <- bartools::mapBarcodeReads(reads = test.extract, bowtie_index = NULL, reference_fasta = reference.fa, mismatches = 1, # maximum 3 mismatches threads = 2, prefix = \"test_map\") test.map test.counts <- utils::read.delim(\"./test_map_counts.csv\", header = T, sep = \",\", row.names = 1) test.counts %>% tibble::rownames_to_column() %>% dplyr::arrange(dplyr::desc(mapped))"},{"path":"https://danevass.github.io/bartools/articles/preprocessing.html","id":"alternative-pre-processing-workflow-using-bartab","dir":"Articles","previous_headings":"Cellular Barcoding Dataset Preprocessing","what":"Alternative pre-processing workflow using BARtab","title":"bartools: preprocessing datasets ","text":"large numbers samples can cumbersome time consuming process raw count data within R shown . efficient process samples generate count tables using pipeline can distributed across HPC infrastructure developed BARtab, Nextflow pipeline preprocessing DNA barcoding datasets bulk single-cell experiments. pipeline associated running instructions can found https://github.com/DaneVass/BARtab.","code":""},{"path":"https://danevass.github.io/bartools/articles/preprocessing.html","id":"session-info","dir":"Articles","previous_headings":"Cellular Barcoding Dataset Preprocessing","what":"Session Info","title":"bartools: preprocessing datasets ","text":"","code":"sessionInfo() #> R version 4.2.2 (2022-10-31) #> Platform: aarch64-apple-darwin20 (64-bit) #> Running under: macOS Ventura 13.1 #> #> Matrix products: default #> BLAS: /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRblas.0.dylib #> LAPACK: /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRlapack.dylib #> #> locale: #> [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8 #> #> attached base packages: #> [1] stats graphics grDevices utils datasets methods base #> #> other attached packages: #> [1] bartools_0.2.3 ggplot2_3.4.2 edgeR_3.40.2 limma_3.54.2 #> #> loaded via a namespace (and not attached): #> [1] locfit_1.5-9.8 tidyselect_1.2.0 xfun_0.39 bslib_0.5.0 #> [5] purrr_1.0.1 splines_4.2.2 lattice_0.21-8 colorspace_2.1-0 #> [9] vctrs_0.6.3 generics_0.1.3 htmltools_0.5.5 mgcv_1.9-0 #> [13] yaml_2.3.7 utf8_1.2.3 rlang_1.1.1 pkgdown_2.0.7 #> [17] jquerylib_0.1.4 pillar_1.9.0 glue_1.6.2 withr_2.5.0 #> [21] lifecycle_1.0.3 stringr_1.5.0 munsell_0.5.0 gtable_0.3.3 #> [25] ragg_1.2.5 memoise_2.0.1 evaluate_0.21 knitr_1.43 #> [29] permute_0.9-7 fastmap_1.1.1 parallel_4.2.2 fansi_1.0.4 #> [33] Rcpp_1.0.11 scales_1.2.1 cachem_1.0.8 vegan_2.6-4 #> [37] desc_1.4.2 jsonlite_1.8.7 ineq_0.2-13 systemfonts_1.0.4 #> [41] fs_1.6.2 textshaping_0.3.6 digest_0.6.33 stringi_1.7.12 #> [45] dplyr_1.1.2 grid_4.2.2 rprojroot_2.0.3 cli_3.6.1 #> [49] tools_4.2.2 magrittr_2.0.3 sass_0.4.6 tibble_3.2.1 #> [53] cluster_2.1.4 pkgconfig_2.0.3 Matrix_1.6-0 MASS_7.3-60 #> [57] rmarkdown_2.23 rstudioapi_0.15.0 R6_2.5.1 nlme_3.1-162 #> [61] compiler_4.2.2"},{"path":"https://danevass.github.io/bartools/articles/quickstart.html","id":"bartools-quickstart-guide","dir":"Articles","previous_headings":"","what":"Bartools Quickstart Guide","title":"bartools: quickstart guide","text":"Load bartools library","code":"library(bartools) ## Loading required package: edgeR ## Loading required package: limma ## Loading required package: ggplot2"},{"path":"https://danevass.github.io/bartools/articles/quickstart.html","id":"importing-dna-barcode-count-data","dir":"Articles","previous_headings":"Bartools Quickstart Guide","what":"1. Importing DNA barcode count data","title":"bartools: quickstart guide","text":"Raw barcode count data can thought similarly raw integer-based count data count based experiments RNA-sequencing. data types edgeR package provides efficient DGEList object structure store sample counts associated metadata. bartools makes use object structure store process DNA barcode counts.","code":""},{"path":"https://danevass.github.io/bartools/articles/quickstart.html","id":"an-example-barcoding-experiment","dir":"Articles","previous_headings":"Bartools Quickstart Guide > 1. Importing DNA barcode count data","what":"An example barcoding experiment","title":"bartools: quickstart guide","text":"section make use hypothetical DNA barcoding dataset based recent unpublished data Dawson lab investigating response acute myeloid leukaemia (AML) cells novel class MYST acetyltransferase inhibitor described recently MacPherson et al. Nature 2019. AML cells cultured vitro, barcoded using lentiviral based barcoding library called SPLINTR, transplanted three groups C57BL/6J mice daily dosing MYST inhibitor low high dose corresponding vehicle control. Barcode samples harvested bone marrow moribund mice sequenced technical replicate replicate. follow along vignette raw counts tables sample metadata included bartools package.","code":"data(test.dge)"},{"path":"https://danevass.github.io/bartools/articles/quickstart.html","id":"generating-a-dgelist-object-from-sample-counts-and-metadata","dir":"Articles","previous_headings":"Bartools Quickstart Guide > 1. Importing DNA barcode count data","what":"Generating a DGEList object from sample counts and metadata","title":"bartools: quickstart guide","text":"Counts objects defined can specified sample metadata sheet shown . easiest way generate DGEList object containing count information metadata interest set barcode sequencing samples. example process shown . Load counts specified samplesheet DGEList object results creation DGEList object containing counts metadata information sample","code":"samplesheet <- read.csv(system.file(\"extdata\", \"test_sampletable.csv\", package = \"bartools\", mustWork = T), header = T, stringsAsFactors = F) samplesheet dge <- edgeR::readDGE(files = samplesheet, group = samplesheet$treatment, labels = samplesheet$sample, header = T) data(test.dge) test.dge ## An object of class \"DGEList\" ## $samples ## Sample Experiment Group PCR_Replicate Treatment group ## T0-1 T0-1 test_01 T0 1 T0 T0 ## T0-2 T0-2 test_01 T0 2 T0 T0 ## S10-1 S10-1 test_01 10_High_dose 1 High_dose 10_High_dose ## S10-2 S10-2 test_01 10_High_dose 2 High_dose 10_High_dose ## S11-1 S11-1 test_01 11_Vehicle 1 Vehicle 11_Vehicle ## lib.size norm.factors ## T0-1 3584606 1 ## T0-2 3349340 1 ## S10-1 4114186 1 ## S10-2 4196458 1 ## S11-1 2907500 1 ## 33 more rows ... ## ## $counts ## Samples ## Tags T0-1 T0-2 S10-1 S10-2 S11-1 S11-2 S12-1 S12-2 S13-1 S13-2 S14-1 S14-2 ## BC_1 175 79 0 0 0 0 0 0 0 0 0 0 ## BC_13 1458 834 0 0 0 0 0 0 0 0 0 0 ## BC_99 1155 1554 0 0 0 0 0 0 0 0 0 0 ## BC_120 285 184 0 0 0 0 0 0 0 0 0 0 ## BC_351 0 0 0 0 0 0 0 0 0 0 0 0 ## Samples ## Tags S15-1 S15-2 S16-1 S16-2 S17-1 S17-2 S18-1 S18-2 S1-1 S1-2 S2-1 S2-2 ## BC_1 0 0 0 0 0 0 0 0 0 0 0 0 ## BC_13 0 0 0 0 0 0 0 0 0 0 0 0 ## BC_99 0 0 0 0 0 0 0 0 105 205 0 0 ## BC_120 0 0 0 0 0 0 0 0 0 0 0 0 ## BC_351 0 0 0 0 0 0 0 0 0 0 0 0 ## Samples ## Tags S3-1 S3-2 S4-1 S4-2 S5-1 S5-2 S6-1 S6-2 S7-1 S7-2 S8-1 S8-2 S9-1 S9-2 ## BC_1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## BC_13 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## BC_99 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## BC_120 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## BC_351 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ## 1634 more rows ..."},{"path":[]},{"path":"https://danevass.github.io/bartools/articles/quickstart.html","id":"data-qc","dir":"Articles","previous_headings":"Bartools Quickstart Guide > 2. Data QC and Normalisation","what":"Data QC","title":"bartools: quickstart guide","text":"first want ensure working clean data. remove barcodes counts across samples. normalise samples sequencing depth can plot raw normalised sequencing depth get idea depth discrepancies PCR replicates lentiviral based barcoding experiments, one, common library exhibit degree skewness based cloning method. means barcodes represented library others greater chance transduced multiple cells. experiments assume individual barcode transduced one cell, cell transduced one barcode.ensured using low multiplicity infection (MOI) transduction likelihood cell transduced one barcode containing virions follows Poisson distribution. mind, also can useful check total counts per barcode identify bias counts sample vs. frequency barcode reference library. plot individual barcodes x-axis ordered based frequency library pool. increased number counts per barcode toward left hand side plot suggestive transduction bias, meaning reads average attributed abundant barcodes library. , likely multiple cells transduced barcode. don’t see suggesting problem experiment.","code":"# Remove rows with no data keeprows = rowSums(test.dge$counts) >= 1 dge.filtered <- test.dge[keeprows,] dge.filtered.counts <- dge.filtered$counts dim(dge.filtered.counts) ## [1] 1490 38 # Normalise each sample to counts per million dge.cpmnorm <- normaliseCounts(dge.filtered, method = \"cpm\", threshold = 1) ## Error in normaliseCounts(dge.filtered, method = \"cpm\", threshold = 1): method argument must be one of CPM, RLE, TMM, upperquartile or TMMwsp # raw counts per sample plotReadCounts(dge.filtered.counts, group = dge.filtered$samples$Treatment) # normalised counts per sample plotReadCounts(dge.cpmnorm, group = dge.filtered$samples$Treatment) ## Error in is.data.frame(x): object 'dge.cpmnorm' not found # plot detected barcodes plotBarcodeCounts(dge.cpmnorm, log10 = F) ## Error in is.data.frame(x): object 'dge.cpmnorm' not found plotBarcodeCounts(dge.cpmnorm, log10 = T) ## Error in is.data.frame(x): object 'dge.cpmnorm' not found"},{"path":"https://danevass.github.io/bartools/articles/quickstart.html","id":"check-correlation-between-pcr-replicates","dir":"Articles","previous_headings":"Bartools Quickstart Guide > 2. Data QC and Normalisation","what":"Check correlation between PCR replicates","title":"bartools: quickstart guide","text":"also important ensure individual samples sequenced appropriate depth ensures entire barcode repertoire present sample captured data. Sequencing technical duplicates sample generated library PCR stage good way ensure . experiment 9 samples total, two PCR technical replicates. correlate barcode distributions pair technical replicates. fit linear model technical replicates per sample plot regression line. Note expect high correlation PCR duplicates barcode pool. can easily get correlation values replicates","code":"samps <- unique(test.dge$samples$group) lapply(samps, function(x){ df <- test.dge[,test.dge$samples$group %in% as.character(x)] plotBarcodeRegression(df, samp1 = colnames(df)[[1]], samp2 = colnames(df)[[2]]) }) ## [[1]] ## `geom_smooth()` using formula = 'y ~ x' ## ## [[2]] ## `geom_smooth()` using formula = 'y ~ x' ## ## [[3]] ## `geom_smooth()` using formula = 'y ~ x' ## ## [[4]] ## `geom_smooth()` using formula = 'y ~ x' ## ## [[5]] ## `geom_smooth()` using formula = 'y ~ x' ## ## [[6]] ## `geom_smooth()` using formula = 'y ~ x' ## ## [[7]] ## `geom_smooth()` using formula = 'y ~ x' ## ## [[8]] ## `geom_smooth()` using formula = 'y ~ x' ## ## [[9]] ## `geom_smooth()` using formula = 'y ~ x' ## ## [[10]] ## `geom_smooth()` using formula = 'y ~ x' ## ## [[11]] ## `geom_smooth()` using formula = 'y ~ x' ## ## [[12]] ## `geom_smooth()` using formula = 'y ~ x' ## ## [[13]] ## `geom_smooth()` using formula = 'y ~ x' ## ## [[14]] ## `geom_smooth()` using formula = 'y ~ x' ## ## [[15]] ## `geom_smooth()` using formula = 'y ~ x' ## ## [[16]] ## `geom_smooth()` using formula = 'y ~ x' ## ## [[17]] ## `geom_smooth()` using formula = 'y ~ x' ## ## [[18]] ## `geom_smooth()` using formula = 'y ~ x' ## ## [[19]] ## `geom_smooth()` using formula = 'y ~ x' corrs <- calcReplicateCorr(test.dge, group = test.dge$samples$Group, corr.thresh = 0.9) ## Warning in colnames(dge$samples) == group: longer object length is not a ## multiple of shorter object length ## Error in table(dge$samples[, group.pos]): nothing to tabulate corrs ## Error in eval(expr, envir, enclos): object 'corrs' not found which(corrs < 0.9) ## Error in which(corrs < 0.9): object 'corrs' not found"},{"path":"https://danevass.github.io/bartools/articles/quickstart.html","id":"collapse-pcr-replicates-in-object","dir":"Articles","previous_headings":"Bartools Quickstart Guide > 2. Data QC and Normalisation","what":"Collapse PCR replicates in object","title":"bartools: quickstart guide","text":"Now know samples good quality can merge PCR replicate information. point onward good idea collapse PCR replicates. take average PCR technical replicates within sample result clean barcode sequencing dataset ready investigation visualisation.","code":"dim(dge.filtered) ## [1] 1490 38 dge.filtered.collapsed <- collapseReplicates(dge.filtered, groupby = dge.filtered$samples$Group, by = \"mean\", show_reps = F) dim(dge.filtered.collapsed) ## [1] 1490 19 head(dge.filtered.collapsed) ## An object of class \"DGEList\" ## $samples ## Sample Experiment Group PCR_Replicate Treatment ## 1_High_dose S1-1 test_01 1_High_dose 1 High_dose ## 10_High_dose S10-1 test_01 10_High_dose 1 High_dose ## 11_Vehicle S11-1 test_01 11_Vehicle 1 Vehicle ## 12_Vehicle S12-1 test_01 12_Vehicle 1 Vehicle ## 13_Low_dose S13-1 test_01 13_Low_dose 1 Low_dose ## group lib.size norm.factors ## 1_High_dose 1_High_dose 3447629 1 ## 10_High_dose 10_High_dose 4114186 1 ## 11_Vehicle 11_Vehicle 2907500 1 ## 12_Vehicle 12_Vehicle 4202337 1 ## 13_Low_dose 13_Low_dose 4513559 1 ## 14 more rows ... ## ## $counts ## Samples ## Tags 1_High_dose 10_High_dose 11_Vehicle 12_Vehicle 13_Low_dose 14_Low_dose ## BC_1 0 0 0 0 0 0 ## BC_13 0 0 0 0 0 0 ## BC_99 155 0 0 0 0 0 ## BC_120 0 0 0 0 0 0 ## BC_426 0 0 0 0 0 0 ## BC_430 0 0 0 0 0 0 ## Samples ## Tags 15_Low_dose 16_Low_dose 17_High_dose 18_Vehicle 2_High_dose ## BC_1 0 0 0 0 0 ## BC_13 0 0 0 0 0 ## BC_99 0 0 0 0 0 ## BC_120 0 0 0 0 0 ## BC_426 0 0 0 0 0 ## BC_430 0 0 0 413 0 ## Samples ## Tags 3_High_dose 4_Vehicle 5_Low_dose 6_High_dose 7_Vehicle 8_Vehicle ## BC_1 0 0 0 0 0 0 ## BC_13 0 0 0 0 0 0 ## BC_99 0 0 0 0 0 0 ## BC_120 0 0 0 0 0 0 ## BC_426 0 0 0 0 0 0 ## BC_430 0 13 0 0 0 0 ## Samples ## Tags 9_Low_dose T0 ## BC_1 0 127 ## BC_13 0 1146 ## BC_99 0 1354 ## BC_120 0 234 ## BC_426 0 81 ## BC_430 0 1022"},{"path":"https://danevass.github.io/bartools/articles/quickstart.html","id":"visualisation","dir":"Articles","previous_headings":"Bartools Quickstart Guide","what":"3. Visualisation","title":"bartools: quickstart guide","text":"bartools includes range visualisation options examining barcode-seq datasets.","code":""},{"path":"https://danevass.github.io/bartools/articles/quickstart.html","id":"bubble-plot","dir":"Articles","previous_headings":"Bartools Quickstart Guide > 3. Visualisation","what":"Bubble plot","title":"bartools: quickstart guide","text":"Sometimes visual depiction data suitable. barcodes/tags represented bubbles aligned single plane. size bubbles reflects percentage abundance barcode within sample Bubbleplots can also ordered according particular sample can help visual representation large vs small clones. Barcodes fail meet defined abundance threshold sample can greyed ","code":"plotBarcodeBubble(dge.filtered.collapsed$counts, proportion.cutoff = 10, labels = T) plotOrderedBubble(dge.filtered.collapsed$counts, proportion.cutoff = 10, labels = T, orderSample = \"T0\", colorDominant = T) ## Error in methods::is(counts): object 'counts' not found plotOrderedBubble(dge.filtered.collapsed$counts, proportion.cutoff = 10, labels = T, orderSample = \"T0\", colorDominant = F) ## Error in methods::is(counts): object 'counts' not found"},{"path":"https://danevass.github.io/bartools/articles/quickstart.html","id":"barcode-plot","dir":"Articles","previous_headings":"Bartools Quickstart Guide > 3. Visualisation","what":"Barcode Plot","title":"bartools: quickstart guide","text":"Alternatively, can focus abundant barcodes within set samples easily observe change frequency course experiment.","code":"plotBarcodeHistogram(dge.filtered.collapsed$counts, sample = dge.filtered.collapsed$samples$Group[[1]], top = 50)"},{"path":"https://danevass.github.io/bartools/articles/quickstart.html","id":"timeseries-plots","dir":"Articles","previous_headings":"Bartools Quickstart Guide > 3. Visualisation","what":"Timeseries Plots","title":"bartools: quickstart guide","text":"timecourse experiments useful visualise kinetics barcode diversity changes time. instance can use plotBarcodeTimeseries get idea relative abundance top n barcodes sample relative others.","code":"plotBarcodeTimeseries(dge.filtered.collapsed, top = 5) ## Using barcode as id variables"},{"path":"https://danevass.github.io/bartools/articles/quickstart.html","id":"principal-components-analysis","dir":"Articles","previous_headings":"Bartools Quickstart Guide > 3. Visualisation","what":"Principal Components Analysis","title":"bartools: quickstart guide","text":"global level PCA analysis good way get high level understanding similarities differences samples.","code":"plotBarcodePCA(dge.filtered.collapsed, intgroup = \"Treatment\") plotBarcodePCA(dge.filtered.collapsed[,dge.filtered.collapsed$samples$Treatment %in% c(\"T0\",\"Vehicle\",\"High_dose\")], intgroup = \"Treatment\")"},{"path":"https://danevass.github.io/bartools/articles/quickstart.html","id":"heatmaps","dir":"Articles","previous_headings":"Bartools Quickstart Guide > 3. Visualisation","what":"Heatmaps","title":"bartools: quickstart guide","text":"Another method comparing abundance across samples using heatmap. barcodes ranked among top n abundant within sample indicated asterix. heatmap shows high dose samples generally distinct low dose vehicle group.","code":"plotBarcodeHeatmap(cpm(dge.filtered.collapsed$counts), N = 5)"},{"path":"https://danevass.github.io/bartools/articles/quickstart.html","id":"analysing-composition-and-diversity","dir":"Articles","previous_headings":"Bartools Quickstart Guide","what":"4. Analysing Composition and Diversity","title":"bartools: quickstart guide","text":"important able visualise data also understand relationships barcodes/tags data level","code":""},{"path":"https://danevass.github.io/bartools/articles/quickstart.html","id":"identify-compositional-bias-within-samples","dir":"Articles","previous_headings":"Bartools Quickstart Guide > 4. Analysing Composition and Diversity","what":"Identify compositional bias within samples","title":"bartools: quickstart guide","text":"plots give global visualisation abundance barcode within sample however compositional makeup can obscured visualising data way. can helpful examine fraction barcodes comprise sample. plots calculate cumulative sum sample relation samples defined user.","code":"plotBarcodeCumSum(dge.filtered.collapsed$counts, sample1 = \"T0\", samples = colnames(dge.filtered.collapsed$counts)[1:5])"},{"path":"https://danevass.github.io/bartools/articles/quickstart.html","id":"identifying-abundant-barcodes-within-samples","dir":"Articles","previous_headings":"Bartools Quickstart Guide > 4. Analysing Composition and Diversity","what":"Identifying abundant barcodes within samples","title":"bartools: quickstart guide","text":"important able determine barcodes abundant wihtin sample. Bartools allows easily calculated according abundance threshold. can use specific plots visualise dominance specific barcodes within across samples. plots show top barcodes T0 sample essentially extinguished high dose treatment","code":"top.bc <- getDominantBarcodes(dge.filtered.collapsed, pct.thresh = 5) top.bc ## $`1_High_dose` ## [1] \"BC_79755\" \"BC_102160\" \"BC_59493\" \"BC_23361\" \"BC_53234\" \"BC_400391\" ## ## $`10_High_dose` ## [1] \"BC_8419\" \"BC_124796\" ## ## $`11_Vehicle` ## [1] \"BC_53234\" \"BC_205581\" \"BC_90135\" \"BC_172626\" \"BC_58978\" ## ## $`12_Vehicle` ## [1] \"BC_389078\" \"BC_159570\" \"BC_135438\" \"BC_500780\" \"BC_79755\" ## ## $`13_Low_dose` ## [1] \"BC_8419\" \"BC_124796\" \"BC_102160\" \"BC_257382\" ## ## $`14_Low_dose` ## [1] \"BC_90135\" \"BC_388103\" \"BC_31610\" ## ## $`15_Low_dose` ## [1] \"BC_1478\" \"BC_94579\" \"BC_124796\" \"BC_8419\" ## ## $`16_Low_dose` ## [1] \"BC_53234\" \"BC_79755\" \"BC_140320\" \"BC_31610\" \"BC_248880\" \"BC_1478\" ## ## $`17_High_dose` ## [1] \"BC_183691\" \"BC_90135\" \"BC_400391\" ## ## $`18_Vehicle` ## [1] \"BC_49397\" \"BC_91412\" \"BC_159570\" \"BC_62602\" \"BC_93485\" \"BC_70225\" ## ## $`2_High_dose` ## [1] \"BC_248880\" \"BC_65104\" \"BC_9141\" \"BC_1478\" \"BC_31610\" \"BC_79755\" ## ## $`3_High_dose` ## [1] \"BC_79755\" \"BC_142024\" \"BC_102160\" \"BC_183691\" ## ## $`4_Vehicle` ## [1] \"BC_53234\" \"BC_58978\" \"BC_62602\" \"BC_68847\" \"BC_140320\" \"BC_79755\" ## ## $`5_Low_dose` ## [1] \"BC_561471\" \"BC_118640\" \"BC_59493\" \"BC_1607\" \"BC_31610\" ## ## $`6_High_dose` ## [1] \"BC_79755\" \"BC_8419\" \"BC_124796\" \"BC_4564\" ## ## $`7_Vehicle` ## [1] \"BC_53234\" \"BC_54442\" \"BC_142024\" \"BC_140320\" \"BC_93485\" ## ## $`8_Vehicle` ## [1] \"BC_142024\" \"BC_90135\" \"BC_58978\" \"BC_93485\" ## ## $`9_Low_dose` ## [1] \"BC_79755\" \"BC_4198\" \"BC_81579\" \"BC_118640\" \"BC_560839\" \"BC_345889\" ## [7] \"BC_400391\" ## ## $T0 ## [1] \"BC_245016\" \"BC_121651\" plotBarcodeBoxplot(dge.filtered.collapsed, barcodes = top.bc$T0, condition = c(\"T0\", \"Low_dose\", \"High_dose\")) ## multiple barcodes given. No metadata group"},{"path":"https://danevass.github.io/bartools/articles/quickstart.html","id":"calculating-and-plotting-percentile-abundance-","dir":"Articles","previous_headings":"Bartools Quickstart Guide > 4. Analysing Composition and Diversity","what":"Calculating and plotting percentile abundance.","title":"bartools: quickstart guide","text":"graphs demonstrate relatively barcodes can sometimes comprise majority sample’s clonality, particularly following selective event drug treatment. useful formally analyse based desired percentile threshold. common threshold 95th percentile. can eliminate small barcodes comprise tail dataset give sense many clones truly comprise sample plots show clones comprise majority dataset per mouse. Also, generally fewer clones present high dose group compared vehicle low dose groups.","code":"calcPercentileBarcodes(dge.filtered.collapsed, percentile = 0.95) ## $NumBarcodes ## Sample NumBarcodes ## 1 1_High_dose 15 ## 2 10_High_dose 3 ## 3 11_Vehicle 19 ## 4 12_Vehicle 14 ## 5 13_Low_dose 11 ## 6 14_Low_dose 6 ## 7 15_Low_dose 7 ## 8 16_Low_dose 19 ## 9 17_High_dose 5 ## 10 18_Vehicle 27 ## 11 2_High_dose 12 ## 12 3_High_dose 10 ## 13 4_Vehicle 25 ## 14 5_Low_dose 11 ## 15 6_High_dose 9 ## 16 7_Vehicle 16 ## 17 8_Vehicle 21 ## 18 9_Low_dose 19 ## 19 T0 596 ## ## $TopBarcodes ## $TopBarcodes$`1_High_dose` ## [1] \"BC_79755\" \"BC_102160\" \"BC_59493\" \"BC_23361\" \"BC_53234\" \"BC_400391\" ## [7] \"BC_142112\" \"BC_94511\" \"BC_68847\" \"BC_142024\" \"BC_23241\" \"BC_47310\" ## [13] \"BC_9141\" \"BC_5472\" \"BC_65104\" ## ## $TopBarcodes$`10_High_dose` ## [1] \"BC_8419\" \"BC_124796\" \"BC_102160\" ## ## $TopBarcodes$`11_Vehicle` ## [1] \"BC_53234\" \"BC_205581\" \"BC_90135\" \"BC_172626\" \"BC_58978\" \"BC_79755\" ## [7] \"BC_159570\" \"BC_118640\" \"BC_31610\" \"BC_635211\" \"BC_257382\" \"BC_324711\" ## [13] \"BC_1607\" \"BC_4531\" \"BC_327422\" \"BC_297704\" \"BC_194823\" \"BC_415460\" ## [19] \"BC_140320\" ## ## $TopBarcodes$`12_Vehicle` ## [1] \"BC_389078\" \"BC_159570\" \"BC_135438\" \"BC_500780\" \"BC_79755\" \"BC_31610\" ## [7] \"BC_49629\" \"BC_118640\" \"BC_47726\" \"BC_78349\" \"BC_58978\" \"BC_245016\" ## [13] \"BC_1607\" \"BC_329679\" ## ## $TopBarcodes$`13_Low_dose` ## [1] \"BC_8419\" \"BC_124796\" \"BC_102160\" \"BC_257382\" \"BC_172626\" \"BC_345889\" ## [7] \"BC_53234\" \"BC_5472\" \"BC_79755\" \"BC_205581\" \"BC_17608\" ## ## $TopBarcodes$`14_Low_dose` ## [1] \"BC_90135\" \"BC_388103\" \"BC_31610\" \"BC_58978\" \"BC_292962\" \"BC_159570\" ## ## $TopBarcodes$`15_Low_dose` ## [1] \"BC_1478\" \"BC_94579\" \"BC_124796\" \"BC_8419\" \"BC_316204\" \"BC_400391\" ## [7] \"BC_23241\" ## ## $TopBarcodes$`16_Low_dose` ## [1] \"BC_53234\" \"BC_79755\" \"BC_140320\" \"BC_31610\" \"BC_248880\" \"BC_1478\" ## [7] \"BC_17608\" \"BC_257382\" \"BC_345889\" \"BC_94511\" \"BC_142112\" \"BC_142024\" ## [13] \"BC_91412\" \"BC_58978\" \"BC_190202\" \"BC_4198\" \"BC_68847\" \"BC_389078\" ## [19] \"BC_47726\" ## ## $TopBarcodes$`17_High_dose` ## [1] \"BC_183691\" \"BC_90135\" \"BC_400391\" \"BC_31610\" \"BC_68847\" ## ## $TopBarcodes$`18_Vehicle` ## [1] \"BC_49397\" \"BC_91412\" \"BC_159570\" \"BC_62602\" \"BC_93485\" \"BC_70225\" ## [7] \"BC_79755\" \"BC_4564\" \"BC_124796\" \"BC_58978\" \"BC_8419\" \"BC_149935\" ## [13] \"BC_68847\" \"BC_90135\" \"BC_4173\" \"BC_4469\" \"BC_172626\" \"BC_31610\" ## [19] \"BC_383329\" \"BC_55960\" \"BC_62970\" \"BC_102160\" \"BC_244320\" \"BC_400391\" ## [25] \"BC_135438\" \"BC_203989\" \"BC_133178\" ## ## $TopBarcodes$`2_High_dose` ## [1] \"BC_248880\" \"BC_65104\" \"BC_9141\" \"BC_1478\" \"BC_31610\" \"BC_79755\" ## [7] \"BC_388103\" \"BC_257382\" \"BC_68618\" \"BC_102160\" \"BC_4531\" \"BC_90135\" ## ## $TopBarcodes$`3_High_dose` ## [1] \"BC_79755\" \"BC_142024\" \"BC_102160\" \"BC_183691\" \"BC_59493\" \"BC_345889\" ## [7] \"BC_23361\" \"BC_70225\" \"BC_30946\" \"BC_159570\" ## ## $TopBarcodes$`4_Vehicle` ## [1] \"BC_53234\" \"BC_58978\" \"BC_62602\" \"BC_68847\" \"BC_140320\" \"BC_79755\" ## [7] \"BC_327422\" \"BC_36103\" \"BC_102160\" \"BC_81579\" \"BC_118640\" \"BC_135438\" ## [13] \"BC_205581\" \"BC_1607\" \"BC_4564\" \"BC_91412\" \"BC_229324\" \"BC_560839\" ## [19] \"BC_159570\" \"BC_468335\" \"BC_49397\" \"BC_203989\" \"BC_124796\" \"BC_8419\" ## [25] \"BC_190202\" ## ## $TopBarcodes$`5_Low_dose` ## [1] \"BC_561471\" \"BC_118640\" \"BC_59493\" \"BC_1607\" \"BC_31610\" \"BC_23361\" ## [7] \"BC_159570\" \"BC_93485\" \"BC_468335\" \"BC_81579\" \"BC_257382\" ## ## $TopBarcodes$`6_High_dose` ## [1] \"BC_79755\" \"BC_8419\" \"BC_124796\" \"BC_4564\" \"BC_400391\" \"BC_31610\" ## [7] \"BC_1478\" \"BC_90135\" \"BC_388103\" ## ## $TopBarcodes$`7_Vehicle` ## [1] \"BC_53234\" \"BC_54442\" \"BC_142024\" \"BC_140320\" \"BC_93485\" \"BC_124796\" ## [7] \"BC_23544\" \"BC_8419\" \"BC_324711\" \"BC_159570\" \"BC_58978\" \"BC_90135\" ## [13] \"BC_345889\" \"BC_4564\" \"BC_102160\" \"BC_400391\" ## ## $TopBarcodes$`8_Vehicle` ## [1] \"BC_142024\" \"BC_90135\" \"BC_58978\" \"BC_93485\" \"BC_363879\" \"BC_4531\" ## [7] \"BC_205581\" \"BC_118640\" \"BC_79755\" \"BC_31610\" \"BC_1607\" \"BC_82758\" ## [13] \"BC_194823\" \"BC_389078\" \"BC_251167\" \"BC_68847\" \"BC_77209\" \"BC_102160\" ## [19] \"BC_4173\" \"BC_4564\" \"BC_94579\" ## ## $TopBarcodes$`9_Low_dose` ## [1] \"BC_79755\" \"BC_4198\" \"BC_81579\" \"BC_118640\" \"BC_560839\" \"BC_345889\" ## [7] \"BC_400391\" \"BC_1607\" \"BC_319105\" \"BC_35704\" \"BC_205581\" \"BC_90135\" ## [13] \"BC_124796\" \"BC_142024\" \"BC_8419\" \"BC_561471\" \"BC_19146\" \"BC_68847\" ## [19] \"BC_78912\" ## ## $TopBarcodes$T0 ## [1] \"BC_245016\" \"BC_121651\" \"BC_420967\" \"BC_108059\" \"BC_323670\" \"BC_9141\" ## [7] \"BC_331193\" \"BC_119418\" \"BC_65104\" \"BC_17570\" \"BC_35704\" \"BC_92942\" ## [13] \"BC_383329\" \"BC_1396\" \"BC_83680\" \"BC_24510\" \"BC_55858\" \"BC_38589\" ## [19] \"BC_137495\" \"BC_446739\" \"BC_69627\" \"BC_313129\" \"BC_171385\" \"BC_280320\" ## [25] \"BC_12367\" \"BC_221975\" \"BC_15782\" \"BC_310257\" \"BC_3618\" \"BC_67201\" ## [31] \"BC_107789\" \"BC_36103\" \"BC_2596\" \"BC_215831\" \"BC_43535\" \"BC_69641\" ## [37] \"BC_13611\" \"BC_16618\" \"BC_588849\" \"BC_294188\" \"BC_22306\" \"BC_141060\" ## [43] \"BC_249402\" \"BC_428803\" \"BC_344050\" \"BC_25084\" \"BC_46538\" \"BC_175651\" ## [49] \"BC_206193\" \"BC_205581\" \"BC_403084\" \"BC_56819\" \"BC_400391\" \"BC_16956\" ## [55] \"BC_219379\" \"BC_19146\" \"BC_172626\" \"BC_18278\" \"BC_43128\" \"BC_47439\" ## [61] \"BC_148563\" \"BC_114027\" \"BC_325065\" \"BC_62602\" \"BC_58978\" \"BC_252374\" ## [67] \"BC_68847\" \"BC_262618\" \"BC_111272\" \"BC_224300\" \"BC_476380\" \"BC_289383\" ## [73] \"BC_50136\" \"BC_202891\" \"BC_276866\" \"BC_521149\" \"BC_21018\" \"BC_550404\" ## [79] \"BC_162898\" \"BC_255436\" \"BC_458543\" \"BC_168818\" \"BC_176946\" \"BC_425129\" ## [85] \"BC_204266\" \"BC_103528\" \"BC_175871\" \"BC_83707\" \"BC_64584\" \"BC_105387\" ## [91] \"BC_11643\" \"BC_34617\" \"BC_227720\" \"BC_72619\" \"BC_516753\" \"BC_168530\" ## [97] \"BC_2040\" \"BC_556149\" \"BC_376665\" \"BC_18345\" \"BC_249161\" \"BC_345717\" ## [103] \"BC_114040\" \"BC_24159\" \"BC_41692\" \"BC_248159\" \"BC_236596\" \"BC_166467\" ## [109] \"BC_54442\" \"BC_206263\" \"BC_363714\" \"BC_56338\" \"BC_16754\" \"BC_30946\" ## [115] \"BC_503475\" \"BC_11277\" \"BC_612467\" \"BC_67854\" \"BC_43673\" \"BC_160057\" ## [121] \"BC_32325\" \"BC_7405\" \"BC_118831\" \"BC_418545\" \"BC_120596\" \"BC_5256\" ## [127] \"BC_39281\" \"BC_258799\" \"BC_29284\" \"BC_85441\" \"BC_103714\" \"BC_37062\" ## [133] \"BC_140320\" \"BC_20263\" \"BC_46524\" \"BC_321677\" \"BC_172412\" \"BC_139928\" ## [139] \"BC_203520\" \"BC_105732\" \"BC_173736\" \"BC_13395\" \"BC_11391\" \"BC_142999\" ## [145] \"BC_172238\" \"BC_79026\" \"BC_43123\" \"BC_128266\" \"BC_355420\" \"BC_415373\" ## [151] \"BC_129519\" \"BC_3733\" \"BC_93485\" \"BC_40856\" \"BC_42612\" \"BC_289568\" ## [157] \"BC_10454\" \"BC_38398\" \"BC_160158\" \"BC_32680\" \"BC_135438\" \"BC_346124\" ## [163] \"BC_146666\" \"BC_42178\" \"BC_53234\" \"BC_356531\" \"BC_389078\" \"BC_635211\" ## [169] \"BC_183401\" \"BC_132225\" \"BC_229995\" \"BC_102205\" \"BC_47726\" \"BC_564039\" ## [175] \"BC_40516\" \"BC_45955\" \"BC_113839\" \"BC_102803\" \"BC_42310\" \"BC_8835\" ## [181] \"BC_91412\" \"BC_15740\" \"BC_173674\" \"BC_44599\" \"BC_324711\" \"BC_519985\" ## [187] \"BC_77030\" \"BC_350464\" \"BC_235128\" \"BC_151544\" \"BC_33699\" \"BC_474982\" ## [193] \"BC_210529\" \"BC_217211\" \"BC_221056\" \"BC_187025\" \"BC_320233\" \"BC_7122\" ## [199] \"BC_87047\" \"BC_76947\" \"BC_63846\" \"BC_560839\" \"BC_65130\" \"BC_2294\" ## [205] \"BC_102160\" \"BC_273124\" \"BC_161563\" \"BC_389742\" \"BC_521485\" \"BC_145719\" ## [211] \"BC_373901\" \"BC_162119\" \"BC_189813\" \"BC_17608\" \"BC_324953\" \"BC_320752\" ## [217] \"BC_283335\" \"BC_67965\" \"BC_2865\" \"BC_242383\" \"BC_227308\" \"BC_346627\" ## [223] \"BC_67233\" \"BC_672202\" \"BC_8324\" \"BC_211828\" \"BC_410172\" \"BC_495859\" ## [229] \"BC_35659\" \"BC_199881\" \"BC_44600\" \"BC_377226\" \"BC_147006\" \"BC_41523\" ## [235] \"BC_336794\" \"BC_26769\" \"BC_244320\" \"BC_183112\" \"BC_14377\" \"BC_7299\" ## [241] \"BC_53380\" \"BC_10347\" \"BC_164378\" \"BC_806\" \"BC_20557\" \"BC_57621\" ## [247] \"BC_389087\" \"BC_100344\" \"BC_408928\" \"BC_156353\" \"BC_154990\" \"BC_228994\" ## [253] \"BC_33764\" \"BC_23522\" \"BC_4564\" \"BC_78349\" \"BC_10808\" \"BC_201293\" ## [259] \"BC_81309\" \"BC_45652\" \"BC_123791\" \"BC_214604\" \"BC_2206\" \"BC_14492\" ## [265] \"BC_146146\" \"BC_6093\" \"BC_421415\" \"BC_58144\" \"BC_167638\" \"BC_366637\" ## [271] \"BC_207651\" \"BC_154519\" \"BC_4733\" \"BC_19353\" \"BC_326596\" \"BC_1764\" ## [277] \"BC_86358\" \"BC_101581\" \"BC_23241\" \"BC_138900\" \"BC_11270\" \"BC_64945\" ## [283] \"BC_37363\" \"BC_638624\" \"BC_195246\" \"BC_121663\" \"BC_672021\" \"BC_194823\" ## [289] \"BC_206798\" \"BC_521264\" \"BC_12191\" \"BC_130431\" \"BC_462254\" \"BC_4531\" ## [295] \"BC_22999\" \"BC_64931\" \"BC_214878\" \"BC_9340\" \"BC_350335\" \"BC_150291\" ## [301] \"BC_196282\" \"BC_95934\" \"BC_211811\" \"BC_587139\" \"BC_199093\" \"BC_147380\" ## [307] \"BC_228018\" \"BC_116921\" \"BC_514247\" \"BC_380559\" \"BC_141246\" \"BC_218093\" ## [313] \"BC_88464\" \"BC_157906\" \"BC_145403\" \"BC_73098\" \"BC_61489\" \"BC_77101\" ## [319] \"BC_266451\" \"BC_138737\" \"BC_201372\" \"BC_654016\" \"BC_444573\" \"BC_53219\" ## [325] \"BC_57692\" \"BC_175957\" \"BC_12418\" \"BC_357846\" \"BC_14810\" \"BC_49226\" ## [331] \"BC_175198\" \"BC_49044\" \"BC_539228\" \"BC_527469\" \"BC_181957\" \"BC_181554\" ## [337] \"BC_91989\" \"BC_353112\" \"BC_130861\" \"BC_55675\" \"BC_10293\" \"BC_617554\" ## [343] \"BC_346761\" \"BC_302238\" \"BC_426662\" \"BC_77187\" \"BC_19610\" \"BC_171953\" ## [349] \"BC_217320\" \"BC_446649\" \"BC_29208\" \"BC_93884\" \"BC_20573\" \"BC_146322\" ## [355] \"BC_221691\" \"BC_261055\" \"BC_3583\" \"BC_9626\" \"BC_6304\" \"BC_354350\" ## [361] \"BC_13093\" \"BC_203989\" \"BC_114257\" \"BC_234390\" \"BC_249649\" \"BC_88099\" ## [367] \"BC_322542\" \"BC_351660\" \"BC_52081\" \"BC_225019\" \"BC_596629\" \"BC_15448\" ## [373] \"BC_528062\" \"BC_2430\" \"BC_324042\" \"BC_587343\" \"BC_67374\" \"BC_14484\" ## [379] \"BC_83816\" \"BC_141114\" \"BC_318282\" \"BC_11055\" \"BC_45402\" \"BC_79755\" ## [385] \"BC_357977\" \"BC_415903\" \"BC_115436\" \"BC_43206\" \"BC_50222\" \"BC_655329\" ## [391] \"BC_147556\" \"BC_182320\" \"BC_31779\" \"BC_9322\" \"BC_162322\" \"BC_296283\" ## [397] \"BC_381245\" \"BC_283541\" \"BC_67247\" \"BC_4198\" \"BC_31196\" \"BC_11152\" ## [403] \"BC_61500\" \"BC_90135\" \"BC_47310\" \"BC_292962\" \"BC_82595\" \"BC_26258\" ## [409] \"BC_99\" \"BC_94579\" \"BC_35283\" \"BC_238577\" \"BC_49397\" \"BC_363879\" ## [415] \"BC_309303\" \"BC_12410\" \"BC_231962\" \"BC_7691\" \"BC_147063\" \"BC_302454\" ## [421] \"BC_92584\" \"BC_31185\" \"BC_110945\" \"BC_144061\" \"BC_75688\" \"BC_70909\" ## [427] \"BC_238160\" \"BC_491991\" \"BC_149347\" \"BC_195887\" \"BC_125634\" \"BC_1593\" ## [433] \"BC_57794\" \"BC_207333\" \"BC_228333\" \"BC_316483\" \"BC_169627\" \"BC_52856\" ## [439] \"BC_129559\" \"BC_67370\" \"BC_21535\" \"BC_515934\" \"BC_329007\" \"BC_275321\" ## [445] \"BC_444761\" \"BC_13\" \"BC_516611\" \"BC_154696\" \"BC_129768\" \"BC_159570\" ## [451] \"BC_103862\" \"BC_268745\" \"BC_9747\" \"BC_194092\" \"BC_2791\" \"BC_176231\" ## [457] \"BC_57582\" \"BC_50311\" \"BC_48168\" \"BC_330625\" \"BC_413271\" \"BC_322219\" ## [463] \"BC_179919\" \"BC_56489\" \"BC_257382\" \"BC_120505\" \"BC_472019\" \"BC_282868\" ## [469] \"BC_164652\" \"BC_124624\" \"BC_355439\" \"BC_563624\" \"BC_430\" \"BC_15707\" ## [475] \"BC_1977\" \"BC_17509\" \"BC_345889\" \"BC_473006\" \"BC_567519\" \"BC_26384\" ## [481] \"BC_28938\" \"BC_245600\" \"BC_145103\" \"BC_81579\" \"BC_10190\" \"BC_248582\" ## [487] \"BC_142112\" \"BC_576338\" \"BC_288976\" \"BC_90599\" \"BC_255928\" \"BC_454109\" ## [493] \"BC_220827\" \"BC_35633\" \"BC_33035\" \"BC_62970\" \"BC_381050\" \"BC_14452\" ## [499] \"BC_189854\" \"BC_329679\" \"BC_66948\" \"BC_82758\" \"BC_84350\" \"BC_228451\" ## [505] \"BC_186267\" \"BC_257523\" \"BC_20281\" \"BC_170233\" \"BC_15652\" \"BC_41280\" ## [511] \"BC_276301\" \"BC_25609\" \"BC_182092\" \"BC_186401\" \"BC_37116\" \"BC_14380\" ## [517] \"BC_259621\" \"BC_171345\" \"BC_278096\" \"BC_567514\" \"BC_44350\" \"BC_131019\" ## [523] \"BC_297704\" \"BC_165975\" \"BC_451991\" \"BC_370968\" \"BC_80020\" \"BC_212837\" ## [529] \"BC_4173\" \"BC_97874\" \"BC_2762\" \"BC_143062\" \"BC_35459\" \"BC_115038\" ## [535] \"BC_6453\" \"BC_163063\" \"BC_28077\" \"BC_23918\" \"BC_389378\" \"BC_222891\" ## [541] \"BC_250471\" \"BC_56792\" \"BC_229036\" \"BC_170407\" \"BC_154806\" \"BC_38465\" ## [547] \"BC_337697\" \"BC_230643\" \"BC_164579\" \"BC_146760\" \"BC_59243\" \"BC_557872\" ## [553] \"BC_652798\" \"BC_127999\" \"BC_4061\" \"BC_22263\" \"BC_22981\" \"BC_66747\" ## [559] \"BC_8838\" \"BC_23213\" \"BC_49060\" \"BC_29053\" \"BC_535770\" \"BC_345175\" ## [565] \"BC_7076\" \"BC_29984\" \"BC_153858\" \"BC_5615\" \"BC_26097\" \"BC_147873\" ## [571] \"BC_19557\" \"BC_19612\" \"BC_14131\" \"BC_100070\" \"BC_190202\" \"BC_125776\" ## [577] \"BC_388103\" \"BC_29222\" \"BC_509872\" \"BC_561471\" \"BC_57889\" \"BC_168163\" ## [583] \"BC_18218\" \"BC_294271\" \"BC_65912\" \"BC_4165\" \"BC_345888\" \"BC_8159\" ## [589] \"BC_1534\" \"BC_15172\" \"BC_78760\" \"BC_23363\" \"BC_427925\" \"BC_65621\" ## [595] \"BC_41997\" \"BC_109658\" ## ## ## $TopBarcodeCounts ## $TopBarcodeCounts$`1_High_dose` ## 1_High_dose ## BC_79755 1107678 ## BC_102160 761271 ## BC_59493 435329 ## BC_23361 232003 ## BC_53234 222954 ## BC_400391 192477 ## BC_142112 187155 ## BC_94511 128591 ## BC_68847 62744 ## BC_142024 61392 ## BC_23241 49611 ## BC_47310 34916 ## BC_9141 34887 ## BC_5472 32110 ## BC_65104 31160 ## ## $TopBarcodeCounts$`10_High_dose` ## 10_High_dose ## BC_8419 1993924 ## BC_124796 1680405 ## BC_102160 206154 ## ## $TopBarcodeCounts$`11_Vehicle` ## 11_Vehicle ## BC_53234 676539 ## BC_205581 486994 ## BC_90135 325817 ## BC_172626 241604 ## BC_58978 182366 ## BC_79755 111942 ## BC_159570 105098 ## BC_118640 65182 ## BC_31610 63582 ## BC_635211 63026 ## BC_257382 60037 ## BC_324711 55954 ## BC_1607 55929 ## BC_4531 54568 ## BC_327422 32704 ## BC_297704 25038 ## BC_194823 24130 ## BC_415460 20811 ## BC_140320 17939 ## ## $TopBarcodeCounts$`12_Vehicle` ## 12_Vehicle ## BC_389078 1405728 ## BC_159570 586842 ## BC_135438 260286 ## BC_500780 240675 ## BC_79755 227341 ## BC_31610 178619 ## BC_49629 137913 ## BC_118640 126942 ## BC_47726 123364 ## BC_78349 122342 ## BC_58978 106717 ## BC_245016 103055 ## BC_1607 101604 ## BC_329679 76624 ## ## $TopBarcodeCounts$`13_Low_dose` ## 13_Low_dose ## BC_8419 1104437 ## BC_124796 1074874 ## BC_102160 790394 ## BC_257382 746244 ## BC_172626 109387 ## BC_345889 81047 ## BC_53234 69197 ## BC_5472 53875 ## BC_79755 53474 ## BC_205581 40089 ## BC_17608 39223 ## ## $TopBarcodeCounts$`14_Low_dose` ## 14_Low_dose ## BC_90135 1914931 ## BC_388103 1415098 ## BC_31610 305126 ## BC_58978 173933 ## BC_292962 126389 ## BC_159570 79752 ## ## $TopBarcodeCounts$`15_Low_dose` ## 15_Low_dose ## BC_1478 1163226 ## BC_94579 953114 ## BC_124796 492547 ## BC_8419 483327 ## BC_316204 95554 ## BC_400391 64143 ## BC_23241 43129 ## ## $TopBarcodeCounts$`16_Low_dose` ## 16_Low_dose ## BC_53234 961560 ## BC_79755 689442 ## BC_140320 613980 ## BC_31610 353626 ## BC_248880 304109 ## BC_1478 286142 ## BC_17608 217539 ## BC_257382 191984 ## BC_345889 189430 ## BC_94511 141895 ## BC_142112 139280 ## BC_142024 111530 ## BC_91412 96804 ## BC_58978 76756 ## BC_190202 66260 ## BC_4198 51301 ## BC_68847 50082 ## BC_389078 40573 ## BC_47726 37815 ## ## $TopBarcodeCounts$`17_High_dose` ## 17_High_dose ## BC_183691 2717363 ## BC_90135 1592156 ## BC_400391 596410 ## BC_31610 211787 ## BC_68847 152083 ## ## $TopBarcodeCounts$`18_Vehicle` ## 18_Vehicle ## BC_49397 790751 ## BC_91412 431176 ## BC_159570 426867 ## BC_62602 276110 ## BC_93485 261381 ## BC_70225 254975 ## BC_79755 209422 ## BC_4564 153377 ## BC_124796 138207 ## BC_58978 129417 ## BC_8419 129121 ## BC_149935 116170 ## BC_68847 115439 ## BC_90135 110019 ## BC_4173 104414 ## BC_4469 100734 ## BC_172626 95167 ## BC_31610 91203 ## BC_383329 79083 ## BC_55960 77254 ## BC_62970 60999 ## BC_102160 54950 ## BC_244320 41598 ## BC_400391 37075 ## BC_135438 32576 ## BC_203989 23509 ## BC_133178 20108 ## ## $TopBarcodeCounts$`2_High_dose` ## 2_High_dose ## BC_248880 1596729 ## BC_65104 1180121 ## BC_9141 1139782 ## BC_1478 706185 ## BC_31610 644411 ## BC_79755 385647 ## BC_388103 175121 ## BC_257382 160815 ## BC_68618 87664 ## BC_102160 62632 ## BC_4531 43062 ## BC_90135 35085 ## ## $TopBarcodeCounts$`3_High_dose` ## 3_High_dose ## BC_79755 2325114 ## BC_142024 631337 ## BC_102160 500221 ## BC_183691 230313 ## BC_59493 179949 ## BC_345889 98351 ## BC_23361 87369 ## BC_70225 61458 ## BC_30946 55306 ## BC_159570 53497 ## ## $TopBarcodeCounts$`4_Vehicle` ## 4_Vehicle ## BC_53234 600113 ## BC_58978 283022 ## BC_62602 231169 ## BC_68847 191550 ## BC_140320 184096 ## BC_79755 173832 ## BC_327422 164339 ## BC_36103 150193 ## BC_102160 122425 ## BC_81579 118058 ## BC_118640 110948 ## BC_135438 95015 ## BC_205581 82397 ## BC_1607 78418 ## BC_4564 76796 ## BC_91412 75495 ## BC_229324 51708 ## BC_560839 51599 ## BC_159570 51597 ## BC_468335 50798 ## BC_49397 41726 ## BC_203989 38296 ## BC_124796 31384 ## BC_8419 26856 ## BC_190202 22301 ## ## $TopBarcodeCounts$`5_Low_dose` ## 5_Low_dose ## BC_561471 2049491 ## BC_118640 306629 ## BC_59493 257272 ## BC_1607 242144 ## BC_31610 176241 ## BC_23361 128368 ## BC_159570 45318 ## BC_93485 30275 ## BC_468335 25238 ## BC_81579 24984 ## BC_257382 24269 ## ## $TopBarcodeCounts$`6_High_dose` ## 6_High_dose ## BC_79755 1313507 ## BC_8419 1120457 ## BC_124796 1013931 ## BC_4564 461928 ## BC_400391 93601 ## BC_31610 43251 ## BC_1478 33933 ## BC_90135 32480 ## BC_388103 29467 ## ## $TopBarcodeCounts$`7_Vehicle` ## 7_Vehicle ## BC_53234 903928 ## BC_54442 695015 ## BC_142024 453623 ## BC_140320 340812 ## BC_93485 313117 ## BC_124796 187546 ## BC_23544 176555 ## BC_8419 171530 ## BC_324711 157126 ## BC_159570 110265 ## BC_58978 96686 ## BC_90135 88718 ## BC_345889 57693 ## BC_4564 56267 ## BC_102160 50723 ## BC_400391 40763 ## ## $TopBarcodeCounts$`8_Vehicle` ## 8_Vehicle ## BC_142024 1166326 ## BC_90135 662506 ## BC_58978 308475 ## BC_93485 241349 ## BC_363879 186351 ## BC_4531 138174 ## BC_205581 128351 ## BC_118640 125295 ## BC_79755 112885 ## BC_31610 90763 ## BC_1607 89905 ## BC_82758 70527 ## BC_194823 48711 ## BC_389078 45367 ## BC_251167 45299 ## BC_68847 41855 ## BC_77209 39896 ## BC_102160 31578 ## BC_4173 25818 ## BC_4564 22920 ## BC_94579 20012 ## ## $TopBarcodeCounts$`9_Low_dose` ## 9_Low_dose ## BC_79755 681795 ## BC_4198 416270 ## BC_81579 227482 ## BC_118640 194256 ## BC_560839 189001 ## BC_345889 175817 ## BC_400391 171811 ## BC_1607 128762 ## BC_319105 107294 ## BC_35704 99211 ## BC_205581 92979 ## BC_90135 56017 ## BC_124796 52960 ## BC_142024 44150 ## BC_8419 41231 ## BC_561471 35822 ## BC_19146 29471 ## BC_68847 28059 ## BC_78912 19369 ## ## $TopBarcodeCounts$T0 ## T0 ## BC_245016 229791 ## BC_121651 217035 ## BC_420967 135919 ## BC_108059 104092 ## BC_323670 88710 ## BC_9141 71948 ## BC_331193 58746 ## BC_119418 50353 ## BC_65104 44928 ## BC_17570 44319 ## BC_35704 42046 ## BC_92942 40055 ## BC_383329 38560 ## BC_1396 36561 ## BC_83680 36463 ## BC_24510 35588 ## BC_55858 32109 ## BC_38589 30544 ## BC_137495 27922 ## BC_446739 26850 ## BC_69627 26818 ## BC_313129 23810 ## BC_171385 23135 ## BC_280320 22617 ## BC_12367 22481 ## BC_221975 21723 ## BC_15782 21679 ## BC_310257 19781 ## BC_3618 17819 ## BC_67201 17468 ## BC_107789 17251 ## BC_36103 15445 ## BC_2596 15022 ## BC_215831 14165 ## BC_43535 13541 ## BC_69641 13517 ## BC_13611 13429 ## BC_16618 13410 ## BC_588849 13050 ## BC_294188 12892 ## BC_22306 12639 ## BC_141060 12371 ## BC_249402 12341 ## BC_428803 11779 ## BC_344050 11654 ## BC_25084 11583 ## BC_46538 11346 ## BC_175651 11293 ## BC_206193 11236 ## BC_205581 10974 ## BC_403084 10962 ## BC_56819 10940 ## BC_400391 10850 ## BC_16956 10763 ## BC_219379 10737 ## BC_19146 10234 ## BC_172626 9948 ## BC_18278 9561 ## BC_43128 9537 ## BC_47439 9501 ## BC_148563 9476 ## BC_114027 9237 ## BC_325065 9181 ## BC_62602 9167 ## BC_58978 9000 ## BC_252374 8738 ## BC_68847 8599 ## BC_262618 8455 ## BC_111272 8364 ## BC_224300 8275 ## BC_476380 8220 ## BC_289383 8174 ## BC_50136 8154 ## BC_202891 8124 ## BC_276866 8112 ## BC_521149 8104 ## BC_21018 7902 ## BC_550404 7724 ## BC_162898 7650 ## BC_255436 7647 ## BC_458543 7621 ## BC_168818 7611 ## BC_176946 7577 ## BC_425129 7376 ## BC_204266 7372 ## BC_103528 7202 ## BC_175871 7145 ## BC_83707 7076 ## BC_64584 7057 ## BC_105387 6847 ## BC_11643 6519 ## BC_34617 6449 ## BC_227720 6426 ## BC_72619 6404 ## BC_516753 6327 ## BC_168530 6204 ## BC_2040 6176 ## BC_556149 6128 ## BC_376665 6061 ## BC_18345 5987 ## BC_249161 5846 ## BC_345717 5778 ## BC_114040 5767 ## BC_24159 5744 ## BC_41692 5676 ## BC_248159 5671 ## BC_236596 5595 ## BC_166467 5526 ## BC_54442 5514 ## BC_206263 5499 ## BC_363714 5403 ## BC_56338 5073 ## BC_16754 5025 ## BC_30946 5005 ## BC_503475 4938 ## BC_11277 4875 ## BC_612467 4863 ## BC_67854 4809 ## BC_43673 4805 ## BC_160057 4794 ## BC_32325 4782 ## BC_7405 4719 ## BC_118831 4679 ## BC_418545 4661 ## BC_120596 4648 ## BC_5256 4643 ## BC_39281 4628 ## BC_258799 4591 ## BC_29284 4553 ## BC_85441 4534 ## BC_103714 4532 ## BC_37062 4505 ## BC_140320 4505 ## BC_20263 4464 ## BC_46524 4425 ## BC_321677 4409 ## BC_172412 4394 ## BC_139928 4370 ## BC_203520 4366 ## BC_105732 4315 ## BC_173736 4310 ## BC_13395 4244 ## BC_11391 4182 ## BC_142999 4102 ## BC_172238 4071 ## BC_79026 4055 ## BC_43123 4027 ## BC_128266 4010 ## BC_355420 3989 ## BC_415373 3989 ## BC_129519 3975 ## BC_3733 3965 ## BC_93485 3963 ## BC_40856 3941 ## BC_42612 3934 ## BC_289568 3933 ## BC_10454 3890 ## BC_38398 3876 ## BC_160158 3828 ## BC_32680 3822 ## BC_135438 3785 ## BC_346124 3761 ## BC_146666 3735 ## BC_42178 3703 ## BC_53234 3698 ## BC_356531 3677 ## BC_389078 3674 ## BC_635211 3668 ## BC_183401 3652 ## BC_132225 3650 ## BC_229995 3579 ## BC_102205 3564 ## BC_47726 3537 ## BC_564039 3502 ## BC_40516 3499 ## BC_45955 3495 ## BC_113839 3469 ## BC_102803 3468 ## BC_42310 3435 ## BC_8835 3425 ## BC_91412 3394 ## BC_15740 3388 ## BC_173674 3386 ## BC_44599 3373 ## BC_324711 3368 ## BC_519985 3294 ## BC_77030 3285 ## BC_350464 3253 ## BC_235128 3226 ## BC_151544 3214 ## BC_33699 3181 ## BC_474982 3168 ## BC_210529 3162 ## BC_217211 3130 ## BC_221056 3130 ## BC_187025 3128 ## BC_320233 3119 ## BC_7122 3112 ## BC_87047 3102 ## BC_76947 3053 ## BC_63846 3009 ## BC_560839 2990 ## BC_65130 2989 ## BC_2294 2986 ## BC_102160 2954 ## BC_273124 2945 ## BC_161563 2940 ## BC_389742 2920 ## BC_521485 2920 ## BC_145719 2911 ## BC_373901 2905 ## BC_162119 2891 ## BC_189813 2890 ## BC_17608 2878 ## BC_324953 2871 ## BC_320752 2863 ## BC_283335 2856 ## BC_67965 2840 ## BC_2865 2822 ## BC_242383 2817 ## BC_227308 2815 ## BC_346627 2779 ## BC_67233 2767 ## BC_672202 2748 ## BC_8324 2739 ## BC_211828 2737 ## BC_410172 2726 ## BC_495859 2711 ## BC_35659 2695 ## BC_199881 2675 ## BC_44600 2666 ## BC_377226 2649 ## BC_147006 2639 ## BC_41523 2631 ## BC_336794 2621 ## BC_26769 2607 ## BC_244320 2607 ## BC_183112 2600 ## BC_14377 2590 ## BC_7299 2571 ## BC_53380 2558 ## BC_10347 2543 ## BC_164378 2532 ## BC_806 2530 ## BC_20557 2523 ## BC_57621 2521 ## BC_389087 2510 ## BC_100344 2483 ## BC_408928 2456 ## BC_156353 2437 ## BC_154990 2422 ## BC_228994 2422 ## BC_33764 2417 ## BC_23522 2415 ## BC_4564 2388 ## BC_78349 2381 ## BC_10808 2376 ## BC_201293 2355 ## BC_81309 2350 ## BC_45652 2344 ## BC_123791 2325 ## BC_214604 2325 ## BC_2206 2300 ## BC_14492 2283 ## BC_146146 2282 ## BC_6093 2271 ## BC_421415 2269 ## BC_58144 2264 ## BC_167638 2253 ## BC_366637 2253 ## BC_207651 2252 ## BC_154519 2237 ## BC_4733 2236 ## BC_19353 2226 ## BC_326596 2225 ## BC_1764 2173 ## BC_86358 2169 ## BC_101581 2156 ## BC_23241 2151 ## BC_138900 2149 ## BC_11270 2143 ## BC_64945 2139 ## BC_37363 2131 ## BC_638624 2120 ## BC_195246 2112 ## BC_121663 2104 ## BC_672021 2092 ## BC_194823 2090 ## BC_206798 2069 ## BC_521264 2068 ## BC_12191 2065 ## BC_130431 2055 ## BC_462254 2048 ## BC_4531 2034 ## BC_22999 2032 ## BC_64931 2030 ## BC_214878 2028 ## BC_9340 2024 ## BC_350335 2022 ## BC_150291 2015 ## BC_196282 1997 ## BC_95934 1991 ## BC_211811 1981 ## BC_587139 1952 ## BC_199093 1951 ## BC_147380 1942 ## BC_228018 1942 ## BC_116921 1940 ## BC_514247 1938 ## BC_380559 1931 ## BC_141246 1919 ## BC_218093 1914 ## BC_88464 1912 ## BC_157906 1906 ## BC_145403 1881 ## BC_73098 1878 ## BC_61489 1876 ## BC_77101 1863 ## BC_266451 1849 ## BC_138737 1834 ## BC_201372 1833 ## BC_654016 1816 ## BC_444573 1811 ## BC_53219 1808 ## BC_57692 1792 ## BC_175957 1786 ## BC_12418 1777 ## BC_357846 1775 ## BC_14810 1760 ## BC_49226 1752 ## BC_175198 1746 ## BC_49044 1744 ## BC_539228 1743 ## BC_527469 1740 ## BC_181957 1739 ## BC_181554 1738 ## BC_91989 1731 ## BC_353112 1729 ## BC_130861 1727 ## BC_55675 1723 ## BC_10293 1690 ## BC_617554 1682 ## BC_346761 1681 ## BC_302238 1676 ## BC_426662 1675 ## BC_77187 1669 ## BC_19610 1655 ## BC_171953 1655 ## BC_217320 1654 ## BC_446649 1642 ## BC_29208 1632 ## BC_93884 1629 ## BC_20573 1627 ## BC_146322 1627 ## BC_221691 1620 ## BC_261055 1610 ## BC_3583 1607 ## BC_9626 1597 ## BC_6304 1592 ## BC_354350 1589 ## BC_13093 1586 ## BC_203989 1584 ## BC_114257 1577 ## BC_234390 1563 ## BC_249649 1563 ## BC_88099 1560 ## BC_322542 1550 ## BC_351660 1549 ## BC_52081 1544 ## BC_225019 1536 ## BC_596629 1523 ## BC_15448 1522 ## BC_528062 1522 ## BC_2430 1518 ## BC_324042 1514 ## BC_587343 1514 ## BC_67374 1512 ## BC_14484 1496 ## BC_83816 1487 ## BC_141114 1487 ## BC_318282 1487 ## BC_11055 1484 ## BC_45402 1472 ## BC_79755 1472 ## BC_357977 1466 ## BC_415903 1465 ## BC_115436 1461 ## BC_43206 1458 ## BC_50222 1458 ## BC_655329 1444 ## BC_147556 1443 ## BC_182320 1438 ## BC_31779 1437 ## BC_9322 1436 ## BC_162322 1428 ## BC_296283 1425 ## BC_381245 1422 ## BC_283541 1418 ## BC_67247 1411 ## BC_4198 1409 ## BC_31196 1407 ## BC_11152 1404 ## BC_61500 1398 ## BC_90135 1379 ## BC_47310 1378 ## BC_292962 1373 ## BC_82595 1361 ## BC_26258 1360 ## BC_99 1354 ## BC_94579 1351 ## BC_35283 1345 ## BC_238577 1339 ## BC_49397 1323 ## BC_363879 1323 ## BC_309303 1307 ## BC_12410 1304 ## BC_231962 1301 ## BC_7691 1300 ## BC_147063 1293 ## BC_302454 1290 ## BC_92584 1281 ## BC_31185 1273 ## BC_110945 1266 ## BC_144061 1266 ## BC_75688 1264 ## BC_70909 1244 ## BC_238160 1229 ## BC_491991 1228 ## BC_149347 1222 ## BC_195887 1219 ## BC_125634 1217 ## BC_1593 1213 ## BC_57794 1213 ## BC_207333 1212 ## BC_228333 1211 ## BC_316483 1202 ## BC_169627 1201 ## BC_52856 1196 ## BC_129559 1188 ## BC_67370 1175 ## BC_21535 1174 ## BC_515934 1173 ## BC_329007 1172 ## BC_275321 1169 ## BC_444761 1166 ## BC_13 1146 ## BC_516611 1146 ## BC_154696 1145 ## BC_129768 1140 ## BC_159570 1140 ## BC_103862 1135 ## BC_268745 1128 ## BC_9747 1126 ## BC_194092 1118 ## BC_2791 1116 ## BC_176231 1110 ## BC_57582 1109 ## BC_50311 1103 ## BC_48168 1099 ## BC_330625 1095 ## BC_413271 1091 ## BC_322219 1080 ## BC_179919 1079 ## BC_56489 1077 ## BC_257382 1074 ## BC_120505 1046 ## BC_472019 1044 ## BC_282868 1038 ## BC_164652 1033 ## BC_124624 1031 ## BC_355439 1030 ## BC_563624 1029 ## BC_430 1022 ## BC_15707 1012 ## BC_1977 1010 ## BC_17509 1008 ## BC_345889 1007 ## BC_473006 1005 ## BC_567519 1002 ## BC_26384 996 ## BC_28938 995 ## BC_245600 992 ## BC_145103 991 ## BC_81579 990 ## BC_10190 987 ## BC_248582 983 ## BC_142112 982 ## BC_576338 976 ## BC_288976 972 ## BC_90599 968 ## BC_255928 968 ## BC_454109 958 ## BC_220827 948 ## BC_35633 943 ## BC_33035 937 ## BC_62970 935 ## BC_381050 935 ## BC_14452 933 ## BC_189854 931 ## BC_329679 931 ## BC_66948 930 ## BC_82758 927 ## BC_84350 922 ## BC_228451 921 ## BC_186267 917 ## BC_257523 917 ## BC_20281 916 ## BC_170233 915 ## BC_15652 910 ## BC_41280 910 ## BC_276301 908 ## BC_25609 906 ## BC_182092 895 ## BC_186401 892 ## BC_37116 890 ## BC_14380 887 ## BC_259621 886 ## BC_171345 872 ## BC_278096 872 ## BC_567514 870 ## BC_44350 863 ## BC_131019 861 ## BC_297704 857 ## BC_165975 855 ## BC_451991 855 ## BC_370968 850 ## BC_80020 845 ## BC_212837 845 ## BC_4173 843 ## BC_97874 836 ## BC_2762 834 ## BC_143062 832 ## BC_35459 830 ## BC_115038 826 ## BC_6453 825 ## BC_163063 824 ## BC_28077 821 ## BC_23918 816 ## BC_389378 814 ## BC_222891 812 ## BC_250471 808 ## BC_56792 805 ## BC_229036 803 ## BC_170407 800 ## BC_154806 798 ## BC_38465 797 ## BC_337697 797 ## BC_230643 795 ## BC_164579 794 ## BC_146760 792 ## BC_59243 791 ## BC_557872 789 ## BC_652798 783 ## BC_127999 782 ## BC_4061 779 ## BC_22263 779 ## BC_22981 778 ## BC_66747 778 ## BC_8838 775 ## BC_23213 770 ## BC_49060 770 ## BC_29053 768 ## BC_535770 762 ## BC_345175 758 ## BC_7076 755 ## BC_29984 749 ## BC_153858 748 ## BC_5615 747 ## BC_26097 744 ## BC_147873 742 ## BC_19557 741 ## BC_19612 740 ## BC_14131 737 ## BC_100070 735 ## BC_190202 734 ## BC_125776 733 ## BC_388103 733 ## BC_29222 728 ## BC_509872 727 ## BC_561471 724 ## BC_57889 719 ## BC_168163 716 ## BC_18218 705 ## BC_294271 705 ## BC_65912 699 ## BC_4165 695 ## BC_345888 691 ## BC_8159 687 ## BC_1534 686 ## BC_15172 686 ## BC_78760 686 ## BC_23363 685 ## BC_427925 685 ## BC_65621 682 ## BC_41997 681 ## BC_109658 681 plotDetectedBarcodes(dge.filtered.collapsed, percentile = 1, plot = T, group = \"Treatment\") plotDetectedBarcodes(dge.filtered.collapsed, percentile = 0.95, plot = T, group = \"Treatment\")"},{"path":"https://danevass.github.io/bartools/articles/quickstart.html","id":"diversity-analysis","dir":"Articles","previous_headings":"Bartools Quickstart Guide > 4. Analysing Composition and Diversity","what":"Diversity analysis","title":"bartools: quickstart guide","text":"can examine diversity different ways. common Shannon, Simpson, Inverse Simpson Gini. applicable different circumstances, however Shannon diversity index widely used compare global diversity amongst populations barcoded cells.","code":"diversity <- calcDivIndexes(dge.filtered.collapsed$counts) ## Joining with `by = join_by(name)` diversity ## name shannon simpson invsimpson gini group ## 1 1_High_dose 2.348595 0.8444003 6.426747 0.9944996 Group1 ## 2 10_High_dose 1.225780 0.6032511 2.520486 0.9979181 Group1 ## 3 11_Vehicle 2.651924 0.8814260 8.433552 0.9924681 Group1 ## 4 12_Vehicle 2.414033 0.8399949 6.249800 0.9938575 Group1 ## 5 13_Low_dose 2.019197 0.8131725 5.352531 0.9958395 Group1 ## 6 14_Low_dose 1.519874 0.6814167 3.138896 0.9975312 Group1 ## 7 15_Low_dose 1.832136 0.7746698 4.437931 0.9965920 Group1 ## 8 16_Low_dose 2.778791 0.9048391 10.508519 0.9918608 Group1 ## 9 17_High_dose 1.534413 0.6789847 3.115116 0.9974705 Group1 ## 10 18_Vehicle 3.153732 0.9327037 14.859650 0.9879949 Group1 ## 11 2_High_dose 2.256218 0.8510409 6.713251 0.9949786 Group1 ## 12 3_High_dose 1.798233 0.6916675 3.243252 0.9964293 Group1 ## 13 4_Vehicle 3.122387 0.9318059 14.664035 0.9884237 Group1 ## 14 5_Low_dose 1.704952 0.6350916 2.740414 0.9964065 Group1 ## 15 6_High_dose 1.855001 0.7799122 4.543641 0.9963006 Group1 ## 16 7_Vehicle 2.605078 0.8891045 9.017500 0.9931725 Group1 ## 17 8_Vehicle 2.599803 0.8583799 7.061143 0.9924983 Group1 ## 18 9_Low_dose 2.747539 0.8980367 9.807451 0.9919927 Group1 ## 19 T0 5.503885 0.9851023 67.124565 0.8199223 Group1 qplot(diversity$name, diversity$shannon) + theme_bw() + coord_flip() ## Warning: `qplot()` was deprecated in ggplot2 3.4.0. ## This warning is displayed once every 8 hours. ## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was ## generated."},{"path":"https://danevass.github.io/bartools/articles/quickstart.html","id":"comparing-abundance","dir":"Articles","previous_headings":"Bartools Quickstart Guide > 4. Analysing Composition and Diversity","what":"Comparing abundance","title":"bartools: quickstart guide","text":"can also statistically test barcodes / tags / underrepresented group samples relative another using internal edgeR framework. bartools contains convenience wrapper functionality","code":"diff.bc <- compareAbundance(dge.filtered.collapsed, condition = dge.filtered.collapsed$samples$Treatment, condition_names = c(\"Vehicle\", \"High_dose\")) ## Error in compareAbundance(dge.filtered.collapsed, condition = dge.filtered.collapsed$samples$Treatment, : argument 2 matches multiple formal arguments diff.bc ## Error in eval(expr, envir, enclos): object 'diff.bc' not found"},{"path":"https://danevass.github.io/bartools/articles/quickstart.html","id":"linegraphs-of-barcode-abundance---not-working","dir":"Articles","previous_headings":"Bartools Quickstart Guide > 4. Analysing Composition and Diversity","what":"Linegraphs of barcode abundance - NOT WORKING","title":"bartools: quickstart guide","text":"","code":"plotAbundanceLines(dge.filtered.collapsed, condition = dge.filtered.collapsed$samples$group, condition_names = c(\"Vehicle\",\"High_dose\"), plot_type = 'counts')"},{"path":"https://danevass.github.io/bartools/articles/quickstart.html","id":"session-info","dir":"Articles","previous_headings":"Bartools Quickstart Guide","what":"5. Session Info","title":"bartools: quickstart guide","text":"","code":"sessionInfo() ## R version 4.2.2 (2022-10-31) ## Platform: aarch64-apple-darwin20 (64-bit) ## Running under: macOS Ventura 13.1 ## ## Matrix products: default ## BLAS: /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRblas.0.dylib ## LAPACK: /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRlapack.dylib ## ## locale: ## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8 ## ## attached base packages: ## [1] stats graphics grDevices utils datasets methods base ## ## other attached packages: ## [1] bartools_0.2.3 ggplot2_3.4.2 edgeR_3.40.2 limma_3.54.2 ## ## loaded via a namespace (and not attached): ## [1] nlme_3.1-162 matrixStats_1.0.0 fs_1.6.2 ## [4] RColorBrewer_1.1-3 doParallel_1.0.17 rprojroot_2.0.3 ## [7] ggsci_3.0.0 tools_4.2.2 backports_1.4.1 ## [10] bslib_0.5.0 utf8_1.2.3 R6_2.5.1 ## [13] vegan_2.6-4 BiocGenerics_0.44.0 mgcv_1.9-0 ## [16] colorspace_2.1-0 permute_0.9-7 GetoptLong_1.0.5 ## [19] withr_2.5.0 tidyselect_1.2.0 compiler_4.2.2 ## [22] textshaping_0.3.6 cli_3.6.1 desc_1.4.2 ## [25] labeling_0.4.2 sass_0.4.6 scales_1.2.1 ## [28] pkgdown_2.0.7 systemfonts_1.0.4 stringr_1.5.0 ## [31] digest_0.6.33 rmarkdown_2.23 pkgconfig_2.0.3 ## [34] htmltools_0.5.5 fastmap_1.1.1 highr_0.10 ## [37] rlang_1.1.1 GlobalOptions_0.1.2 rstudioapi_0.15.0 ## [40] shape_1.4.6 jquerylib_0.1.4 farver_2.1.1 ## [43] generics_0.1.3 jsonlite_1.8.7 dplyr_1.1.2 ## [46] car_3.1-2 magrittr_2.0.3 Matrix_1.6-0 ## [49] S4Vectors_0.36.2 Rcpp_1.0.11 munsell_0.5.0 ## [52] fansi_1.0.4 abind_1.4-5 lifecycle_1.0.3 ## [55] stringi_1.7.12 yaml_2.3.7 carData_3.0-5 ## [58] MASS_7.3-60 plyr_1.8.8 grid_4.2.2 ## [61] parallel_4.2.2 forcats_1.0.0 crayon_1.5.2 ## [64] lattice_0.21-8 splines_4.2.2 circlize_0.4.15 ## [67] magick_2.7.4 locfit_1.5-9.8 knitr_1.43 ## [70] ComplexHeatmap_2.14.0 pillar_1.9.0 ggpubr_0.6.0 ## [73] rjson_0.2.21 ggsignif_0.6.4 stats4_4.2.2 ## [76] reshape2_1.4.4 codetools_0.2-19 glue_1.6.2 ## [79] evaluate_0.21 vctrs_0.6.3 png_0.1-8 ## [82] foreach_1.5.2 gtable_0.3.3 purrr_1.0.1 ## [85] tidyr_1.3.0 clue_0.3-64 cachem_1.0.8 ## [88] xfun_0.39 broom_1.0.5 rstatix_0.7.2 ## [91] ragg_1.2.5 tibble_3.2.1 iterators_1.0.14 ## [94] ineq_0.2-13 memoise_2.0.1 IRanges_2.32.0 ## [97] cluster_2.1.4"},{"path":"https://danevass.github.io/bartools/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Dane Vassiliadis. Author, maintainer.","code":""},{"path":"https://danevass.github.io/bartools/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Vassiliadis D (2023). bartools: toolkit analysis visualisation synthetic DNA barcodes high thorughput sequencing datasets. R package version 0.2.5.","code":"@Manual{, title = {bartools: A toolkit for the analysis and visualisation of synthetic DNA barcodes in high thorughput sequencing datasets}, author = {Dane Vassiliadis}, year = {2023}, note = {R package version 0.2.5}, }"},{"path":[]},{"path":[]},{"path":"https://danevass.github.io/bartools/index.html","id":"introduction","dir":"","previous_headings":"","what":"Introduction","title":"A toolkit for the analysis and visualisation of synthetic DNA barcodes in high thorughput sequencing datasets","text":"Cellular barcoding powerful widespread method accurately track progeny clone within population cells, enabling dissection biological phenomena single cell resolution. However remains need scalable standardised open-source tools pre-process visualise cellular barcoding datasets. bartools package R-based toolkit analysis cellular barcoding information high throughput sequencing datasets. package consists suite functions annotate, analyse plot DNA barcodes read using common high throughput sequencing methodologies Illumina machines. bartools package optimised use SPLINTR lineage barcode libraries however functions within can adapted cellular barcoding methodology utiilses random DNA barcodes.","code":""},{"path":"https://danevass.github.io/bartools/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"A toolkit for the analysis and visualisation of synthetic DNA barcodes in high thorughput sequencing datasets","text":"can install development version bartools GitHub:","code":"if (!requireNamespace(\"devtools\", quietly = TRUE)) { install.packages(\"devtools\") } devtools::install_github(\"DaneVass/bartools\", dependencies = TRUE)"},{"path":"https://danevass.github.io/bartools/index.html","id":"getting-started","dir":"","previous_headings":"","what":"Getting started","title":"A toolkit for the analysis and visualisation of synthetic DNA barcodes in high thorughput sequencing datasets","text":"See vignette(bartools) details usage examples.","code":""},{"path":"https://danevass.github.io/bartools/index.html","id":"documentation","dir":"","previous_headings":"","what":"Documentation","title":"A toolkit for the analysis and visualisation of synthetic DNA barcodes in high thorughput sequencing datasets","text":"See Docs full package documentation.","code":""},{"path":"https://danevass.github.io/bartools/index.html","id":"looking-for-a-dataset-preprocessing-pipeline","dir":"","previous_headings":"","what":"Looking for a dataset preprocessing pipeline?","title":"A toolkit for the analysis and visualisation of synthetic DNA barcodes in high thorughput sequencing datasets","text":"also developed BARtab, pre-processing pipeline automate extraction enumeration barcode reads raw sequence files. See github ","code":""},{"path":"https://danevass.github.io/bartools/index.html","id":"contact","dir":"","previous_headings":"","what":"Contact","title":"A toolkit for the analysis and visualisation of synthetic DNA barcodes in high thorughput sequencing datasets","text":"bartools package developed Dane Vassiliadis. Please post issues https://github.com/DaneVass/bartools/issues","code":""},{"path":"https://danevass.github.io/bartools/reference/annotateBarcodeReads.html","id":null,"dir":"Reference","previous_headings":"","what":"Annotate Barcode Reads — annotateBarcodeReads","title":"Annotate Barcode Reads — annotateBarcodeReads","text":"Matches cells single cell experiment detected DNA barcodes. Dataframe returned cells matched barcode barcode matchable cell \".detected\" returned cells multiple detected barcodes barcode returned separated ';'","code":""},{"path":"https://danevass.github.io/bartools/reference/annotateBarcodeReads.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Annotate Barcode Reads — annotateBarcodeReads","text":"","code":"annotateBarcodeReads(all.cells, barcoded.cells)"},{"path":"https://danevass.github.io/bartools/reference/annotateBarcodeReads.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Annotate Barcode Reads — annotateBarcodeReads","text":".cells list cells experiment annotated barcoded.cells dataframe dataframe cell id barcode id columns","code":""},{"path":"https://danevass.github.io/bartools/reference/annotateBarcodeReads.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Annotate Barcode Reads — annotateBarcodeReads","text":"Returns data-frame containing 10X cell ID DNA barcode ID.","code":""},{"path":"https://danevass.github.io/bartools/reference/calcDivIndexes.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate diversity indices for barcode samples — calcDivIndexes","title":"Calculate diversity indices for barcode samples — calcDivIndexes","text":"Takes dataframe barcode counts computes shannon, simpson, inverse simpson gini coefficients sample","code":""},{"path":"https://danevass.github.io/bartools/reference/calcDivIndexes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate diversity indices for barcode samples — calcDivIndexes","text":"","code":"calcDivIndexes(dat)"},{"path":"https://danevass.github.io/bartools/reference/calcDivIndexes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate diversity indices for barcode samples — calcDivIndexes","text":"dat dataframe raw normalised counts samples columns observations/barcodes rows","code":""},{"path":"https://danevass.github.io/bartools/reference/calcDivIndexes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate diversity indices for barcode samples — calcDivIndexes","text":"Returns data-frame containing calculated diversity indices per sample","code":""},{"path":"https://danevass.github.io/bartools/reference/calcDivIndexes.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate diversity indices for barcode samples — calcDivIndexes","text":"calcDivIndexes Calculates common diversity indices per sample dataframe counts","code":""},{"path":"https://danevass.github.io/bartools/reference/calcDivIndexes.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate diversity indices for barcode samples — calcDivIndexes","text":"","code":"indexes <- calcDivIndexes(dat = test.counts) #> Error in calcDivIndexes(dat = test.counts): unused argument (dat = test.counts)"},{"path":"https://danevass.github.io/bartools/reference/calcPercentileBarcodes.html","id":null,"dir":"Reference","previous_headings":"","what":"calcPercentileBarcodes — calcPercentileBarcodes","title":"calcPercentileBarcodes — calcPercentileBarcodes","text":"Calculate barcodes comprising Nth percentile sample & generate cumulative sum plots","code":""},{"path":"https://danevass.github.io/bartools/reference/calcPercentileBarcodes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"calcPercentileBarcodes — calcPercentileBarcodes","text":"","code":"calcPercentileBarcodes(counts.obj, percentile = 0.95)"},{"path":"https://danevass.github.io/bartools/reference/calcPercentileBarcodes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"calcPercentileBarcodes — calcPercentileBarcodes","text":"counts.obj DGEList object containing raw normalised barcode counts percentile desired percentile value","code":""},{"path":"https://danevass.github.io/bartools/reference/calcPercentileBarcodes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"calcPercentileBarcodes — calcPercentileBarcodes","text":"Returns list object containing nth percentile tables barcode counts per sample","code":""},{"path":"https://danevass.github.io/bartools/reference/calcPercentileBarcodes.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"calcPercentileBarcodes — calcPercentileBarcodes","text":"","code":"data(test.dge) calcPercentileBarcodes(test.dge, percentile = .95) #> $NumBarcodes #> Sample NumBarcodes #> 1 T0-1 593 #> 2 T0-2 590 #> 3 S10-1 3 #> 4 S10-2 4 #> 5 S11-1 19 #> 6 S11-2 20 #> 7 S12-1 14 #> 8 S12-2 14 #> 9 S13-1 10 #> 10 S13-2 11 #> 11 S14-1 6 #> 12 S14-2 6 #> 13 S15-1 7 #> 14 S15-2 7 #> 15 S16-1 19 #> 16 S16-2 20 #> 17 S17-1 5 #> 18 S17-2 6 #> 19 S18-1 27 #> 20 S18-2 27 #> 21 S1-1 14 #> 22 S1-2 15 #> 23 S2-1 12 #> 24 S2-2 11 #> 25 S3-1 10 #> 26 S3-2 10 #> 27 S4-1 25 #> 28 S4-2 26 #> 29 S5-1 11 #> 30 S5-2 12 #> 31 S6-1 9 #> 32 S6-2 10 #> 33 S7-1 16 #> 34 S7-2 16 #> 35 S8-1 20 #> 36 S8-2 21 #> 37 S9-1 19 #> 38 S9-2 19 #> #> $TopBarcodes #> $TopBarcodes$`T0-1` #> [1] \"BC_245016\" \"BC_121651\" \"BC_420967\" \"BC_108059\" \"BC_323670\" \"BC_9141\" #> [7] \"BC_331193\" \"BC_119418\" \"BC_65104\" \"BC_17570\" \"BC_35704\" \"BC_383329\" #> [13] \"BC_92942\" \"BC_24510\" \"BC_1396\" \"BC_83680\" \"BC_55858\" \"BC_38589\" #> [19] \"BC_137495\" \"BC_69627\" \"BC_446739\" \"BC_171385\" \"BC_12367\" \"BC_280320\" #> [25] \"BC_15782\" \"BC_310257\" \"BC_221975\" \"BC_313129\" \"BC_67201\" \"BC_3618\" #> [31] \"BC_107789\" \"BC_36103\" \"BC_2596\" \"BC_69641\" \"BC_215831\" \"BC_16618\" #> [37] \"BC_249402\" \"BC_141060\" \"BC_13611\" \"BC_22306\" \"BC_588849\" \"BC_43535\" #> [43] \"BC_16956\" \"BC_175651\" \"BC_294188\" \"BC_46538\" \"BC_344050\" \"BC_25084\" #> [49] \"BC_56819\" \"BC_206193\" \"BC_428803\" \"BC_219379\" \"BC_400391\" \"BC_19146\" #> [55] \"BC_205581\" \"BC_148563\" \"BC_114027\" \"BC_172626\" \"BC_403084\" \"BC_62602\" #> [61] \"BC_224300\" \"BC_47439\" \"BC_18278\" \"BC_43128\" \"BC_68847\" \"BC_111272\" #> [67] \"BC_58978\" \"BC_162898\" \"BC_476380\" \"BC_262618\" \"BC_276866\" \"BC_252374\" #> [73] \"BC_550404\" \"BC_325065\" \"BC_202891\" \"BC_50136\" \"BC_255436\" \"BC_103528\" #> [79] \"BC_425129\" \"BC_168818\" \"BC_204266\" \"BC_176946\" \"BC_521149\" \"BC_105387\" #> [85] \"BC_227720\" \"BC_83707\" \"BC_64584\" \"BC_21018\" \"BC_458543\" \"BC_11643\" #> [91] \"BC_175871\" \"BC_556149\" \"BC_34617\" \"BC_166467\" \"BC_41692\" \"BC_72619\" #> [97] \"BC_168530\" \"BC_289383\" \"BC_2040\" \"BC_516753\" \"BC_24159\" \"BC_249161\" #> [103] \"BC_345717\" \"BC_54442\" \"BC_236596\" \"BC_248159\" \"BC_206263\" \"BC_376665\" #> [109] \"BC_18345\" \"BC_114040\" \"BC_612467\" \"BC_56338\" \"BC_363714\" \"BC_39281\" #> [115] \"BC_30946\" \"BC_32325\" \"BC_418545\" \"BC_103714\" \"BC_140320\" \"BC_120596\" #> [121] \"BC_503475\" \"BC_160057\" \"BC_16754\" \"BC_43673\" \"BC_173736\" \"BC_46524\" #> [127] \"BC_142999\" \"BC_11277\" \"BC_67854\" \"BC_258799\" \"BC_13395\" \"BC_37062\" #> [133] \"BC_38398\" \"BC_105732\" \"BC_139928\" \"BC_355420\" \"BC_40856\" \"BC_128266\" #> [139] \"BC_321677\" \"BC_203520\" \"BC_29284\" \"BC_172412\" \"BC_85441\" \"BC_20263\" #> [145] \"BC_118831\" \"BC_10454\" \"BC_5256\" \"BC_129519\" \"BC_11391\" \"BC_172238\" #> [151] \"BC_43123\" \"BC_42178\" \"BC_102205\" \"BC_346124\" \"BC_389078\" \"BC_42612\" #> [157] \"BC_93485\" \"BC_415373\" \"BC_3733\" \"BC_32680\" \"BC_474982\" \"BC_7405\" #> [163] \"BC_289568\" \"BC_229995\" \"BC_79026\" \"BC_45955\" \"BC_146666\" \"BC_183401\" #> [169] \"BC_356531\" \"BC_187025\" \"BC_42310\" \"BC_221056\" \"BC_132225\" \"BC_235128\" #> [175] \"BC_15740\" \"BC_160158\" \"BC_320233\" \"BC_8835\" \"BC_173674\" \"BC_53234\" #> [181] \"BC_91412\" \"BC_135438\" \"BC_373901\" \"BC_44599\" \"BC_87047\" \"BC_350464\" #> [187] \"BC_324711\" \"BC_40516\" \"BC_113839\" \"BC_162119\" \"BC_33699\" \"BC_635211\" #> [193] \"BC_346627\" \"BC_7122\" \"BC_806\" \"BC_63846\" \"BC_283335\" \"BC_151544\" #> [199] \"BC_273124\" \"BC_227308\" \"BC_47726\" \"BC_102803\" \"BC_244320\" \"BC_10347\" #> [205] \"BC_389742\" \"BC_2865\" \"BC_77030\" \"BC_65130\" \"BC_564039\" \"BC_519985\" #> [211] \"BC_76947\" \"BC_4564\" \"BC_521485\" \"BC_495859\" \"BC_35659\" \"BC_14377\" #> [217] \"BC_199881\" \"BC_147006\" \"BC_26769\" \"BC_100344\" \"BC_672202\" \"BC_146146\" #> [223] \"BC_320752\" \"BC_324953\" \"BC_217211\" \"BC_67965\" \"BC_210529\" \"BC_189813\" #> [229] \"BC_242383\" \"BC_560839\" \"BC_228994\" \"BC_389087\" \"BC_44600\" \"BC_20557\" #> [235] \"BC_10808\" \"BC_138900\" \"BC_81309\" \"BC_145719\" \"BC_58144\" \"BC_7299\" #> [241] \"BC_78349\" \"BC_102160\" \"BC_164378\" \"BC_211828\" \"BC_336794\" \"BC_53380\" #> [247] \"BC_14492\" \"BC_23241\" \"BC_57621\" \"BC_11270\" \"BC_41523\" \"BC_33764\" #> [253] \"BC_12191\" \"BC_17608\" \"BC_8324\" \"BC_183112\" \"BC_167638\" \"BC_2206\" #> [259] \"BC_123791\" \"BC_377226\" \"BC_195246\" \"BC_161563\" \"BC_67233\" \"BC_421415\" #> [265] \"BC_150291\" \"BC_408928\" \"BC_410172\" \"BC_121663\" \"BC_201293\" \"BC_156353\" #> [271] \"BC_45652\" \"BC_357846\" \"BC_199093\" \"BC_214878\" \"BC_6093\" \"BC_214604\" #> [277] \"BC_302238\" \"BC_521264\" \"BC_672021\" \"BC_2294\" \"BC_587139\" \"BC_638624\" #> [283] \"BC_86358\" \"BC_1764\" \"BC_366637\" \"BC_218093\" \"BC_201372\" \"BC_19353\" #> [289] \"BC_130431\" \"BC_22999\" \"BC_49044\" \"BC_154519\" \"BC_324042\" \"BC_37363\" #> [295] \"BC_154990\" \"BC_350335\" \"BC_194823\" \"BC_23522\" \"BC_141246\" \"BC_4531\" #> [301] \"BC_4733\" \"BC_654016\" \"BC_147380\" \"BC_196282\" \"BC_52081\" \"BC_207651\" #> [307] \"BC_380559\" \"BC_527469\" \"BC_353112\" \"BC_426662\" \"BC_655329\" \"BC_73098\" #> [313] \"BC_146322\" \"BC_596629\" \"BC_64945\" \"BC_462254\" \"BC_261055\" \"BC_145403\" #> [319] \"BC_61489\" \"BC_617554\" \"BC_228018\" \"BC_249649\" \"BC_211811\" \"BC_101581\" #> [325] \"BC_43206\" \"BC_326596\" \"BC_514247\" \"BC_88464\" \"BC_203989\" \"BC_206798\" #> [331] \"BC_19610\" \"BC_9626\" \"BC_116921\" \"BC_181554\" \"BC_79755\" \"BC_77101\" #> [337] \"BC_55675\" \"BC_83816\" \"BC_138737\" \"BC_322542\" \"BC_318282\" \"BC_444573\" #> [343] \"BC_9340\" \"BC_49226\" \"BC_57692\" \"BC_93884\" \"BC_181957\" \"BC_217320\" #> [349] \"BC_296283\" \"BC_3583\" \"BC_11055\" \"BC_95934\" \"BC_88099\" \"BC_35283\" #> [355] \"BC_67374\" \"BC_175957\" \"BC_266451\" \"BC_283541\" \"BC_14484\" \"BC_14810\" #> [361] \"BC_157906\" \"BC_354350\" \"BC_539228\" \"BC_61500\" \"BC_45402\" \"BC_234390\" #> [367] \"BC_415903\" \"BC_12418\" \"BC_238160\" \"BC_29208\" \"BC_91989\" \"BC_92584\" #> [373] \"BC_351660\" \"BC_381245\" \"BC_31196\" \"BC_47310\" \"BC_20573\" \"BC_64931\" #> [379] \"BC_2430\" \"BC_15448\" \"BC_94579\" \"BC_171953\" \"BC_9322\" \"BC_587343\" #> [385] \"BC_50222\" \"BC_53219\" \"BC_77187\" \"BC_7691\" \"BC_225019\" \"BC_238577\" #> [391] \"BC_6304\" \"BC_13093\" \"BC_154696\" \"BC_275321\" \"BC_75688\" \"BC_10293\" #> [397] \"BC_13\" \"BC_90135\" \"BC_357977\" \"BC_4198\" \"BC_147556\" \"BC_231962\" #> [403] \"BC_175198\" \"BC_144061\" \"BC_26258\" \"BC_114257\" \"BC_194092\" \"BC_31779\" #> [409] \"BC_268745\" \"BC_67247\" \"BC_82595\" \"BC_103862\" \"BC_149347\" \"BC_346761\" #> [415] \"BC_473006\" \"BC_49397\" \"BC_302454\" \"BC_207333\" \"BC_31185\" \"BC_115436\" #> [421] \"BC_124624\" \"BC_491991\" \"BC_309303\" \"BC_12410\" \"BC_228333\" \"BC_528062\" #> [427] \"BC_363879\" \"BC_70909\" \"BC_563624\" \"BC_11152\" \"BC_446649\" \"BC_129768\" #> [433] \"BC_129559\" \"BC_169627\" \"BC_21535\" \"BC_516611\" \"BC_57582\" \"BC_159570\" #> [439] \"BC_130861\" \"BC_48168\" \"BC_66948\" \"BC_257382\" \"BC_179919\" \"BC_15707\" #> [445] \"BC_17509\" \"BC_444761\" \"BC_57794\" \"BC_147063\" \"BC_52856\" \"BC_14452\" #> [451] \"BC_316483\" \"BC_186401\" \"BC_90599\" \"BC_245600\" \"BC_1977\" \"BC_99\" #> [457] \"BC_176231\" \"BC_472019\" \"BC_292962\" \"BC_221691\" \"BC_170233\" \"BC_182320\" #> [463] \"BC_381050\" \"BC_28938\" \"BC_67370\" \"BC_110945\" \"BC_195887\" \"BC_257523\" #> [469] \"BC_2762\" \"BC_255928\" \"BC_355439\" \"BC_33035\" \"BC_125634\" \"BC_430\" #> [475] \"BC_146760\" \"BC_164579\" \"BC_345889\" \"BC_142112\" \"BC_515934\" \"BC_141114\" #> [481] \"BC_20281\" \"BC_322219\" \"BC_248582\" \"BC_1593\" \"BC_9747\" \"BC_82758\" #> [487] \"BC_329007\" \"BC_567519\" \"BC_229036\" \"BC_50311\" \"BC_576338\" \"BC_189854\" #> [493] \"BC_25609\" \"BC_56489\" \"BC_10190\" \"BC_41280\" \"BC_370968\" \"BC_162322\" #> [499] \"BC_337697\" \"BC_6453\" \"BC_186267\" \"BC_222891\" \"BC_297704\" \"BC_145103\" #> [505] \"BC_81579\" \"BC_35633\" \"BC_170407\" \"BC_62970\" \"BC_120505\" \"BC_66747\" #> [511] \"BC_413466\" \"BC_329679\" \"BC_454109\" \"BC_37116\" \"BC_389378\" \"BC_4061\" #> [517] \"BC_163063\" \"BC_125776\" \"BC_38465\" \"BC_278096\" \"BC_288976\" \"BC_171345\" #> [523] \"BC_557872\" \"BC_127999\" \"BC_259621\" \"BC_314343\" \"BC_80020\" \"BC_228451\" #> [529] \"BC_282868\" \"BC_182092\" \"BC_56792\" \"BC_143062\" \"BC_219703\" \"BC_235649\" #> [535] \"BC_131019\" \"BC_561471\" \"BC_22981\" \"BC_567514\" \"BC_345175\" \"BC_345888\" #> [541] \"BC_7076\" \"BC_220827\" \"BC_23213\" \"BC_73196\" \"BC_2142\" \"BC_41997\" #> [547] \"BC_147873\" \"BC_19557\" \"BC_29053\" \"BC_230643\" \"BC_28077\" \"BC_22263\" #> [553] \"BC_29984\" \"BC_215667\" \"BC_69571\" \"BC_8838\" \"BC_275938\" \"BC_413636\" #> [559] \"BC_2791\" \"BC_26462\" \"BC_29222\" \"BC_4173\" \"BC_89937\" \"BC_8159\" #> [565] \"BC_413271\" \"BC_14131\" \"BC_59243\" \"BC_190202\" \"BC_294271\" \"BC_23918\" #> [571] \"BC_154806\" \"BC_214774\" \"BC_26384\" \"BC_57471\" \"BC_1534\" \"BC_451991\" #> [577] \"BC_15172\" \"BC_109658\" \"BC_483925\" \"BC_71820\" \"BC_330625\" \"BC_153858\" #> [583] \"BC_319910\" \"BC_459545\" \"BC_53645\" \"BC_84350\" \"BC_427925\" \"BC_75310\" #> [589] \"BC_5615\" \"BC_142652\" \"BC_50626\" \"BC_107381\" \"BC_8715\" #> #> $TopBarcodes$`T0-2` #> [1] \"BC_245016\" \"BC_121651\" \"BC_420967\" \"BC_108059\" \"BC_323670\" \"BC_9141\" #> [7] \"BC_331193\" \"BC_119418\" \"BC_65104\" \"BC_17570\" \"BC_35704\" \"BC_92942\" #> [13] \"BC_383329\" \"BC_83680\" \"BC_1396\" \"BC_24510\" \"BC_55858\" \"BC_38589\" #> [19] \"BC_313129\" \"BC_446739\" \"BC_137495\" \"BC_69627\" \"BC_171385\" \"BC_280320\" #> [25] \"BC_221975\" \"BC_12367\" \"BC_15782\" \"BC_3618\" \"BC_310257\" \"BC_107789\" #> [31] \"BC_67201\" \"BC_36103\" \"BC_215831\" \"BC_43535\" \"BC_2596\" \"BC_13611\" #> [37] \"BC_294188\" \"BC_588849\" \"BC_16618\" \"BC_69641\" \"BC_22306\" \"BC_428803\" #> [43] \"BC_403084\" \"BC_141060\" \"BC_205581\" \"BC_249402\" \"BC_206193\" \"BC_25084\" #> [49] \"BC_344050\" \"BC_400391\" \"BC_325065\" \"BC_219379\" \"BC_46538\" \"BC_289383\" #> [55] \"BC_56819\" \"BC_43128\" \"BC_175651\" \"BC_18278\" \"BC_172626\" \"BC_19146\" #> [61] \"BC_47439\" \"BC_252374\" \"BC_58978\" \"BC_21018\" \"BC_521149\" \"BC_16956\" #> [67] \"BC_262618\" \"BC_50136\" \"BC_202891\" \"BC_148563\" \"BC_62602\" \"BC_458543\" #> [73] \"BC_68847\" \"BC_114027\" \"BC_476380\" \"BC_276866\" \"BC_111272\" \"BC_176946\" #> [79] \"BC_255436\" \"BC_168818\" \"BC_550404\" \"BC_175871\" \"BC_204266\" \"BC_64584\" #> [85] \"BC_425129\" \"BC_83707\" \"BC_162898\" \"BC_224300\" \"BC_103528\" \"BC_34617\" #> [91] \"BC_72619\" \"BC_516753\" \"BC_105387\" \"BC_376665\" \"BC_18345\" \"BC_2040\" #> [97] \"BC_168530\" \"BC_11643\" \"BC_114040\" \"BC_227720\" \"BC_556149\" \"BC_7405\" #> [103] \"BC_249161\" \"BC_345717\" \"BC_363714\" \"BC_248159\" \"BC_24159\" \"BC_236596\" #> [109] \"BC_16754\" \"BC_206263\" \"BC_118831\" \"BC_54442\" \"BC_41692\" \"BC_503475\" #> [115] \"BC_5256\" \"BC_11277\" \"BC_67854\" \"BC_30946\" \"BC_43673\" \"BC_29284\" #> [121] \"BC_85441\" \"BC_160057\" \"BC_166467\" \"BC_56338\" \"BC_20263\" \"BC_258799\" #> [127] \"BC_172412\" \"BC_32325\" \"BC_321677\" \"BC_79026\" \"BC_37062\" \"BC_120596\" #> [133] \"BC_203520\" \"BC_418545\" \"BC_135438\" \"BC_139928\" \"BC_160158\" \"BC_635211\" #> [139] \"BC_612467\" \"BC_11391\" \"BC_564039\" \"BC_47726\" \"BC_105732\" \"BC_46524\" #> [145] \"BC_53234\" \"BC_103714\" \"BC_140320\" \"BC_289568\" \"BC_415373\" \"BC_102803\" #> [151] \"BC_39281\" \"BC_3733\" \"BC_172238\" \"BC_93485\" \"BC_13395\" \"BC_43123\" #> [157] \"BC_2294\" \"BC_146666\" \"BC_132225\" \"BC_40516\" \"BC_173736\" \"BC_42612\" #> [163] \"BC_356531\" \"BC_113839\" \"BC_519985\" \"BC_129519\" \"BC_77030\" \"BC_32680\" #> [169] \"BC_183401\" \"BC_128266\" \"BC_210529\" \"BC_161563\" \"BC_355420\" \"BC_217211\" #> [175] \"BC_324711\" \"BC_10454\" \"BC_91412\" \"BC_44599\" \"BC_40856\" \"BC_8835\" #> [181] \"BC_142999\" \"BC_173674\" \"BC_151544\" \"BC_229995\" 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#> BC_503475 4931 #> BC_5256 4925 #> BC_11277 4919 #> BC_67854 4806 #> BC_30946 4718 #> BC_43673 4688 #> BC_29284 4677 #> BC_85441 4668 #> BC_160057 4652 #> BC_166467 4593 #> BC_56338 4587 #> BC_20263 4536 #> BC_258799 4489 #> BC_172412 4372 #> BC_32325 4362 #> BC_321677 4354 #> BC_79026 4342 #> BC_37062 4337 #> BC_120596 4332 #> BC_203520 4274 #> BC_418545 4216 #> BC_135438 4181 #> BC_139928 4158 #> BC_160158 4131 #> BC_635211 4121 #> BC_612467 4112 #> BC_11391 4062 #> BC_564039 4055 #> BC_47726 4015 #> BC_105732 4008 #> BC_46524 3976 #> BC_53234 3974 #> BC_103714 3971 #> BC_140320 3967 #> BC_289568 3947 #> BC_415373 3915 #> BC_102803 3880 #> BC_39281 3868 #> BC_3733 3867 #> BC_172238 3845 #> BC_93485 3842 #> BC_13395 3799 #> BC_43123 3765 #> BC_2294 3755 #> BC_146666 3743 #> BC_132225 3716 #> BC_40516 3710 #> BC_173736 3708 #> BC_42612 3706 #> BC_356531 3698 #> BC_113839 3658 #> BC_519985 3646 #> BC_129519 3613 #> BC_77030 3597 #> BC_32680 3585 #> BC_183401 3585 #> BC_128266 3548 #> BC_210529 3515 #> BC_161563 3448 #> BC_355420 3440 #> BC_217211 3431 #> BC_324711 3417 #> BC_10454 3411 #> BC_91412 3399 #> BC_44599 3397 #> BC_40856 3368 #> BC_8835 3358 #> BC_142999 3334 #> BC_173674 3330 #> BC_151544 3325 #> BC_229995 3292 #> BC_346124 3257 #> BC_42310 3253 #> BC_102160 3250 #> BC_45955 3249 #> BC_15740 3246 #> BC_17608 3193 #> BC_350464 3183 #> BC_560839 3183 #> BC_389078 3178 #> BC_76947 3169 #> BC_33699 3141 #> BC_145719 3132 #> BC_42178 3119 #> BC_67233 3104 #> BC_38398 3094 #> BC_7122 3083 #> BC_410172 3047 #> BC_65130 3008 #> BC_189813 2976 #> BC_8324 2930 #> BC_324953 2909 #> BC_521485 2908 #> BC_235128 2892 #> BC_63846 2883 #> BC_320752 2881 #> BC_87047 2866 #> BC_67965 2860 #> BC_102205 2842 #> BC_242383 2833 #> BC_377226 2832 #> BC_389742 2832 #> BC_211828 2822 #> BC_273124 2790 #> BC_23522 2783 #> BC_154990 2746 #> BC_320233 2714 #> BC_41523 2674 #> BC_183112 2669 #> BC_221056 2643 #> BC_2865 2641 #> BC_672202 2641 #> BC_187025 2632 #> BC_326596 2621 #> BC_283335 2602 #> BC_336794 2593 #> BC_44600 2570 #> BC_227308 2555 #> BC_156353 2536 #> BC_207651 2528 #> BC_408928 2503 #> BC_495859 2502 #> BC_162119 2501 #> BC_64931 2488 #> BC_7299 2476 #> BC_53380 2476 #> BC_35659 2473 #> BC_101581 2466 #> BC_4733 2447 #> BC_199881 2446 #> BC_57621 2434 #> BC_373901 2422 #> BC_164378 2409 #> BC_147006 2391 #> BC_214604 2385 #> BC_45652 2368 #> BC_154519 2364 #> BC_64945 2363 #> BC_346627 2344 #> BC_201293 2343 #> BC_366637 2336 #> BC_26769 2329 #> BC_474982 2326 #> BC_206798 2324 #> BC_9340 2319 #> BC_19353 2314 #> BC_95934 2291 #> BC_20557 2289 #> BC_14377 2272 #> BC_6093 2256 #> BC_33764 2252 #> BC_389087 2245 #> BC_130861 2223 #> BC_157906 2189 #> BC_462254 2183 #> BC_244320 2179 #> BC_1764 2173 #> BC_123791 2169 #> BC_37363 2159 #> BC_86358 2140 #> BC_194823 2129 #> BC_211811 2114 #> BC_53219 2111 #> BC_421415 2111 #> BC_100344 2097 #> BC_2206 2096 #> BC_78349 2096 #> BC_221691 2096 #> BC_116921 2090 #> BC_228994 2056 #> BC_10347 2055 #> BC_514247 2053 #> BC_175198 2052 #> BC_266451 2041 #> BC_638624 2035 #> BC_4531 2033 #> BC_10808 2028 #> BC_88464 2010 #> BC_228018 2003 #> BC_196282 2000 #> BC_81309 1999 #> BC_167638 1997 #> BC_130431 1976 #> BC_346761 1975 #> BC_77101 1969 #> BC_672021 1962 #> BC_446649 1959 #> BC_12418 1958 #> BC_22999 1952 #> BC_350335 1946 #> BC_14492 1930 #> BC_806 1925 #> BC_141114 1924 #> BC_138737 1921 #> BC_10293 1918 #> BC_175957 1910 #> BC_14810 1895 #> BC_444573 1892 #> BC_521264 1892 #> BC_147380 1890 #> BC_380559 1888 #> BC_539228 1879 #> BC_162322 1873 #> BC_91989 1868 #> BC_145403 1865 #> BC_57692 1863 #> BC_61489 1859 #> BC_58144 1855 #> BC_4564 1842 #> BC_77187 1838 #> BC_121663 1816 #> BC_73098 1810 #> BC_141246 1792 #> BC_195246 1778 #> BC_49226 1777 #> BC_171953 1763 #> BC_181957 1761 #> BC_214878 1757 #> BC_182320 1748 #> BC_114257 1734 #> BC_146146 1716 #> BC_6304 1699 #> BC_181554 1696 #> BC_13093 1694 #> BC_528062 1694 #> BC_587139 1693 #> BC_55675 1691 #> BC_23241 1690 #> BC_11270 1682 #> BC_20573 1682 #> BC_29208 1670 #> BC_218093 1661 #> BC_654016 1623 #> BC_150291 1607 #> BC_292962 1600 #> BC_217320 1596 #> BC_199093 1595 #> BC_138900 1579 #> BC_225019 1578 #> BC_12191 1567 #> BC_354350 1567 #> BC_115436 1556 #> BC_99 1554 #> BC_93884 1538 #> BC_234390 1525 #> BC_527469 1520 #> BC_351660 1515 #> BC_201372 1512 #> BC_3583 1511 #> BC_19610 1508 #> BC_587343 1507 #> BC_353112 1499 #> BC_15448 1489 #> BC_357977 1482 #> BC_2430 1479 #> BC_617554 1473 #> BC_11152 1472 #> BC_31779 1461 #> BC_330625 1451 #> BC_147556 1443 #> BC_88099 1436 #> BC_2791 1426 #> BC_67247 1424 #> BC_110945 1421 #> BC_50222 1412 #> BC_147063 1403 #> BC_9626 1400 #> BC_1593 1394 #> BC_426662 1392 #> BC_413271 1390 #> BC_49044 1376 #> BC_4198 1373 #> BC_164652 1373 #> BC_125634 1369 #> BC_322542 1367 #> BC_203989 1353 #> BC_67374 1351 #> BC_45402 1343 #> BC_14484 1337 #> BC_195887 1334 #> BC_9322 1332 #> BC_415903 1331 #> BC_82595 1324 #> BC_329007 1324 #> BC_146322 1318 #> BC_261055 1311 #> BC_90135 1307 #> BC_26258 1298 #> BC_363879 1296 #> BC_515934 1290 #> BC_11055 1269 #> BC_49397 1269 #> BC_381245 1266 #> BC_309303 1253 #> BC_12410 1249 #> BC_249649 1249 #> BC_318282 1244 #> BC_57794 1243 #> BC_67370 1239 #> BC_316483 1238 #> BC_31196 1237 #> BC_357846 1230 #> BC_9747 1223 #> BC_83816 1223 #> BC_26384 1221 #> BC_52856 1219 #> BC_302454 1203 #> BC_50311 1196 #> BC_282868 1192 #> BC_61500 1190 #> BC_238577 1185 #> BC_47310 1181 #> BC_31185 1180 #> BC_283541 1179 #> BC_79755 1165 #> BC_231962 1162 #> BC_56489 1154 #> BC_120505 1153 #> BC_94579 1150 #> BC_444761 1146 #> BC_70909 1143 #> BC_296283 1140 #> BC_15652 1129 #> BC_276301 1124 #> BC_322219 1124 #> BC_169627 1121 #> BC_84350 1120 #> BC_596629 1118 #> BC_21535 1110 #> BC_52081 1108 #> BC_7691 1104 #> BC_302238 1103 #> BC_165975 1102 #> BC_14380 1101 #> BC_144061 1099 #> BC_129559 1093 #> BC_491991 1091 #> BC_43206 1083 #> BC_35459 1081 #> BC_228333 1070 #> BC_176231 1069 #> BC_75688 1063 #> BC_516611 1057 #> BC_149347 1055 #> BC_220827 1051 #> BC_207333 1049 #> BC_159570 1046 #> BC_288976 1044 #> BC_81579 1027 #> BC_44350 1025 #> BC_145103 1024 #> BC_35283 1012 #> BC_250471 1004 #> BC_97874 1000 #> BC_567519 989 #> BC_454109 988 #> BC_355439 986 #> BC_430 984 #> BC_57582 984 #> BC_48168 983 #> BC_10190 979 #> BC_19612 978 #> BC_92584 976 #> BC_212837 976 #> BC_129768 970 #> BC_345889 956 #> BC_115038 955 #> BC_228451 955 #> BC_451991 955 #> BC_179919 948 #> BC_576338 944 #> BC_655329 940 #> BC_472019 939 #> BC_168163 936 #> BC_35633 935 #> BC_257382 935 #> BC_535770 934 #> BC_329679 932 #> BC_248582 931 #> BC_62970 925 #> BC_324042 919 #> BC_18218 912 #> BC_182092 908 #> BC_142112 907 #> BC_4173 892 #> BC_100070 892 #> BC_259621 883 #> BC_567514 880 #> BC_103862 878 #> BC_28938 874 #> BC_325871 872 #> BC_49060 871 #> BC_275321 871 #> BC_186267 868 #> BC_652798 865 #> BC_1977 863 #> BC_238160 862 #> BC_189854 858 #> BC_37116 855 #> BC_255928 854 #> BC_268745 852 #> BC_23918 848 #> BC_131019 847 #> BC_171345 844 #> BC_278096 840 #> BC_13 834 #> BC_82758 833 #> BC_26097 831 #> BC_65912 831 #> BC_41280 826 #> BC_245600 824 #> BC_509872 823 #> BC_15707 821 #> BC_17509 821 #> BC_154696 821 #> BC_194092 820 #> BC_28077 816 #> BC_4165 815 #> BC_154806 813 #> BC_25609 810 #> BC_33035 807 #> BC_1139 802 #> BC_80020 802 #> BC_388103 801 #> BC_59243 795 #> BC_20281 788 #> BC_57889 788 #> BC_143062 788 #> BC_218837 784 #> BC_179997 778 #> BC_90599 776 #> BC_5615 775 #> BC_23363 774 #> BC_352645 772 #> BC_292621 762 #> BC_153858 760 #> BC_230643 759 #> BC_297704 754 #> BC_381050 751 #> BC_203210 746 #> BC_8838 738 #> BC_183115 738 #> BC_257523 737 #> BC_22263 734 #> BC_56792 733 #> BC_163063 729 #> BC_184732 723 #> BC_563624 718 #> BC_77131 713 #> BC_78760 706 #> BC_370968 706 #> BC_307924 704 #> BC_331889 704 #> BC_389378 704 #> BC_29053 702 #> BC_23213 700 #> BC_428204 700 #> BC_500299 697 #> BC_14452 696 #> BC_124624 696 #> BC_22981 694 #> BC_98171 693 #> BC_170233 692 #> BC_38465 691 #> BC_47704 691 #> BC_198370 686 #> BC_14131 683 #> BC_190202 683 #> BC_278655 683 #> BC_6453 682 #> BC_557872 681 #> BC_31562 677 #> BC_128748 676 #> BC_29984 675 #> BC_294176 673 #> BC_127999 668 #> BC_796 664 #> #> $TopBarcodeCounts$`S10-1` #> S10-1 #> BC_8419 1991893 #> BC_124796 1649985 #> BC_102160 203430 #> #> $TopBarcodeCounts$`S10-2` #> S10-2 #> BC_8419 1995956 #> BC_124796 1710825 #> BC_102160 208878 #> BC_68847 68155 #> #> $TopBarcodeCounts$`S11-1` #> S11-1 #> BC_53234 680240 #> BC_205581 493308 #> BC_90135 332038 #> BC_172626 257083 #> BC_58978 196664 #> BC_79755 116795 #> BC_159570 108699 #> BC_118640 73041 #> BC_31610 71654 #> BC_257382 64909 #> BC_1607 61994 #> BC_635211 58710 #> BC_4531 58679 #> BC_324711 55178 #> BC_327422 30934 #> BC_297704 27654 #> BC_194823 25858 #> BC_415460 21413 #> BC_140320 19073 #> #> $TopBarcodeCounts$`S11-2` #> S11-2 #> BC_53234 672838 #> BC_205581 480681 #> BC_90135 319597 #> BC_172626 226125 #> BC_58978 168069 #> BC_79755 107090 #> BC_159570 101497 #> BC_635211 67342 #> BC_118640 57323 #> BC_324711 56730 #> BC_31610 55511 #> BC_257382 55165 #> BC_4531 50457 #> BC_1607 49865 #> BC_327422 34475 #> BC_297704 22422 #> BC_194823 22402 #> BC_415460 20209 #> BC_62602 17792 #> BC_140320 16805 #> #> $TopBarcodeCounts$`S12-1` #> S12-1 #> BC_389078 1413816 #> BC_159570 615742 #> BC_135438 272292 #> BC_500780 246148 #> BC_79755 235092 #> BC_31610 185291 #> BC_49629 144164 #> BC_78349 134743 #> BC_118640 132924 #> BC_47726 128882 #> BC_58978 115558 #> BC_1607 108011 #> BC_245016 107080 #> BC_329679 79183 #> #> $TopBarcodeCounts$`S12-2` #> S12-2 #> BC_389078 1397641 #> BC_159570 557943 #> BC_135438 248280 #> BC_500780 235202 #> BC_79755 219591 #> BC_31610 171947 #> BC_49629 131663 #> BC_118640 120960 #> BC_47726 117847 #> BC_78349 109942 #> BC_245016 99030 #> BC_58978 97876 #> BC_1607 95198 #> BC_329679 74065 #> #> $TopBarcodeCounts$`S13-1` #> S13-1 #> BC_8419 1132876 #> BC_124796 1113617 #> BC_102160 813971 #> BC_257382 768188 #> BC_172626 112559 #> BC_345889 84734 #> BC_53234 72801 #> BC_79755 55138 #> BC_5472 54808 #> BC_205581 41262 #> #> $TopBarcodeCounts$`S13-2` #> S13-2 #> BC_8419 1075999 #> BC_124796 1036131 #> BC_102160 766818 #> BC_257382 724300 #> BC_172626 106215 #> BC_345889 77361 #> BC_53234 65594 #> BC_5472 52942 #> BC_79755 51810 #> BC_17608 39408 #> BC_205581 38917 #> #> $TopBarcodeCounts$`S14-1` #> S14-1 #> BC_90135 2015353 #> BC_388103 1491990 #> BC_31610 325001 #> BC_58978 186583 #> BC_292962 127737 #> BC_159570 82869 #> #> $TopBarcodeCounts$`S14-2` #> S14-2 #> BC_90135 1814510 #> BC_388103 1338206 #> BC_31610 285251 #> BC_58978 161284 #> BC_292962 125041 #> BC_159570 76636 #> #> $TopBarcodeCounts$`S15-1` #> S15-1 #> BC_1478 1137019 #> BC_94579 930125 #> BC_124796 490802 #> BC_8419 480465 #> BC_316204 95059 #> BC_400391 61062 #> BC_23241 42520 #> #> $TopBarcodeCounts$`S15-2` #> S15-2 #> BC_1478 1189434 #> BC_94579 976104 #> BC_124796 494292 #> BC_8419 486189 #> BC_316204 96050 #> BC_400391 67224 #> BC_23241 43739 #> #> $TopBarcodeCounts$`S16-1` #> S16-1 #> BC_53234 967214 #> BC_79755 699557 #> BC_140320 622182 #> BC_31610 355389 #> BC_248880 310481 #> BC_1478 286098 #> BC_17608 219959 #> BC_257382 191551 #> BC_345889 186661 #> BC_94511 140502 #> BC_142112 136360 #> BC_142024 111299 #> BC_91412 96497 #> BC_58978 76133 #> BC_190202 65758 #> BC_4198 51449 #> BC_68847 48868 #> BC_389078 39963 #> BC_47726 37946 #> #> $TopBarcodeCounts$`S16-2` #> S16-2 #> BC_53234 955906 #> BC_79755 679328 #> BC_140320 605778 #> BC_31610 351863 #> BC_248880 297737 #> BC_1478 286187 #> BC_17608 215120 #> BC_257382 192418 #> BC_345889 192200 #> BC_94511 143289 #> BC_142112 142201 #> BC_142024 111762 #> BC_91412 97112 #> BC_58978 77379 #> BC_190202 66762 #> BC_68847 51297 #> BC_4198 51154 #> BC_389078 41183 #> BC_47726 37684 #> BC_205581 34755 #> #> $TopBarcodeCounts$`S17-1` #> S17-1 #> BC_183691 2648948 #> BC_90135 1584269 #> BC_400391 588642 #> BC_31610 216675 #> BC_68847 146098 #> #> $TopBarcodeCounts$`S17-2` #> S17-2 #> BC_183691 2785778 #> BC_90135 1600044 #> BC_400391 604178 #> BC_31610 206900 #> BC_68847 158068 #> BC_53234 139785 #> #> $TopBarcodeCounts$`S18-1` #> S18-1 #> BC_49397 763504 #> BC_91412 419115 #> BC_159570 417080 #> BC_62602 261522 #> BC_93485 252666 #> BC_70225 234477 #> BC_79755 204363 #> BC_4564 141545 #> BC_124796 133198 #> BC_58978 124347 #> BC_8419 120698 #> BC_149935 111889 #> BC_68847 110884 #> BC_90135 103890 #> BC_4173 99281 #> BC_4469 94003 #> BC_172626 92447 #> BC_31610 86277 #> BC_383329 74354 #> BC_55960 71818 #> BC_62970 55812 #> BC_102160 51865 #> BC_244320 38874 #> BC_400391 33062 #> BC_135438 31484 #> BC_203989 21276 #> BC_81579 19900 #> #> $TopBarcodeCounts$`S18-2` #> S18-2 #> BC_49397 817999 #> BC_91412 443237 #> BC_159570 436654 #> BC_62602 290698 #> BC_70225 275474 #> BC_93485 270096 #> BC_79755 214481 #> BC_4564 165210 #> BC_124796 143216 #> BC_8419 137545 #> BC_58978 134487 #> BC_149935 120452 #> BC_68847 119995 #> BC_90135 116149 #> BC_4173 109548 #> BC_4469 107465 #> BC_172626 97888 #> BC_31610 96129 #> BC_383329 83812 #> BC_55960 82691 #> BC_62970 66186 #> BC_102160 58036 #> BC_244320 44322 #> BC_400391 41088 #> BC_135438 33668 #> BC_203989 25743 #> BC_133178 20613 #> #> $TopBarcodeCounts$`S1-1` #> S1-1 #> BC_79755 993343 #> BC_102160 714207 #> BC_59493 406903 #> BC_23361 207760 #> BC_53234 201367 #> BC_400391 189577 #> BC_142112 174047 #> BC_94511 113449 #> BC_68847 58203 #> BC_142024 56214 #> BC_23241 44498 #> BC_47310 31114 #> BC_9141 30245 #> BC_65104 28467 #> #> $TopBarcodeCounts$`S1-2` #> S1-2 #> BC_79755 1222013 #> BC_102160 808336 #> BC_59493 463756 #> BC_23361 256247 #> BC_53234 244541 #> BC_142112 200263 #> BC_400391 195378 #> BC_94511 143734 #> BC_68847 67285 #> BC_142024 66571 #> BC_23241 54725 #> BC_9141 39529 #> BC_47310 38718 #> BC_5472 35839 #> BC_65104 33854 #> #> $TopBarcodeCounts$`S2-1` #> S2-1 #> BC_248880 2011324 #> BC_65104 1426536 #> BC_9141 1346962 #> BC_1478 850892 #> BC_31610 820495 #> BC_79755 462821 #> BC_388103 216599 #> BC_257382 200090 #> BC_68618 105753 #> BC_102160 76111 #> BC_4531 54809 #> BC_90135 45785 #> #> $TopBarcodeCounts$`S2-2` #> S2-2 #> BC_248880 1182134 #> BC_65104 933706 #> BC_9141 932603 #> BC_1478 561478 #> BC_31610 468327 #> BC_79755 308473 #> BC_388103 133643 #> BC_257382 121541 #> BC_68618 69576 #> BC_102160 49153 #> BC_4531 31316 #> #> $TopBarcodeCounts$`S3-1` #> S3-1 #> BC_79755 2163553 #> BC_142024 565889 #> BC_102160 448714 #> BC_183691 203408 #> BC_59493 163823 #> BC_345889 89546 #> BC_23361 81236 #> BC_70225 56521 #> BC_30946 50953 #> BC_159570 45613 #> #> $TopBarcodeCounts$`S3-2` #> S3-2 #> BC_79755 2486675 #> BC_142024 696785 #> BC_102160 551728 #> BC_183691 257218 #> BC_59493 196076 #> BC_345889 107156 #> BC_23361 93503 #> BC_70225 66396 #> BC_159570 61382 #> BC_30946 59659 #> #> $TopBarcodeCounts$`S4-1` #> S4-1 #> BC_53234 692353 #> BC_58978 342379 #> BC_62602 280987 #> BC_68847 237132 #> BC_140320 223426 #> BC_79755 208335 #> BC_327422 190392 #> BC_36103 181458 #> BC_102160 151606 #> BC_81579 139375 #> BC_118640 132880 #> BC_135438 119051 #> BC_205581 102204 #> BC_91412 92210 #> BC_4564 91814 #> BC_1607 90610 #> BC_159570 66307 #> BC_468335 66180 #> BC_560839 64927 #> BC_229324 62954 #> BC_49397 49062 #> BC_203989 43358 #> BC_124796 38389 #> BC_8419 31905 #> BC_190202 26816 #> #> $TopBarcodeCounts$`S4-2` #> S4-2 #> BC_53234 507873 #> BC_58978 223666 #> BC_62602 181352 #> BC_68847 145969 #> BC_140320 144767 #> BC_79755 139330 #> BC_327422 138287 #> BC_36103 118928 #> BC_81579 96742 #> BC_102160 93245 #> BC_118640 89017 #> BC_135438 70979 #> BC_1607 66226 #> BC_205581 62591 #> BC_4564 61778 #> BC_91412 58781 #> BC_229324 40462 #> BC_560839 38271 #> BC_159570 36888 #> BC_468335 35416 #> BC_49397 34391 #> BC_203989 33235 #> BC_124796 24380 #> BC_8419 21807 #> BC_190202 17786 #> BC_23241 16442 #> #> $TopBarcodeCounts$`S5-1` #> S5-1 #> BC_561471 2123044 #> BC_118640 322753 #> BC_59493 268377 #> BC_1607 259730 #> BC_31610 180017 #> BC_23361 133885 #> BC_159570 48021 #> BC_93485 30112 #> BC_468335 25518 #> BC_81579 24169 #> BC_257382 24078 #> #> $TopBarcodeCounts$`S5-2` #> S5-2 #> BC_561471 1975938 #> BC_118640 290505 #> BC_59493 246168 #> BC_1607 224559 #> BC_31610 172466 #> BC_23361 122852 #> BC_159570 42615 #> BC_93485 30438 #> BC_81579 25799 #> BC_468335 24958 #> BC_257382 24460 #> BC_21013 23108 #> #> $TopBarcodeCounts$`S6-1` #> S6-1 #> BC_79755 1282572 #> BC_8419 1123900 #> BC_124796 996596 #> BC_4564 434228 #> BC_400391 84518 #> BC_31610 44921 #> BC_1478 31005 #> BC_90135 30404 #> BC_388103 28921 #> #> $TopBarcodeCounts$`S6-2` #> S6-2 #> BC_79755 1344442 #> BC_8419 1117015 #> BC_124796 1031266 #> BC_4564 489628 #> BC_400391 102685 #> BC_31610 41582 #> BC_1478 36862 #> BC_90135 34556 #> BC_54442 31930 #> BC_388103 30013 #> #> $TopBarcodeCounts$`S7-1` #> S7-1 #> BC_53234 921435 #> BC_54442 710031 #> BC_142024 472357 #> BC_140320 350076 #> BC_93485 324557 #> BC_124796 197590 #> BC_23544 186585 #> BC_8419 179578 #> BC_324711 166985 #> BC_159570 116366 #> BC_58978 106125 #> BC_90135 92285 #> BC_345889 60307 #> BC_4564 56816 #> BC_102160 52674 #> BC_400391 42639 #> #> $TopBarcodeCounts$`S7-2` #> S7-2 #> BC_53234 886421 #> BC_54442 680000 #> BC_142024 434889 #> BC_140320 331549 #> BC_93485 301678 #> BC_124796 177503 #> BC_23544 166526 #> BC_8419 163482 #> BC_324711 147268 #> BC_159570 104165 #> BC_58978 87248 #> BC_90135 85151 #> BC_4564 55719 #> BC_345889 55079 #> BC_102160 48773 #> BC_400391 38887 #> #> $TopBarcodeCounts$`S8-1` #> S8-1 #> BC_142024 1234236 #> BC_90135 711120 #> BC_58978 347497 #> BC_93485 266947 #> BC_363879 206203 #> BC_4531 151017 #> BC_205581 141328 #> BC_118640 139500 #> BC_79755 119797 #> BC_31610 102078 #> BC_1607 99159 #> BC_82758 77715 #> BC_389078 49526 #> BC_194823 49489 #> BC_251167 47114 #> BC_77209 43860 #> BC_68847 43534 #> BC_102160 35811 #> BC_4173 27438 #> BC_4564 23411 #> #> $TopBarcodeCounts$`S8-2` #> S8-2 #> BC_142024 1098417 #> BC_90135 613893 #> BC_58978 269454 #> BC_93485 215752 #> BC_363879 166500 #> BC_4531 125332 #> BC_205581 115375 #> BC_118640 111091 #> BC_79755 105974 #> BC_1607 80651 #> BC_31610 79449 #> BC_82758 63339 #> BC_194823 47934 #> BC_251167 43484 #> BC_389078 41208 #> BC_68847 40176 #> BC_77209 35932 #> BC_102160 27346 #> BC_4173 24198 #> BC_4564 22430 #> BC_94579 18367 #> #> $TopBarcodeCounts$`S9-1` #> S9-1 #> BC_79755 684599 #> BC_4198 412438 #> BC_81579 222908 #> BC_560839 190175 #> BC_118640 190072 #> BC_345889 174338 #> BC_400391 173891 #> BC_1607 124662 #> BC_319105 106683 #> BC_35704 97669 #> BC_205581 96776 #> BC_90135 55050 #> BC_124796 54709 #> BC_8419 41751 #> BC_142024 41208 #> BC_561471 36418 #> BC_68847 26528 #> BC_19146 26470 #> BC_78912 18539 #> #> $TopBarcodeCounts$`S9-2` #> S9-2 #> BC_79755 678991 #> BC_4198 420103 #> BC_81579 232056 #> BC_118640 198440 #> BC_560839 187828 #> BC_345889 177297 #> BC_400391 169731 #> BC_1607 132863 #> BC_319105 107906 #> BC_35704 100753 #> BC_205581 89183 #> BC_90135 56985 #> BC_124796 51211 #> BC_142024 47093 #> BC_8419 40711 #> BC_561471 35227 #> BC_19146 32473 #> BC_68847 29591 #> BC_78912 20199 #> #>"},{"path":"https://danevass.github.io/bartools/reference/calcReplicateCorr.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate correlation between technical replicates — calcReplicateCorr","title":"Calculate correlation between technical replicates — calcReplicateCorr","text":"Calculate return list correlation paired technical replicates dataset","code":""},{"path":"https://danevass.github.io/bartools/reference/calcReplicateCorr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate correlation between technical replicates — calcReplicateCorr","text":"","code":"calcReplicateCorr(dge, group, corr.thresh = 0.8, return = \"all\")"},{"path":"https://danevass.github.io/bartools/reference/calcReplicateCorr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate correlation between technical replicates — calcReplicateCorr","text":"dge DGEList object containing technical replicates compared group grouping variable metadata containing technical replicate information corr.thresh threshold distinguishing good vs bad correlation technical replicates return values return. One \"good\", \"bad\", \"\"","code":""},{"path":"https://danevass.github.io/bartools/reference/calcReplicateCorr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate correlation between technical replicates — calcReplicateCorr","text":"Returns plot read counts per barcode (row) data frame","code":""},{"path":"https://danevass.github.io/bartools/reference/calcReplicateCorr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate correlation between technical replicates — calcReplicateCorr","text":"","code":"data(test.dge) calcReplicateCorr(test.dge, group = \"group\") #> 10_High_dose 11_Vehicle 12_Vehicle 13_Low_dose 14_Low_dose 15_Low_dose #> 0.9998575 0.9988328 0.9992805 0.9999569 0.9999779 0.9998939 #> 16_Low_dose 17_High_dose 18_Vehicle 1_High_dose 2_High_dose 3_High_dose #> 0.9998965 0.9998268 0.9995628 0.9990491 0.9975029 0.9997352 #> 4_Vehicle 5_Low_dose 6_High_dose 7_Vehicle 8_Vehicle 9_Low_dose #> 0.9975620 0.9999389 0.9994985 0.9997088 0.9992879 0.9997057 #> T0 #> 0.9983838"},{"path":"https://danevass.github.io/bartools/reference/cluster_cormat.html","id":null,"dir":"Reference","previous_headings":"","what":"Cluster correlation matrix — cluster_cormat","title":"Cluster correlation matrix — cluster_cormat","text":"cluster correlation matrix using hierarchical clustering","code":""},{"path":"https://danevass.github.io/bartools/reference/cluster_cormat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cluster correlation matrix — cluster_cormat","text":"","code":"cluster_cormat(cormat)"},{"path":"https://danevass.github.io/bartools/reference/cluster_cormat.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cluster correlation matrix — cluster_cormat","text":"cormat matrix correlation values","code":""},{"path":"https://danevass.github.io/bartools/reference/cluster_cormat.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cluster correlation matrix — cluster_cormat","text":"Returns matrix correlation values columns rows hierarchically clustered","code":""},{"path":"https://danevass.github.io/bartools/reference/collapseReplicates.html","id":null,"dir":"Reference","previous_headings":"","what":"Collapse technical replicates — collapseReplicates","title":"Collapse technical replicates — collapseReplicates","text":"Collapse technical replicates DGEList object mean sum. Modified collapseReplicates function DESeq2 package accept DGEList objects allow collapsing replicated mean sum.","code":""},{"path":"https://danevass.github.io/bartools/reference/collapseReplicates.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Collapse technical replicates — collapseReplicates","text":"","code":"collapseReplicates( object, groupby, run, renameCols = TRUE, show_reps = TRUE, by = \"mean\" )"},{"path":"https://danevass.github.io/bartools/reference/collapseReplicates.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Collapse technical replicates — collapseReplicates","text":"object DGEList object containing raw normalised barcode counts replicate grouping info object metadata groupby character vector containing grouping information. Must long columns object. Can pass metadata column object. run optional, names unique column object. provided, new column runsCollapsed added colData pastes together names run renameCols boolean. whether rename columns returned object using levels grouping factor show_reps boolean. print replicate column names console? collapse replicates mean sum","code":""},{"path":"https://danevass.github.io/bartools/reference/collapseReplicates.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Collapse technical replicates — collapseReplicates","text":"Returns dataframe normalised counts sample","code":""},{"path":"https://danevass.github.io/bartools/reference/collapseReplicates.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Collapse technical replicates — collapseReplicates","text":"","code":"data(test.dge) collapseReplicates(test.dge, groupby = test.dge$samples$group, by = \"mean\") #> $`10_High_dose` #> [1] 3 4 #> #> $`11_Vehicle` #> [1] 5 6 #> #> $`12_Vehicle` #> [1] 7 8 #> #> $`13_Low_dose` #> [1] 9 10 #> #> $`14_Low_dose` #> [1] 11 12 #> #> $`15_Low_dose` #> [1] 13 14 #> #> $`16_Low_dose` #> [1] 15 16 #> #> $`17_High_dose` #> [1] 17 18 #> #> $`18_Vehicle` #> [1] 19 20 #> #> $`1_High_dose` #> [1] 21 22 #> #> $`2_High_dose` #> [1] 23 24 #> #> $`3_High_dose` #> [1] 25 26 #> #> $`4_Vehicle` #> [1] 27 28 #> #> $`5_Low_dose` #> [1] 29 30 #> #> $`6_High_dose` #> [1] 31 32 #> #> $`7_Vehicle` #> [1] 33 34 #> #> $`8_Vehicle` #> [1] 35 36 #> #> $`9_Low_dose` #> [1] 37 38 #> #> $T0 #> [1] 1 2 #> #> An object of class \"DGEList\" #> $samples #> Sample Experiment Group PCR_Replicate Treatment #> 10_High_dose S10-1 test_01 10_High_dose 1 High_dose #> 11_Vehicle S11-1 test_01 11_Vehicle 1 Vehicle #> 12_Vehicle S12-1 test_01 12_Vehicle 1 Vehicle #> 13_Low_dose S13-1 test_01 13_Low_dose 1 Low_dose #> 14_Low_dose S14-1 test_01 14_Low_dose 1 Low_dose #> group lib.size norm.factors #> 10_High_dose 10_High_dose 4114186 1 #> 11_Vehicle 11_Vehicle 2907500 1 #> 12_Vehicle 12_Vehicle 4202337 1 #> 13_Low_dose 13_Low_dose 4513559 1 #> 14_Low_dose 14_Low_dose 4503490 1 #> 14 more rows ... #> #> $counts #> Samples #> Tags 10_High_dose 11_Vehicle 12_Vehicle 13_Low_dose 14_Low_dose 15_Low_dose #> BC_1 0 0 0 0 0 0 #> BC_13 0 0 0 0 0 0 #> BC_99 0 0 0 0 0 0 #> BC_120 0 0 0 0 0 0 #> BC_351 0 0 0 0 0 0 #> Samples #> Tags 16_Low_dose 17_High_dose 18_Vehicle 1_High_dose 2_High_dose #> BC_1 0 0 0 0 0 #> BC_13 0 0 0 0 0 #> BC_99 0 0 0 155 0 #> BC_120 0 0 0 0 0 #> BC_351 0 0 0 0 0 #> Samples #> Tags 3_High_dose 4_Vehicle 5_Low_dose 6_High_dose 7_Vehicle 8_Vehicle #> BC_1 0 0 0 0 0 0 #> BC_13 0 0 0 0 0 0 #> BC_99 0 0 0 0 0 0 #> BC_120 0 0 0 0 0 0 #> BC_351 0 0 0 0 0 0 #> Samples #> Tags 9_Low_dose T0 #> BC_1 0 127 #> BC_13 0 1146 #> BC_99 0 1354 #> BC_120 0 234 #> BC_351 0 0 #> 1634 more rows ... #>"},{"path":"https://danevass.github.io/bartools/reference/compareAbundance.html","id":null,"dir":"Reference","previous_headings":"","what":"Barcode abundance comparison — compareAbundance","title":"Barcode abundance comparison — compareAbundance","text":"Takes dataframe barcode counts, computes mean abundance barcode two specific conditions, line plot conditions","code":""},{"path":"https://danevass.github.io/bartools/reference/compareAbundance.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Barcode abundance comparison — compareAbundance","text":"","code":"compareAbundance(counts, condition, condition_names)"},{"path":"https://danevass.github.io/bartools/reference/compareAbundance.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Barcode abundance comparison — compareAbundance","text":"counts dataframe containing raw counts barcodes condition sample condition interest condition_names names possible conditions experiment","code":""},{"path":"https://danevass.github.io/bartools/reference/compareAbundance.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Barcode abundance comparison — compareAbundance","text":"Returns text file","code":""},{"path":"https://danevass.github.io/bartools/reference/compareAbundance.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Barcode abundance comparison — compareAbundance","text":"compareAbundance Compare barcode abundance 2 conditions using EdgeR","code":""},{"path":"https://danevass.github.io/bartools/reference/compareAbundance.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Barcode abundance comparison — compareAbundance","text":"","code":"data(test.dge) compareAbundance(test.dge, test.dge$samples$group, c(\"T0\",\"10_High_dose\")) #> Error in .subset2(x, i, exact = exact): no such index at level 1"},{"path":"https://danevass.github.io/bartools/reference/countBarcodeReads.html","id":null,"dir":"Reference","previous_headings":"","what":"Count Barcode Reads — countBarcodeReads","title":"Count Barcode Reads — countBarcodeReads","text":"Count number occurences barcode reference library","code":""},{"path":"https://danevass.github.io/bartools/reference/countBarcodeReads.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count Barcode Reads — countBarcodeReads","text":"","code":"countBarcodeReads(bam, bam_index, outdir = \".\", prefix = \"map_barcode_reads\")"},{"path":"https://danevass.github.io/bartools/reference/countBarcodeReads.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count Barcode Reads — countBarcodeReads","text":"bam BAM file mapBarcodeReads containing mapped barcode reads bam_index Path bowtie index barcode reference library. Include index prefix. outdir Optional. Save output directory prefix Optional. Prefix add output file","code":""},{"path":"https://danevass.github.io/bartools/reference/countBarcodeReads.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count Barcode Reads — countBarcodeReads","text":"Returns data-frame containing Barcode ID, count per sample.","code":""},{"path":"https://danevass.github.io/bartools/reference/createBarcodeObj.html","id":null,"dir":"Reference","previous_headings":"","what":"createBarcodeObj — createBarcodeObj","title":"createBarcodeObj — createBarcodeObj","text":"generate DGEList object containing barcode counts sample metadata raw counts files","code":""},{"path":"https://danevass.github.io/bartools/reference/createBarcodeObj.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"createBarcodeObj — createBarcodeObj","text":"","code":"createBarcodeObj(counts.dir = \"counts/\", sampleanno = NULL)"},{"path":"https://danevass.github.io/bartools/reference/createBarcodeObj.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"createBarcodeObj — createBarcodeObj","text":"counts.dir directory containing counts files per sample sampleanno annotation file detailing sample ID groups","code":""},{"path":"https://danevass.github.io/bartools/reference/createBarcodeObj.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"createBarcodeObj — createBarcodeObj","text":"data frame containing barcode counts rows per sample columns.","code":""},{"path":"https://danevass.github.io/bartools/reference/extractBAM.html","id":null,"dir":"Reference","previous_headings":"","what":"extractBAM — extractBAM","title":"extractBAM — extractBAM","text":"Barcode extraction function BAM alignment files","code":""},{"path":"https://danevass.github.io/bartools/reference/extractBAM.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"extractBAM — extractBAM","text":"","code":"extractBAM( bam, barcode_pattern, constant = \"\", yieldSize = 1e+06, outfile = NULL )"},{"path":"https://danevass.github.io/bartools/reference/extractBAM.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"extractBAM — extractBAM","text":"bam BAM file containing barcoded reads barcode_pattern Pattern search reads constant constant region flanking barcode, two constant regions can input character vector. set constant region part barcode_pattern. two constant regions can set e.g. c(\"ACTG\",\"TACG\") yieldSize many lines input file stream pass outfile Optional. path save parsed reads output file","code":""},{"path":"https://danevass.github.io/bartools/reference/extractBAM.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"extractBAM — extractBAM","text":"Returns data-frame containing read ID, 10X cell ID, 10X umi, barcode DNA sequence barcode ID.","code":""},{"path":"https://danevass.github.io/bartools/reference/extractBarcodeReads.html","id":null,"dir":"Reference","previous_headings":"","what":"extractBarcodeReads — extractBarcodeReads","title":"extractBarcodeReads — extractBarcodeReads","text":"Identify extract reads containing DNA barcodes FASTQ BAM alignment file","code":""},{"path":"https://danevass.github.io/bartools/reference/extractBarcodeReads.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"extractBarcodeReads — extractBarcodeReads","text":"","code":"extractBarcodeReads( infile, barcode_pattern = \"[ACTG][ACTG][GC][AT][GC][ACTG][ACTG][AT][GC][AT]\", constant = NULL, yieldSize = 1e+06, outfile = NULL, clean = TRUE, verbose = FALSE, overwrite = TRUE )"},{"path":"https://danevass.github.io/bartools/reference/extractBarcodeReads.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"extractBarcodeReads — extractBarcodeReads","text":"infile Input fastq bam file containing barcoded reads barcode_pattern Pattern search reads constant constant region flanking barcode, two constant regions can input character vector. set constant region part barcode_pattern. two constant regions can set e.g. c(\"ACTG\",\"TACG\") yieldSize many lines input file stream pass outfile Optional. path save parsed reads output file clean Logical. remove previous output files generated extractBarcodeReads current run verbose Logical. print detailed output console overwrite Logical. Overwrite previous output files generated extractBarcodeReads","code":""},{"path":"https://danevass.github.io/bartools/reference/extractBarcodeReads.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"extractBarcodeReads — extractBarcodeReads","text":"Returns data-frame containing read ID, 10X cell ID, 10X umi, barcode DNA sequence barcode ID.","code":""},{"path":"https://danevass.github.io/bartools/reference/extractBarcodeReads.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"extractBarcodeReads — extractBarcodeReads","text":"","code":"test.fq <- system.file(\"extdata\", \"test_extract.fastq.gz\", package = \"bartools\") extractBarcodeReads(infile = test.fq, barcode_pattern = \"ACTG\") #> Error in extractBarcodeReads(infile = test.fq, barcode_pattern = \"ACTG\"): could not find function \"extractBarcodeReads\" test.bam <- system.file(\"extdata\", \"test_extract_anno.bam\", package = \"bartools\") extractBarcodeReads(infile = test.bam, barcode_pattern = \"ACTG\") #> Error in extractBarcodeReads(infile = test.bam, barcode_pattern = \"ACTG\"): could not find function \"extractBarcodeReads\""},{"path":"https://danevass.github.io/bartools/reference/extractFASTQ.html","id":null,"dir":"Reference","previous_headings":"","what":"extractFASTQ — extractFASTQ","title":"extractFASTQ — extractFASTQ","text":"Barcode extraction function raw FASTQ files","code":""},{"path":"https://danevass.github.io/bartools/reference/extractFASTQ.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"extractFASTQ — extractFASTQ","text":"","code":"extractFASTQ( fastq, barcode_pattern, constant = NULL, outfile = NULL, block_size = 1e+06, verbose = FALSE, overwrite = FALSE )"},{"path":"https://danevass.github.io/bartools/reference/extractFASTQ.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"extractFASTQ — extractFASTQ","text":"fastq REQUIRED. Path input fastq file barcode_pattern REQUIRED. Pattern search reads. constant constant region(s) flanking barcode. set constant region part barcode_pattern. two constant regions can set e.g. constant = c(\"ACTG\",\"TACG\") outfile OPTIONAL. path save filtered reads output fasta file read name sequence ID block_size Number reads load memory per chunk. Default = 5e5 verbose Boolean. Show verbose output ShortRead? Default = FALSE. overwrite Boolean. Overwrite output files? Default = FALSE.","code":""},{"path":"https://danevass.github.io/bartools/reference/extractFASTQ.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"extractFASTQ — extractFASTQ","text":"Returns data-frame containing read ID extracted DNA barcode sequence","code":""},{"path":"https://danevass.github.io/bartools/reference/getDominantBarcodes.html","id":null,"dir":"Reference","previous_headings":"","what":"Get dominant barcodes per sample — getDominantBarcodes","title":"Get dominant barcodes per sample — getDominantBarcodes","text":"Takes DGEList dataframe barcode counts, computes percentage abundance barcode within sample returns list barcodes meeting threshold abundance per sample","code":""},{"path":"https://danevass.github.io/bartools/reference/getDominantBarcodes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get dominant barcodes per sample — getDominantBarcodes","text":"","code":"getDominantBarcodes(counts.obj, pct.thresh = 5)"},{"path":"https://danevass.github.io/bartools/reference/getDominantBarcodes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get dominant barcodes per sample — getDominantBarcodes","text":"counts.obj DGEList object dataframe containing raw / normalised counts barcodes pct.thresh percentage abundance threshold call dominant barcodes","code":""},{"path":"https://danevass.github.io/bartools/reference/getDominantBarcodes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get dominant barcodes per sample — getDominantBarcodes","text":"Returns named list containing vectors dominant barcodes per sample","code":""},{"path":"https://danevass.github.io/bartools/reference/getDominantBarcodes.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get dominant barcodes per sample — getDominantBarcodes","text":"getDominantBarcodes get names barcodes given percentage frequency per sample","code":""},{"path":"https://danevass.github.io/bartools/reference/getDominantBarcodes.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get dominant barcodes per sample — getDominantBarcodes","text":"","code":"data(test.dge) getDominantBarcodes(test.dge, pct.thresh = 5) #> $`T0-1` #> [1] \"BC_245016\" \"BC_121651\" #> #> $`T0-2` #> [1] \"BC_245016\" \"BC_121651\" #> #> $`S10-1` #> [1] \"BC_8419\" \"BC_124796\" #> #> $`S10-2` #> [1] \"BC_8419\" \"BC_124796\" #> #> $`S11-1` #> [1] \"BC_53234\" \"BC_205581\" \"BC_90135\" \"BC_172626\" \"BC_58978\" #> #> $`S11-2` #> [1] \"BC_53234\" \"BC_205581\" \"BC_90135\" \"BC_172626\" \"BC_58978\" #> #> $`S12-1` #> [1] \"BC_389078\" \"BC_159570\" \"BC_135438\" \"BC_500780\" \"BC_79755\" #> #> $`S12-2` #> [1] \"BC_389078\" \"BC_159570\" \"BC_135438\" \"BC_500780\" \"BC_79755\" #> #> $`S13-1` #> [1] \"BC_8419\" \"BC_124796\" \"BC_102160\" \"BC_257382\" #> #> $`S13-2` #> [1] \"BC_8419\" \"BC_124796\" \"BC_102160\" \"BC_257382\" #> #> $`S14-1` #> [1] \"BC_90135\" \"BC_388103\" \"BC_31610\" #> #> $`S14-2` #> [1] \"BC_90135\" \"BC_388103\" \"BC_31610\" #> #> $`S15-1` #> [1] \"BC_1478\" \"BC_94579\" \"BC_124796\" \"BC_8419\" #> #> $`S15-2` #> [1] \"BC_1478\" \"BC_94579\" \"BC_124796\" \"BC_8419\" #> #> $`S16-1` #> [1] \"BC_53234\" \"BC_79755\" \"BC_140320\" \"BC_31610\" \"BC_248880\" \"BC_1478\" #> #> $`S16-2` #> [1] \"BC_53234\" \"BC_79755\" \"BC_140320\" \"BC_31610\" \"BC_248880\" \"BC_1478\" #> #> $`S17-1` #> [1] \"BC_183691\" \"BC_90135\" \"BC_400391\" #> #> $`S17-2` #> [1] \"BC_183691\" \"BC_90135\" \"BC_400391\" #> #> $`S18-1` #> [1] \"BC_49397\" \"BC_91412\" \"BC_159570\" \"BC_62602\" \"BC_93485\" \"BC_70225\" #> #> $`S18-2` #> [1] \"BC_49397\" \"BC_91412\" \"BC_159570\" \"BC_62602\" \"BC_70225\" \"BC_93485\" #> #> $`S1-1` #> [1] \"BC_79755\" \"BC_102160\" \"BC_59493\" \"BC_23361\" \"BC_53234\" \"BC_400391\" #> [7] \"BC_142112\" #> #> $`S1-2` #> [1] \"BC_79755\" \"BC_102160\" \"BC_59493\" \"BC_23361\" \"BC_53234\" #> #> $`S2-1` #> [1] \"BC_248880\" \"BC_65104\" \"BC_9141\" \"BC_1478\" \"BC_31610\" \"BC_79755\" #> #> $`S2-2` #> [1] \"BC_248880\" \"BC_65104\" \"BC_9141\" \"BC_1478\" \"BC_31610\" \"BC_79755\" #> #> $`S3-1` #> [1] \"BC_79755\" \"BC_142024\" \"BC_102160\" #> #> $`S3-2` #> [1] \"BC_79755\" \"BC_142024\" \"BC_102160\" \"BC_183691\" #> #> $`S4-1` #> [1] \"BC_53234\" \"BC_58978\" \"BC_62602\" \"BC_68847\" \"BC_140320\" \"BC_79755\" #> #> $`S4-2` #> [1] \"BC_53234\" \"BC_58978\" \"BC_62602\" \"BC_68847\" \"BC_140320\" \"BC_79755\" #> [7] \"BC_327422\" #> #> $`S5-1` #> [1] \"BC_561471\" \"BC_118640\" \"BC_59493\" \"BC_1607\" #> #> $`S5-2` #> [1] \"BC_561471\" \"BC_118640\" \"BC_59493\" \"BC_1607\" \"BC_31610\" #> #> $`S6-1` #> [1] \"BC_79755\" \"BC_8419\" \"BC_124796\" \"BC_4564\" #> #> $`S6-2` #> [1] \"BC_79755\" \"BC_8419\" \"BC_124796\" \"BC_4564\" #> #> $`S7-1` #> [1] \"BC_53234\" \"BC_54442\" \"BC_142024\" \"BC_140320\" \"BC_93485\" #> #> $`S7-2` #> [1] \"BC_53234\" \"BC_54442\" \"BC_142024\" \"BC_140320\" \"BC_93485\" #> #> $`S8-1` #> [1] \"BC_142024\" \"BC_90135\" \"BC_58978\" \"BC_93485\" #> #> $`S8-2` #> [1] \"BC_142024\" \"BC_90135\" \"BC_58978\" \"BC_93485\" #> #> $`S9-1` #> [1] \"BC_79755\" \"BC_4198\" \"BC_81579\" \"BC_560839\" \"BC_118640\" \"BC_345889\" #> [7] \"BC_400391\" #> #> $`S9-2` #> [1] \"BC_79755\" \"BC_4198\" \"BC_81579\" \"BC_118640\" \"BC_560839\" \"BC_345889\" #> [7] \"BC_400391\" #>"},{"path":"https://danevass.github.io/bartools/reference/get_upper_tri.html","id":null,"dir":"Reference","previous_headings":"","what":"Get upper triangle — get_upper_tri","title":"Get upper triangle — get_upper_tri","text":"change lower triangle values mirrored matrix NA","code":""},{"path":"https://danevass.github.io/bartools/reference/get_upper_tri.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get upper triangle — get_upper_tri","text":"","code":"get_upper_tri(cormat)"},{"path":"https://danevass.github.io/bartools/reference/get_upper_tri.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get upper triangle — get_upper_tri","text":"cormat matrix correlation values","code":""},{"path":"https://danevass.github.io/bartools/reference/get_upper_tri.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get upper triangle — get_upper_tri","text":"Returns matrix correlation values lower triangle values changed NA","code":""},{"path":"https://danevass.github.io/bartools/reference/mapBarcodeReads.html","id":null,"dir":"Reference","previous_headings":"","what":"Map Barcode Reads — mapBarcodeReads","title":"Map Barcode Reads — mapBarcodeReads","text":"Map reads containing lintraceR DNA barcode barcode reference library/fasta return CellID:lintracer barcode ID annotations","code":""},{"path":"https://danevass.github.io/bartools/reference/mapBarcodeReads.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Map Barcode Reads — mapBarcodeReads","text":"","code":"mapBarcodeReads( reads, bowtie_index = NULL, mismatches = 2, threads = 4, outdir = getwd(), prefix = \"map_barcode_reads\", cleanup = TRUE, reference_fasta = NULL )"},{"path":"https://danevass.github.io/bartools/reference/mapBarcodeReads.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Map Barcode Reads — mapBarcodeReads","text":"reads Dataframe extractBarcodeReads containing barcode reads bowtie_index Path bowtie index barcode reference library. Include index prefix. mismatches Number mismatches allowed bowtie read mapping threads Number CPU threads bowtie use outdir Optional. save output directory prefix Optional. prefix add output file cleanup Logical. Remove intermediate files generated mapBarcodeReads reference_fasta Optional. Fasta file reference barcode library alignment. Use instead bowtie_index.","code":""},{"path":"https://danevass.github.io/bartools/reference/mapBarcodeReads.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Map Barcode Reads — mapBarcodeReads","text":"Returns data-frame containing read ID, 10X cell ID, 10X umi, lintraceR DNA barcode sequence mapped lintraceR DNA barcode ID.","code":""},{"path":"https://danevass.github.io/bartools/reference/mapBarcodeReads.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Map Barcode Reads — mapBarcodeReads","text":"","code":"load(system.file(\"extdata\", \"test_map.rda\", package = \"bartools\")) #> Warning: cannot open compressed file '', probable reason 'No such file or directory' #> Error in readChar(con, 5L, useBytes = TRUE): cannot open the connection test.ref <- system.file(\"extdata\", \"barcode_lib_reference_test.fasta\", package = \"bartools\") mapBarcodeReads(reads = test.map, reference_fasta = test.ref, outdir = tempdir(), prefix = \"map\") #> Error in mapBarcodeReads(reads = test.map, reference_fasta = test.ref, outdir = tempdir(), prefix = \"map\"): could not find function \"mapBarcodeReads\""},{"path":"https://danevass.github.io/bartools/reference/normaliseCounts.html","id":null,"dir":"Reference","previous_headings":"","what":"Normalise barcode counts — normaliseCounts","title":"Normalise barcode counts — normaliseCounts","text":"Normalise barcode counts within DGEList object","code":""},{"path":"https://danevass.github.io/bartools/reference/normaliseCounts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Normalise barcode counts — normaliseCounts","text":"","code":"normaliseCounts(dge, method = \"cpm\", threshold = 0)"},{"path":"https://danevass.github.io/bartools/reference/normaliseCounts.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Normalise barcode counts — normaliseCounts","text":"dge DGEList object containing raw barcode counts normalised method method normalisation threshold Threshold apply counts normalisation","code":""},{"path":"https://danevass.github.io/bartools/reference/normaliseCounts.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Normalise barcode counts — normaliseCounts","text":"Returns data.frame object containing normalised barcode counts","code":""},{"path":"https://danevass.github.io/bartools/reference/normaliseCounts.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Normalise barcode counts — normaliseCounts","text":"","code":"data(test.dge) normaliseCounts(test.dge, method = \"cpm\", threshold = 0) #> Error in normaliseCounts(test.dge, method = \"cpm\", threshold = 0): method argument must be one of CPM, RLE, TMM, upperquartile or TMMwsp"},{"path":"https://danevass.github.io/bartools/reference/plotAbundanceLines.html","id":null,"dir":"Reference","previous_headings":"","what":"Lineplot of barcode abundances in two conditions — plotAbundanceLines","title":"Lineplot of barcode abundances in two conditions — plotAbundanceLines","text":"Takes dataframe barcode counts, computes median abundance barcode two specific conditions, line plot conditions","code":""},{"path":"https://danevass.github.io/bartools/reference/plotAbundanceLines.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Lineplot of barcode abundances in two conditions — plotAbundanceLines","text":"","code":"plotAbundanceLines( dge, condition, condition_names, plot_type = \"DEG\", keep = 0.9, nbarcode = 10, title = \"\" )"},{"path":"https://danevass.github.io/bartools/reference/plotAbundanceLines.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Lineplot of barcode abundances in two conditions — plotAbundanceLines","text":"dge DGEList object containing raw counts barcodes condition sample condition interest condition_names vector size 2. Gives name two conditions conditions plot plot_type barcodes plotted. 'DEG' plot 10 differentially abundant barcodes. 'counts' plots 5 top highest abundant barcodes condition. 'log2FC' plots barcodes highest absolute log2FC (number barcodes plot given nbarcode) keep percentage highest abundant barcode keep. Rest barcodes filtered used plots. nbarcode number barcodes plot using 'log2FC' plot type. title desired plot title","code":""},{"path":"https://danevass.github.io/bartools/reference/plotAbundanceLines.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Lineplot of barcode abundances in two conditions — plotAbundanceLines","text":"Returns lineplot","code":""},{"path":"https://danevass.github.io/bartools/reference/plotAbundanceLines.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Lineplot of barcode abundances in two conditions — plotAbundanceLines","text":"plotAbundanceLines Lineplot barcode abundances two different experimental settings","code":""},{"path":"https://danevass.github.io/bartools/reference/plotAbundanceLines.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Lineplot of barcode abundances in two conditions — plotAbundanceLines","text":"","code":"data(test.dge) plotAbundanceLines(dge = test.dge, condition = test.dge$samples$group, condition_names = c(\"T0\",\"10_High_dose\"), plot_type = 'counts', title = 'test')"},{"path":"https://danevass.github.io/bartools/reference/plotBARtabFilterQC.html","id":null,"dir":"Reference","previous_headings":"","what":"plotBARtabFilterQC — plotBARtabFilterQC","title":"plotBARtabFilterQC — plotBARtabFilterQC","text":"Plot filtered read percentages BARtab run folder","code":""},{"path":"https://danevass.github.io/bartools/reference/plotBARtabFilterQC.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"plotBARtabFilterQC — plotBARtabFilterQC","text":"","code":"plotBARtabFilterQC( dir = NULL, recursive = T, pattern.log = \"*filter.log\", pattern.value = \"reads\", full.names = T, normalised = F, plot = T, title = \"BARtab Filter QC\" )"},{"path":"https://danevass.github.io/bartools/reference/plotBARtabFilterQC.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"plotBARtabFilterQC — plotBARtabFilterQC","text":"dir directory BARtab successfuly run barcode count datasets recursive Logical. TRUE recurse regex search subdirectories#' pattern.log regex string specify filter stage log files pattern.value grep string log files full.names Logical. Return full names files detected regex search normalised Logical. log10 normalise counts plot Logical. Generate plots. False returns raw data title Optional. title plots.","code":""},{"path":"https://danevass.github.io/bartools/reference/plotBARtabFilterQC.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"plotBARtabFilterQC — plotBARtabFilterQC","text":"Returns plot / dataset containing BARtab Filtering stage QC data","code":""},{"path":"https://danevass.github.io/bartools/reference/plotBARtabMapQC.html","id":null,"dir":"Reference","previous_headings":"","what":"plotBARtabMapQC — plotBARtabMapQC","title":"plotBARtabMapQC — plotBARtabMapQC","text":"Plot mapped read percentages BARtab run folder","code":""},{"path":"https://danevass.github.io/bartools/reference/plotBARtabMapQC.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"plotBARtabMapQC — plotBARtabMapQC","text":"","code":"plotBARtabMapQC( dir = NULL, recursive = T, pattern.log = \"*bowtie.log\", full.names = T, plot = T, title = \"BARtab Mapping QC\" )"},{"path":"https://danevass.github.io/bartools/reference/plotBARtabMapQC.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"plotBARtabMapQC — plotBARtabMapQC","text":"dir directory BARtab successfuly run barcode count datasets recursive Logical. TRUE recurse regex search subdirectories#' pattern.log regex string specify filter stage log files full.names Logical. Return full names files detected regex search plot Logical. Generate plots. False returns raw data title Optional. title plots.","code":""},{"path":"https://danevass.github.io/bartools/reference/plotBARtabMapQC.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"plotBARtabMapQC — plotBARtabMapQC","text":"Returns plot / dataset containing BARtab Filtering stage QC data","code":""},{"path":"https://danevass.github.io/bartools/reference/plotBarcodeBoxplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Boxplot of selected barcodes — plotBarcodeBoxplot","title":"Boxplot of selected barcodes — plotBarcodeBoxplot","text":"Takes dataframe barcode counts, select specific barcodes dataframe boxplot counts","code":""},{"path":"https://danevass.github.io/bartools/reference/plotBarcodeBoxplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Boxplot of selected barcodes — plotBarcodeBoxplot","text":"","code":"plotBarcodeBoxplot(dge.obj, barcodes, condition = NULL)"},{"path":"https://danevass.github.io/bartools/reference/plotBarcodeBoxplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Boxplot of selected barcodes — plotBarcodeBoxplot","text":"dge.obj DGE object containing counts sample information barcodes vector barcodes plotted condition specific metadata condition plotted","code":""},{"path":"https://danevass.github.io/bartools/reference/plotBarcodeBoxplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Boxplot of selected barcodes — plotBarcodeBoxplot","text":"Returns boxplot","code":""},{"path":"https://danevass.github.io/bartools/reference/plotBarcodeBoxplot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Boxplot of selected barcodes — plotBarcodeBoxplot","text":"plotBCBoxplot Boxplot selected barcodes","code":""},{"path":"https://danevass.github.io/bartools/reference/plotBarcodeBoxplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Boxplot of selected barcodes — plotBarcodeBoxplot","text":"","code":"data(test.dge) barcodes <- sample(rownames(test.dge), 10) plotBarcodeBoxplot(test.dge, barcodes) #> multiple barcodes given. No metadata group plotBarcodeBoxplot(test.dge, barcodes, condition = c(\"T0\", \"T0b\")) #> multiple barcodes given. No metadata group"},{"path":"https://danevass.github.io/bartools/reference/plotBarcodeBubble.html","id":null,"dir":"Reference","previous_headings":"","what":"plotBarcodeBubble — plotBarcodeBubble","title":"plotBarcodeBubble — plotBarcodeBubble","text":"Generate proportional bubbleplots raw count object barcodes labelled specified threshold","code":""},{"path":"https://danevass.github.io/bartools/reference/plotBarcodeBubble.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"plotBarcodeBubble — plotBarcodeBubble","text":"","code":"plotBarcodeBubble( counts.obj, labels = T, proportion.cutoff = 10, name = \"Proportional Bubble Plot\" )"},{"path":"https://danevass.github.io/bartools/reference/plotBarcodeBubble.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"plotBarcodeBubble — plotBarcodeBubble","text":"counts.obj dataframe containing raw counts barcodes labels Boolean. print barcode labels? proportion.cutoff barcodes represented percentage within sample threshold labelled name desired plot title","code":""},{"path":"https://danevass.github.io/bartools/reference/plotBarcodeBubble.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"plotBarcodeBubble — plotBarcodeBubble","text":"Returns bubbleplot barcodes represented proportion total pool","code":""},{"path":"https://danevass.github.io/bartools/reference/plotBarcodeBubble.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"plotBarcodeBubble — plotBarcodeBubble","text":"","code":"data(test.counts) #> Warning: data set ‘test.counts’ not found plotBarcodeBubble(test.counts, labels = TRUE, proportion.cutoff = 10) #> Error in as.data.frame(counts.obj): object 'test.counts' not found plotBarcodeBubble(test.counts, labels = FALSE, proportion.cutoff = 10) #> Error in as.data.frame(counts.obj): object 'test.counts' not found"},{"path":"https://danevass.github.io/bartools/reference/plotBarcodeCorrelation.html","id":null,"dir":"Reference","previous_headings":"","what":"Sample to sample correlation — plotBarcodeCorrelation","title":"Sample to sample correlation — plotBarcodeCorrelation","text":"Plot sample correlation barcode sets / samples","code":""},{"path":"https://danevass.github.io/bartools/reference/plotBarcodeCorrelation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sample to sample correlation — plotBarcodeCorrelation","text":"","code":"plotBarcodeCorrelation( counts, method = \"pearson\", upper = T, clustered = T, name = \"Sample Correlation\" )"},{"path":"https://danevass.github.io/bartools/reference/plotBarcodeCorrelation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sample to sample correlation — plotBarcodeCorrelation","text":"counts matrix/dataframe containing raw normalised counts method correlation metric use. Refer stats::cor available options upper Logical. plot upper half matrix clustered Logical. cluster rows columns matrix name title plot","code":""},{"path":"https://danevass.github.io/bartools/reference/plotBarcodeCorrelation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sample to sample correlation — plotBarcodeCorrelation","text":"Returns heatmap sample distances using desired clustering","code":""},{"path":"https://danevass.github.io/bartools/reference/plotBarcodeCorrelation.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sample to sample correlation — plotBarcodeCorrelation","text":"","code":"test.mat <- matrix(rnorm(10*10,mean=0,sd=2), 10, 10) plotBarcodeCorrelation(counts = test.mat, method = \"pearson\")"},{"path":"https://danevass.github.io/bartools/reference/plotBarcodeCounts.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot total counts per barcode in library — plotBarcodeCounts","title":"Plot total counts per barcode in library — plotBarcodeCounts","text":"Simple plot total read counts per barcode library","code":""},{"path":"https://danevass.github.io/bartools/reference/plotBarcodeCounts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot total counts per barcode in library — plotBarcodeCounts","text":"","code":"plotBarcodeCounts(counts, order = F, log10 = F)"},{"path":"https://danevass.github.io/bartools/reference/plotBarcodeCounts.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot total counts per barcode in library — plotBarcodeCounts","text":"counts data.frame barcode count x sample order Logical. Order dataset decreasing abundance log10 Logical. log10 transform data. Adds pseudocount 1","code":""},{"path":"https://danevass.github.io/bartools/reference/plotBarcodeCounts.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot total counts per barcode in library — plotBarcodeCounts","text":"Returns plot read counts per barcode (row) data frame","code":""},{"path":"https://danevass.github.io/bartools/reference/plotBarcodeCounts.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot total counts per barcode in library — plotBarcodeCounts","text":"","code":"data(test.counts) #> Warning: data set ‘test.counts’ not found plotBarcodeCounts(test.counts) #> Error in is.data.frame(x): object 'test.counts' not found plotBarcodeCounts(test.counts, order = TRUE) #> Error in is.data.frame(x): object 'test.counts' not found plotBarcodeCounts(test.counts, order = TRUE, log10 = TRUE) #> Error in is.data.frame(x): object 'test.counts' not found"},{"path":"https://danevass.github.io/bartools/reference/plotBarcodeCumSum.html","id":null,"dir":"Reference","previous_headings":"","what":"Barcode cumulative sum plot of a list of samples against and ordered by sample1 — plotBarcodeCumSum","title":"Barcode cumulative sum plot of a list of samples against and ordered by sample1 — plotBarcodeCumSum","text":"Takes dataframe barcode cpm counts, calculate relative abundance barcode sample. , barcodes samples ordered sample1 decreasing order cumulative sum calculated sample. Cumulative sum plotted sample1.","code":""},{"path":"https://danevass.github.io/bartools/reference/plotBarcodeCumSum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Barcode cumulative sum plot of a list of samples against and ordered by sample1 — plotBarcodeCumSum","text":"","code":"plotBarcodeCumSum(counts, sample1 = NULL, samples = NULL)"},{"path":"https://danevass.github.io/bartools/reference/plotBarcodeCumSum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Barcode cumulative sum plot of a list of samples against and ordered by sample1 — plotBarcodeCumSum","text":"counts Dataframe containing cpm counts sample1 sample compare others samples vector sample names plotted sample1","code":""},{"path":"https://danevass.github.io/bartools/reference/plotBarcodeCumSum.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Barcode cumulative sum plot of a list of samples against and ordered by sample1 — plotBarcodeCumSum","text":"Returns plot cumulative sum read counts per barcode","code":""},{"path":"https://danevass.github.io/bartools/reference/plotBarcodeCumSum.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Barcode cumulative sum plot of a list of samples against and ordered by sample1 — plotBarcodeCumSum","text":"plotBarcodeCumSum Plot cumulative sum barcode cpm counts list samples ordered sample1.","code":""},{"path":"https://danevass.github.io/bartools/reference/plotBarcodeCumSum.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Barcode cumulative sum plot of a list of samples against and ordered by sample1 — plotBarcodeCumSum","text":"","code":"data(test.counts) #> Warning: data set ‘test.counts’ not found plotBarcodeCumSum(counts = test.counts, sample1 = 'T0-1', samples = c('T0-1','S9-1', 'S8-1')) #> Error in plotBarcodeCumSum(counts = test.counts, sample1 = \"T0-1\", samples = c(\"T0-1\", \"S9-1\", \"S8-1\")): object 'test.counts' not found"},{"path":"https://danevass.github.io/bartools/reference/plotBarcodeDistance.html","id":null,"dir":"Reference","previous_headings":"","what":"Sample to sample distances — plotBarcodeDistance","title":"Sample to sample distances — plotBarcodeDistance","text":"Plot sample distances barcode sets / samples","code":""},{"path":"https://danevass.github.io/bartools/reference/plotBarcodeDistance.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sample to sample distances — plotBarcodeDistance","text":"","code":"plotBarcodeDistance( counts, method = \"euclidean\", upper = T, clustered = T, name = \"Sample Distances\" )"},{"path":"https://danevass.github.io/bartools/reference/plotBarcodeDistance.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sample to sample distances — plotBarcodeDistance","text":"counts matrix/dataframe containing raw normalised counts method distance metric use. Refer stats::dist available options upper Logical. plot upper half matrix clustered Logical. cluster rows columns matrix name title plot","code":""},{"path":"https://danevass.github.io/bartools/reference/plotBarcodeDistance.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sample to sample distances — plotBarcodeDistance","text":"Returns heatmap sample distances using desired clustering","code":""},{"path":"https://danevass.github.io/bartools/reference/plotBarcodeDistance.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sample to sample distances — plotBarcodeDistance","text":"","code":"test.mat <- matrix(rnorm(10*10,mean=0,sd=2), 10, 10) plotBarcodeDistance(test.mat)"},{"path":"https://danevass.github.io/bartools/reference/plotBarcodeHeatmap.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot barcode heatmap — plotBarcodeHeatmap","title":"Plot barcode heatmap — plotBarcodeHeatmap","text":"Takes dataframe barcode counts, selects N abundant barcodes per sample. abundant barcodes gathered list heatmap barcodes plotted samples. Stars indicate abundant barcodes per sample.","code":""},{"path":"https://danevass.github.io/bartools/reference/plotBarcodeHeatmap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot barcode heatmap — plotBarcodeHeatmap","text":"","code":"plotBarcodeHeatmap(counts, N, name = \"CPM\", show_bc = FALSE)"},{"path":"https://danevass.github.io/bartools/reference/plotBarcodeHeatmap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot barcode heatmap — plotBarcodeHeatmap","text":"counts Dataframe containing cpm counts N String sample 1 name (used ranked barcode abundance) name String heatmap scale name show_bc Boolean show barcode names heatmap","code":""},{"path":"https://danevass.github.io/bartools/reference/plotBarcodeHeatmap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot barcode heatmap — plotBarcodeHeatmap","text":"Returns heatmap","code":""},{"path":"https://danevass.github.io/bartools/reference/plotBarcodeHeatmap.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot barcode heatmap — plotBarcodeHeatmap","text":"plotBarcodeHeatmap Plot heatmap N abundant barcodes per sample","code":""},{"path":"https://danevass.github.io/bartools/reference/plotBarcodeHeatmap.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot barcode heatmap — plotBarcodeHeatmap","text":"","code":"data(test.counts) #> Warning: data set ‘test.counts’ not found plotBarcodeHeatmap(test.counts, 10,'Counts',FALSE) #> Error in is.data.frame(x): object 'test.counts' not found"},{"path":"https://danevass.github.io/bartools/reference/plotBarcodeHistogram.html","id":null,"dir":"Reference","previous_headings":"","what":"plotBarcodeHistogram — plotBarcodeHistogram","title":"plotBarcodeHistogram — plotBarcodeHistogram","text":"Generate stacked barcode plots showing proportion raw count object.","code":""},{"path":"https://danevass.github.io/bartools/reference/plotBarcodeHistogram.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"plotBarcodeHistogram — plotBarcodeHistogram","text":"","code":"plotBarcodeHistogram( counts.obj, sample = NULL, top = 10, name = \"Barcode Histogram\" )"},{"path":"https://danevass.github.io/bartools/reference/plotBarcodeHistogram.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"plotBarcodeHistogram — plotBarcodeHistogram","text":"counts.obj dataframe containing raw counts barcodes sample desired name sample order barcodes top top n barcodes sample color, barcodes shown grey name desired plot title","code":""},{"path":"https://danevass.github.io/bartools/reference/plotBarcodeHistogram.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"plotBarcodeHistogram — plotBarcodeHistogram","text":"Returns barcode histogram plot barcodes represented proportion total pool","code":""},{"path":"https://danevass.github.io/bartools/reference/plotBarcodeHistogram.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"plotBarcodeHistogram — plotBarcodeHistogram","text":"","code":"data(test.counts) #> Warning: data set ‘test.counts’ not found plotBarcodeHistogram(test.counts, sample = \"T0-1\", top = 10, name = \"Barcode Histogram\") #> Error in sweep(counts.obj, 2, colSums(counts.obj), `/`): object 'test.counts' not found"},{"path":"https://danevass.github.io/bartools/reference/plotBarcodePCA.html","id":null,"dir":"Reference","previous_headings":"","what":"Barcode PCA plot — plotBarcodePCA","title":"Barcode PCA plot — plotBarcodePCA","text":"Takes edgeR DGEList object contining barcode data dataframe barcode cpm counts performs PCA. , plots first two dimensions. Modified DESeq2 package plotPCA function","code":""},{"path":"https://danevass.github.io/bartools/reference/plotBarcodePCA.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Barcode PCA plot — plotBarcodePCA","text":"","code":"plotBarcodePCA( object, intgroup = \"condition\", col = \"group\", ntop = 500, returnData = FALSE, batch = NULL )"},{"path":"https://danevass.github.io/bartools/reference/plotBarcodePCA.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Barcode PCA plot — plotBarcodePCA","text":"object DGEList dataframe containing normalised barcode counts intgroup vector grouping variables interest col color group plot, must one intgroup ntop number top variable genes used PCA calculation returnData Logical. return data.frame PCA calculation? batch metadata category indicating source batch effects. used limma removeBatchEffect function prior PCA","code":""},{"path":"https://danevass.github.io/bartools/reference/plotBarcodePCA.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Barcode PCA plot — plotBarcodePCA","text":"Returns plot first two principal components dataset","code":""},{"path":"https://danevass.github.io/bartools/reference/plotBarcodePCA.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Barcode PCA plot — plotBarcodePCA","text":"plotBarcodePCA Plot first two principal components barcode dataset","code":""},{"path":"https://danevass.github.io/bartools/reference/plotBarcodePCA.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Barcode PCA plot — plotBarcodePCA","text":"","code":"data(test.dge) plotBarcodePCA(test.dge, intgroup = \"group\", ntop = 500, returnData = FALSE, batch = NULL)"},{"path":"https://danevass.github.io/bartools/reference/plotBarcodeRegression.html","id":null,"dir":"Reference","previous_headings":"","what":"plotBarcodeRegression — plotBarcodeRegression","title":"plotBarcodeRegression — plotBarcodeRegression","text":"Generate linear regression scatterplot two sets sample counts.","code":""},{"path":"https://danevass.github.io/bartools/reference/plotBarcodeRegression.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"plotBarcodeRegression — plotBarcodeRegression","text":"","code":"plotBarcodeRegression( dge, samp1 = NULL, samp2 = NULL, title = NULL, trendline = T, trans = NULL, rug = F )"},{"path":"https://danevass.github.io/bartools/reference/plotBarcodeRegression.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"plotBarcodeRegression — plotBarcodeRegression","text":"dge DGEList object containing grouping variable fit linear model samp1 name sample 1. must one colnames(dge) samp2 name sample 2. must one colnames(dge) title desired name output plot trendline Logical. Include linear trendline using stat_smooth() trans ggplot2. continuous scales, name transformation object object . rug Logical. Include geom_rug density information axes?","code":""},{"path":"https://danevass.github.io/bartools/reference/plotBarcodeRegression.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"plotBarcodeRegression — plotBarcodeRegression","text":"Returns scatterplot two samples","code":""},{"path":"https://danevass.github.io/bartools/reference/plotBarcodeRegression.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"plotBarcodeRegression — plotBarcodeRegression","text":"","code":"data(test.dge) plotBarcodeRegression(dge = test.dge, samp1 = \"T0-1\", samp2 = \"T0-2\") #> `geom_smooth()` using formula = 'y ~ x'"},{"path":"https://danevass.github.io/bartools/reference/plotBarcodeTimeseries.html","id":null,"dir":"Reference","previous_headings":"","what":"plotBarcodeTimeseries — plotBarcodeTimeseries","title":"plotBarcodeTimeseries — plotBarcodeTimeseries","text":"Generate proportional timeseries plot raw / normalised barcode count object.","code":""},{"path":"https://danevass.github.io/bartools/reference/plotBarcodeTimeseries.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"plotBarcodeTimeseries — plotBarcodeTimeseries","text":"","code":"plotBarcodeTimeseries(counts.obj, name = \"\", seed = 5, top = 50)"},{"path":"https://danevass.github.io/bartools/reference/plotBarcodeTimeseries.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"plotBarcodeTimeseries — plotBarcodeTimeseries","text":"counts.obj dataframe containing raw counts barcodes name desired plot title seed RNG seed top number top barcodes per sample plot","code":""},{"path":"https://danevass.github.io/bartools/reference/plotBarcodeTimeseries.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"plotBarcodeTimeseries — plotBarcodeTimeseries","text":"Returns bubbleplot barcodes represented proportion total pool","code":""},{"path":"https://danevass.github.io/bartools/reference/plotBarcodeTimeseries.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"plotBarcodeTimeseries — plotBarcodeTimeseries","text":"","code":"data(test.counts) #> Warning: data set ‘test.counts’ not found plotBarcodeTimeseries(test.counts[,1:4], name = \"Proportional Timeseries Plot\", seed = 5, top = 10) #> Error in as.data.frame(counts.obj): object 'test.counts' not found"},{"path":"https://danevass.github.io/bartools/reference/plotDetectedBarcodes.html","id":null,"dir":"Reference","previous_headings":"","what":"plotDetectedBarcodes — plotDetectedBarcodes","title":"plotDetectedBarcodes — plotDetectedBarcodes","text":"Plot total number barcodes detected sample","code":""},{"path":"https://danevass.github.io/bartools/reference/plotDetectedBarcodes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"plotDetectedBarcodes — plotDetectedBarcodes","text":"","code":"plotDetectedBarcodes( counts, percentile = 0.95, plot = T, sample.order = NULL, group = NULL, title = NULL )"},{"path":"https://danevass.github.io/bartools/reference/plotDetectedBarcodes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"plotDetectedBarcodes — plotDetectedBarcodes","text":"counts DGEList dataframe containing raw normalised barcode counts percentile desired percentile value. 95th percentile default plot Logical. plot data instead returning counts table. sample.order desired ordering samples plot group grouping field dgelist$samples color samples title desired plot title","code":""},{"path":"https://danevass.github.io/bartools/reference/plotDetectedBarcodes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"plotDetectedBarcodes — plotDetectedBarcodes","text":"Returns histogram plot number detected barcodes per sample.","code":""},{"path":"https://danevass.github.io/bartools/reference/plotDetectedBarcodes.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"plotDetectedBarcodes — plotDetectedBarcodes","text":"","code":"data(test.dge) plotDetectedBarcodes(test.dge, percentile = .95) #> Warning: Use of `percentile.df$Sample` is discouraged. #> ℹ Use `Sample` instead. #> Warning: Use of `percentile.df$Barcodes` is discouraged. #> ℹ Use `Barcodes` instead. #> Warning: Use of `percentile.df$Barcodes` is discouraged. #> ℹ Use `Barcodes` instead. #> Warning: Use of `percentile.df$Barcodes` is discouraged. #> ℹ Use `Barcodes` instead. #> Warning: Use of `percentile.df$Sample` is discouraged. #> ℹ Use `Sample` instead. plotDetectedBarcodes(test.dge, plot = FALSE) #> Sample Barcodes #> 1 T0-1 593 #> 2 T0-2 590 #> 3 S10-1 3 #> 4 S10-2 4 #> 5 S11-1 19 #> 6 S11-2 20 #> 7 S12-1 14 #> 8 S12-2 14 #> 9 S13-1 10 #> 10 S13-2 11 #> 11 S14-1 6 #> 12 S14-2 6 #> 13 S15-1 7 #> 14 S15-2 7 #> 15 S16-1 19 #> 16 S16-2 20 #> 17 S17-1 5 #> 18 S17-2 6 #> 19 S18-1 27 #> 20 S18-2 27 #> 21 S1-1 14 #> 22 S1-2 15 #> 23 S2-1 12 #> 24 S2-2 11 #> 25 S3-1 10 #> 26 S3-2 10 #> 27 S4-1 25 #> 28 S4-2 26 #> 29 S5-1 11 #> 30 S5-2 12 #> 31 S6-1 9 #> 32 S6-2 10 #> 33 S7-1 16 #> 34 S7-2 16 #> 35 S8-1 20 #> 36 S8-2 21 #> 37 S9-1 19 #> 38 S9-2 19"},{"path":"https://danevass.github.io/bartools/reference/plotLibraryCumSum.html","id":null,"dir":"Reference","previous_headings":"","what":"Cumulative sum plot of barcode abundances in a reference library — plotLibraryCumSum","title":"Cumulative sum plot of barcode abundances in a reference library — plotLibraryCumSum","text":"Takes dataframe barcode counts, computes median abundance barcode two specific conditions, line plot conditions","code":""},{"path":"https://danevass.github.io/bartools/reference/plotLibraryCumSum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cumulative sum plot of barcode abundances in a reference library — plotLibraryCumSum","text":"","code":"plotLibraryCumSum(barcodes, samplename = \"Library\", cutoff = 10)"},{"path":"https://danevass.github.io/bartools/reference/plotLibraryCumSum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cumulative sum plot of barcode abundances in a reference library — plotLibraryCumSum","text":"barcodes dataframe containing raw counts barcodes samplename sample condition interest cutoff rowsum cutoff defining rows keep","code":""},{"path":"https://danevass.github.io/bartools/reference/plotLibraryCumSum.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cumulative sum plot of barcode abundances in a reference library — plotLibraryCumSum","text":"Returns cumulative sum plot desired barcode library sample","code":""},{"path":"https://danevass.github.io/bartools/reference/plotLibraryCumSum.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Cumulative sum plot of barcode abundances in a reference library — plotLibraryCumSum","text":"plotLibraryCumSum Plot cumulative sum barcode abundances barcode library","code":""},{"path":"https://danevass.github.io/bartools/reference/plotLibraryDiversity.html","id":null,"dir":"Reference","previous_headings":"","what":"Lineplot of barcode abundances in two conditions — plotLibraryDiversity","title":"Lineplot of barcode abundances in two conditions — plotLibraryDiversity","text":"Takes dataframe barcode counts, computes plots abundance barcode library sample","code":""},{"path":"https://danevass.github.io/bartools/reference/plotLibraryDiversity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Lineplot of barcode abundances in two conditions — plotLibraryDiversity","text":"","code":"plotLibraryDiversity(barcodes, samplename = \"Library\", cutoff = 10, skew = F)"},{"path":"https://danevass.github.io/bartools/reference/plotLibraryDiversity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Lineplot of barcode abundances in two conditions — plotLibraryDiversity","text":"barcodes dataframe containing raw counts barcodes samplename sample condition interest cutoff rowsum cutoff defining rows keep skew Logical. Calculate plot library skew ratio (90th percentile / 10th percentile)","code":""},{"path":"https://danevass.github.io/bartools/reference/plotLibraryDiversity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Lineplot of barcode abundances in two conditions — plotLibraryDiversity","text":"Returns barcode library frequency plot","code":""},{"path":"https://danevass.github.io/bartools/reference/plotLibraryDiversity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Lineplot of barcode abundances in two conditions — plotLibraryDiversity","text":"plotLibraryDiversity Lineplot barcode abundances two different experimental settings","code":""},{"path":"https://danevass.github.io/bartools/reference/plotOrderedBubble.html","id":null,"dir":"Reference","previous_headings":"","what":"plotOrderedBubble — plotOrderedBubble","title":"plotOrderedBubble — plotOrderedBubble","text":"Generate ordered proportional bubbleplots raw count object barcodes labelled specified threshold","code":""},{"path":"https://danevass.github.io/bartools/reference/plotOrderedBubble.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"plotOrderedBubble — plotOrderedBubble","text":"","code":"plotOrderedBubble( counts.obj, labels = T, name = \"Proportional Bubble Plot\", orderSample = NULL, proportion.cutoff = 10, colorDominant = FALSE )"},{"path":"https://danevass.github.io/bartools/reference/plotOrderedBubble.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"plotOrderedBubble — plotOrderedBubble","text":"counts.obj dataframe containing raw counts barcodes. assumes barcodes rownames. labels logical. Print barcode labels name desired plot title orderSample name sample order proportion.cutoff barcodes represented percentage within sample threshold labelled colorDominant color clones frequency proportion.cutoff. Others colored grey","code":""},{"path":"https://danevass.github.io/bartools/reference/plotOrderedBubble.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"plotOrderedBubble — plotOrderedBubble","text":"Returns bubbleplot barcodes represented proportion total pool","code":""},{"path":"https://danevass.github.io/bartools/reference/plotOrderedBubble.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"plotOrderedBubble — plotOrderedBubble","text":"","code":"data(test.counts) #> Warning: data set ‘test.counts’ not found plotOrderedBubble(test.counts, orderSample = \"T0-1\") #> Error in methods::is(counts): object 'counts' not found"},{"path":"https://danevass.github.io/bartools/reference/plotReadCounts.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot sample read counts — plotReadCounts","title":"Plot sample read counts — plotReadCounts","text":"Simple plot total read counts per sample","code":""},{"path":"https://danevass.github.io/bartools/reference/plotReadCounts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot sample read counts — plotReadCounts","text":"","code":"plotReadCounts( counts, group = NULL, log10 = FALSE, legend = TRUE, order = TRUE )"},{"path":"https://danevass.github.io/bartools/reference/plotReadCounts.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot sample read counts — plotReadCounts","text":"counts data.frame barcode count x sample group character vector containing grouping information. Must equal number columns counts. Can pass metadata column DGEList object. log10 Boolean. log10 transform output? legend Boolean. Include legend? order Boolean. Order samples group?","code":""},{"path":"https://danevass.github.io/bartools/reference/plotReadCounts.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot sample read counts — plotReadCounts","text":"Returns plot read counts per column (sample) data frame","code":""},{"path":"https://danevass.github.io/bartools/reference/plotReadCounts.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot sample read counts — plotReadCounts","text":"","code":"data(test.counts) #> Warning: data set ‘test.counts’ not found plotReadCounts(test.dge$counts, group = test.dge$samples$Group)"},{"path":"https://danevass.github.io/bartools/reference/processBarcodeLibrary.html","id":null,"dir":"Reference","previous_headings":"","what":"processBarcodeLibrary — processBarcodeLibrary","title":"processBarcodeLibrary — processBarcodeLibrary","text":"process barcode reference file raw sequencing datasets","code":""},{"path":"https://danevass.github.io/bartools/reference/processBarcodeLibrary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"processBarcodeLibrary — processBarcodeLibrary","text":"","code":"processBarcodeLibrary( file = NULL, samplename = \"Barcode\", cutoff = 10, header = FALSE, outdir = tempdir() )"},{"path":"https://danevass.github.io/bartools/reference/processBarcodeLibrary.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"processBarcodeLibrary — processBarcodeLibrary","text":"file path starcode output barcode library samplename name reference library. prefixed barcode reference cutoff rowsum cutoff defining barcodes keep reference library header Logical. input file contain header line outdir Desired output directory save library files","code":""},{"path":"https://danevass.github.io/bartools/reference/processBarcodeLibrary.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"processBarcodeLibrary — processBarcodeLibrary","text":"returns data frame containing barcode read count reference","code":""},{"path":"https://danevass.github.io/bartools/reference/processBarcodeLibrary.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"processBarcodeLibrary — processBarcodeLibrary","text":"","code":"load(system.file(\"extdata\", \"test_raw_lib.rda\", package = \"bartools\")) #> Warning: cannot open compressed file '', probable reason 'No such file or directory' #> Error in readChar(con, 5L, useBytes = TRUE): cannot open the connection processBarcodeLibrary(file = test.raw.lib, samplename = \"Barcode\", outdir = tempdir()) #> Error in processBarcodeLibrary(file = test.raw.lib, samplename = \"Barcode\", outdir = tempdir()): object 'test.raw.lib' not found"},{"path":"https://danevass.github.io/bartools/reference/test.counts.html","id":null,"dir":"Reference","previous_headings":"","what":"Test barcode counts — test.counts","title":"Test barcode counts — test.counts","text":"dataset containing normalised count information 1639 hypothetical barcodes 38 hypothetical samples","code":""},{"path":"https://danevass.github.io/bartools/reference/test.counts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Test barcode counts — test.counts","text":"","code":"test.counts"},{"path":"https://danevass.github.io/bartools/reference/test.counts.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Test barcode counts — test.counts","text":"data frame 1639 rows 38 variables: T0-1 sample T0, replicate 1 T0-2 sample T0, replicate 2","code":""},{"path":"https://danevass.github.io/bartools/reference/test.dge.html","id":null,"dir":"Reference","previous_headings":"","what":"Test barcode DGEList object — test.dge","title":"Test barcode DGEList object — test.dge","text":"DGEList object containing raw count information 1639 hypothetical barcodes 38 hypothetical samples","code":""},{"path":"https://danevass.github.io/bartools/reference/test.dge.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Test barcode DGEList object — test.dge","text":"","code":"test.dge"},{"path":"https://danevass.github.io/bartools/reference/test.dge.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Test barcode DGEList object — test.dge","text":"DGEList object containing 1639 rows 38 variables:","code":""},{"path":"https://danevass.github.io/bartools/reference/thresholdCounts.html","id":null,"dir":"Reference","previous_headings":"","what":"Threshold counts — thresholdCounts","title":"Threshold counts — thresholdCounts","text":"Threshold dataframe given level return number barcodes meeting threshold sample.","code":""},{"path":"https://danevass.github.io/bartools/reference/thresholdCounts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Threshold counts — thresholdCounts","text":"","code":"thresholdCounts(df, threshold = 20, plot = FALSE)"},{"path":"https://danevass.github.io/bartools/reference/thresholdCounts.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Threshold counts — thresholdCounts","text":"df Dataframe thresholded. threshold threshold use. 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