Description Usage Arguments Details Value Author(s) See Also Examples
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.
1 2 | plotSummaryGenes(threshold1, threshold2, percentage1, percentage2,
badCoverageGenes, output)
|
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 |
percentage2 |
Float, defining the percentage of samples that have to feature a coverage of at least |
badCoverageGenes |
Data frame object, return value of function |
output |
The folder to write the output file into. If this argument is an empty string, the plot is printed on the screen. |
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.
No value is returned.
Sarah Sandmann <sarah.sandmann@uni-muenster.de>
BadRegionFinder
, determineCoverage
, determineCoverageQuality
, determineRegionsOfInterest
, reportBadRegionsSummary
, reportBadRegionsDetailed
, reportBadRegionsGenes
, plotSummary
, plotDetailed
, determineQuantiles
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | 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)
|
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