| 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)