BadRegionFinder (version 1.0.0)

plotSummaryGenes: Plots a summary of the coverage quality concerning the genes only

Description

The function plotSummaryGenes provides a possibility to visualize the output of reportBadRegionsGenes. A barplot is returned, visualizing the percent of each gene that falls into each category of coverage quality. The plot thereby serves to quickly distinguish well from bad covered genes.

Usage

plotSummaryGenes(threshold1, threshold2, percentage1, percentage2,  badCoverageGenes, output)

Arguments

threshold1

Integer, threshold defining the number of reads that have to be registered for a sample that its coverage is classified as acceptable.

threshold2

Integer, threshold defining the number of reads that have to be registered for a sample that its coverage is classified as good.

percentage1

Float, defining the percentage of samples that have to feature a coverage of at least threshold1 so that the position is classified as acceptably covered.

percentage2

Float, defining the percentage of samples that have to feature a coverage of at least threshold2 so that the position is classified as well covered.

badCoverageGenes

Data frame object, return value of function reportBadRegionsGenes.

output

The folder to write the output file into. If this argument is an empty string, the plot is printed on the screen.

Value

No value is returned.

Details

The function plotSummaryGenes serves to summarize the previously determined coverage quality in a visual way concerning the genes only.

For every gene either one or two stacked bars are plotted. If a gene is covered, but it was not originally targeted, a bar is plotted containing the following color code: black - bad region off target; dark gray - acceptable region off target; light gray - good region off target. If a gene was originally targeted, a bar is plotted containing the following color code: red - bad region on target; yellow - acceptable region on target; green - good region on target.

See Also

BadRegionFinder, determineCoverage, determineCoverageQuality, determineRegionsOfInterest, reportBadRegionsSummary, reportBadRegionsDetailed, reportBadRegionsGenes, plotSummary, plotDetailed, determineQuantiles

Examples

library("BSgenome.Hsapiens.UCSC.hg19")
threshold1 <- 20
threshold2 <- 100
percentage1 <- 0.80
percentage2 <- 0.90
sample_file <- system.file("extdata", "SampleNames.txt", 
                           package = "BadRegionFinder")
samples <- read.table(sample_file)
bam_input <- system.file("extdata", package = "BadRegionFinder")
output <- system.file("extdata", package = "BadRegionFinder")
target_regions <- system.file("extdata", "targetRegions.bed",
                              package = "BadRegionFinder")
targetRegions <- read.table(target_regions, header = FALSE,
                            stringsAsFactors = FALSE)

coverage_summary <- determineCoverage(samples, bam_input, targetRegions, output,
                                      TRonly = TRUE)
coverage_indicators <- determineCoverageQuality(threshold1, threshold2,
                                                percentage1, percentage2,
                                                coverage_summary)
badCoverageSummary <- reportBadRegionsSummary(threshold1, threshold2,
                                              percentage1, percentage2,
                                              coverage_indicators, "", output)
badCoverageGenes <- reportBadRegionsGenes(threshold1, threshold2, percentage1,
                                          percentage2, badCoverageSummary, 
                                          output)
plotSummaryGenes(threshold1, threshold2, percentage1, percentage2,
                 badCoverageGenes, output)