plotRegionCoverage: Makes plots for every region while summarizing the annotation

Description

This function takes the regions found in calculatePvalues and assigns them genomic states contructed with makeGenomicState. The main workhorse functions are countOverlaps and findOverlaps. For an alternative plot check plotCluster which is much slower and we recommend it's use only after quickly checking the results with this function.

Usage

plotRegionCoverage(regions, regionCoverage, groupInfo, nearestAnnotation,
  annotatedRegions, txdb = NULL, whichRegions = seq_len(min(100,
  length(regions))), colors = NULL, scalefac = 32, ask = interactive(),
  ylab = "Coverage", verbose = TRUE)

Arguments

regions

The $regions output from calculatePvalues.

regionCoverage

The output from getRegionCoverage used on regions.

groupInfo

A factor specifying the group membership of each sample. It will be used to color the samples by group.

nearestAnnotation

The output from annotateNearest used on regions.

annotatedRegions

The output from annotateRegions used on regions.

txdb

A TxDb object. If specified, transcript annotation will be extracted from this object and used to plot the transcripts.

whichRegions

An integer vector with the index of the regions to plot.

colors

If NULL then brewer.pal with the 'Dark2' color scheme is used.

scalefac

The parameter used in preprocessCoverage.

ask

If TRUE then the user is prompted before each plot is made.

ylab

The name of the of the Y axis.

verbose

If TRUE basic status updates will be printed along the way.

Value

A plot for every region showing the coverage of each sample at each base of the region as well as the summarized annotation information.

Examples

Run this code

## Load data
library('derfinder')

## Annotate regions, first two regions only
regions <- genomeRegions$regions[1:2]
annotatedRegions <- annotateRegions(regions = regions,
    genomicState = genomicState$fullGenome, minoverlap = 1)

## Find nearest annotation with bumphunter::matchGenes()
library('bumphunter')
library('TxDb.Hsapiens.UCSC.hg19.knownGene')
genes <- annotateTranscripts(txdb = TxDb.Hsapiens.UCSC.hg19.knownGene)
nearestAnnotation <- matchGenes(x = regions, subject = genes)

## Obtain fullCov object
fullCov <- list('21'=genomeDataRaw$coverage)

## Assign chr lengths using hg19 information
library('GenomicRanges')
data(hg19Ideogram, package = 'biovizBase', envir = environment())
seqlengths(regions) <- seqlengths(hg19Ideogram)[names(seqlengths(regions))]

## Get the region coverage
regionCov <- getRegionCoverage(fullCov=fullCov, regions=regions)
## Make plots for the regions
plotRegionCoverage(regions=regions, regionCoverage=regionCov,
    groupInfo=genomeInfo$pop, nearestAnnotation=nearestAnnotation,
    annotatedRegions=annotatedRegions, whichRegions=1:2)

## Re-make plots with transcript information
plotRegionCoverage(regions=regions, regionCoverage=regionCov,
    groupInfo=genomeInfo$pop, nearestAnnotation=nearestAnnotation,
    annotatedRegions=annotatedRegions, whichRegions=1:2,
    txdb = TxDb.Hsapiens.UCSC.hg19.knownGene)

## If you prefer, you can save the plots to a pdf file
pdf('ders.pdf', h = 6, w = 9)
plotRegionCoverage(regions=regions, regionCoverage=regionCov,
    groupInfo=genomeInfo$pop, nearestAnnotation=nearestAnnotation,
    annotatedRegions=annotatedRegions, whichRegions=1:2,
    txdb = TxDb.Hsapiens.UCSC.hg19.knownGene, ask = FALSE)
dev.off()

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