PlotBootstrapDistributions {CNVPanelizer} | R Documentation |
Plots the generated bootstrap distribution as violin plots. Genes showing significant values are marked in a different color.
PlotBootstrapDistributions(bootList, reportTables, outputFolder = getwd(), sampleNames = NULL, save = FALSE, scale = 10)
bootList |
List of bootstrapped read counts for each sample data |
reportTables |
List of report tables for each sample data |
outputFolder |
Path to the folder where the data plots will be created |
sampleNames |
List with sample names |
save |
Boolean to save the plots to the output folder |
scale |
Numeric scale factor |
A list with ggplot2 objects.
Thomas Wolf, Cristiano Oliveira
data(sampleReadCounts) data(referenceReadCounts) ## Gene names should be same size as row columns geneNames <- row.names(referenceReadCounts) ampliconNames <- NULL normalizedReadCounts <- CombinedNormalizedCounts(sampleReadCounts, referenceReadCounts, ampliconNames = ampliconNames) # After normalization data sets need to be splitted again to perform bootstrap samplesNormalizedReadCounts = normalizedReadCounts["samples"][[1]] referenceNormalizedReadCounts = normalizedReadCounts["reference"][[1]] # Should be used values above 10000 replicates <- 10 # Perform the bootstrap based analysis bootList <- BootList(geneNames, samplesNormalizedReadCounts, referenceNormalizedReadCounts, replicates = replicates) backgroundNoise <- Background(geneNames, samplesNormalizedReadCounts, referenceNormalizedReadCounts, bootList, replicates = replicates) reportTables <- ReportTables(geneNames, samplesNormalizedReadCounts, referenceNormalizedReadCounts, bootList, backgroundNoise) PlotBootstrapDistributions(bootList, reportTables, save = FALSE)