plotDistCountAnalysis: Creates a boxplot to see the distribution of read counts in...

In Mathelab/ALTRE: Workflow for Identifying Regulatory Elements from Chromatin Accessibility Assay Data

Description

Takes the rlog transformation of the RRKM (Reads Per Kilobase of transcript per Million) of the read counts of type-specific and shared regulatory regions and plots the distribution of those read counts in all sample types analyzed in the workflow.

Usage

plotDistCountAnalysis(analysisresults, counts, viewer = TRUE,
  palette = c("#C71585", "#d3d3d3", "#00E5EE", "#000080"),
  xlabelsize = "13px", ylabel = "log2(FPKM)", ylabelsize = "13px",
  maintitle = "Distribution of Normalized Counts (peaks types determine by intensity)",
  maintitlesize = "20px", xlabel = NULL)

Arguments

analysisresults	output generated from countanalysis() then categAltrePeaks()
counts	output generated from getCounts()
viewer	whether the plot should be displayed in the RStudio viewer or in Shiny/Knittr
palette	choose an RColorBrewer palette ("Set1", "Set2", "Set3", "Pastel1", "Pastel2", "Paired", etc.) or submit a vector of colors
xlabelsize	size of xlabel (default, 15px)
ylabel	label for y-axis (default, "Observations")
ylabelsize	size of ylabel (default, 15px)
maintitle	main title (default, "Distribution of Normalized Counts")
maintitlesize	main title size (default, 20px)
xlabel	label for x-axis (default, sample names)

Value

a highcharter object

Examples

## Not run: 
csvfile <- loadCSVFile("DNaseEncodeExample.csv")
samplePeaks <- loadBedFiles(csvfile)
consensusPeaks <- getConsensusPeaks(samplepeaks = samplePeaks,
minreps = 2)
TSSannot <- getTSS()
consensusPeaksAnnotated <- combineAnnotatePeaks(conspeaks = consensusPeaks,
TSS = TSSannot,
merge = TRUE,
regionspecific = TRUE,
distancefromTSSdist = 1500,
distancefromTSSprox = 1000)
consensusPeaksCounts <- getCounts(annotpeaks = consensusPeaksAnnotated,
                                 sampleinfo = csvfile,
                                 reference = 'SAEC',
                                 chrom = 'chr21')
alteredPeaks <- countanalysis(counts = consensusPeaksCounts,
pval = 0.01,
lfcvalue = 1)
alteredPeaksCategorized <- categAltrePeaks(alteredPeaks,
                                          lfctypespecific = 1.5,
                                          lfcshared = 1.2,
                                          pvaltypespecific = 0.01,
                                          pvalshared = 0.05)
plotDistCountAnalysis(analysisresults = alteredPeaksCategorized, counts = consensusPeaksCounts)

## End(Not run)

Mathelab/ALTRE documentation built on May 7, 2019, 3:41 p.m.