Makes plots for every region while summarizing the annotation

Source: R/plotRegionCoverage.R

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.

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 matchGenes 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.

Author

Andrew Jaffe, Leonardo Collado-Torres

Examples

## Load data
library("derfinder")

## Annotate regions, first two regions only
regions <- genomeRegions$regions[1:2]
annotatedRegions <- annotateRegions(
    regions = regions,
    genomicState = genomicState$fullGenome, minoverlap = 1
)
#> 2023-05-07 05:31:07.909464 annotateRegions: counting
#> 2023-05-07 05:31:08.001931 annotateRegions: annotating

## Find nearest annotation with bumphunter::matchGenes()
library("bumphunter")
library("TxDb.Hsapiens.UCSC.hg19.knownGene")
genes <- annotateTranscripts(txdb = TxDb.Hsapiens.UCSC.hg19.knownGene)
#> No annotationPackage supplied. Trying org.Hs.eg.db.
#> Getting TSS and TSE.
#> Getting CSS and CSE.
#> Getting exons.
#> Annotating genes.
nearestAnnotation <- matchGenes(x = regions, subject = genes)

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

## Assign chr lengths using hg19 information
library("GenomicRanges")
seqlengths(regions) <- seqlengths(getChromInfoFromUCSC("hg19",
    as.Seqinfo = TRUE
))[
    mapSeqlevels(names(seqlengths(regions)), "UCSC")
]

## Get the region coverage
regionCov <- getRegionCoverage(fullCov = fullCov, regions = regions)
#> extendedMapSeqlevels: sequence names mapped from NCBI to UCSC for species homo_sapiens
#> 2023-05-07 05:31:37.883024 getRegionCoverage: processing chr21
#> 2023-05-07 05:31:37.926655 getRegionCoverage: done processing chr21
#
## 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
)
#> 2023-05-07 05:31:38.096363 plotRegionCoverage: extracting Tx info
#> 2023-05-07 05:31:40.659734 plotRegionCoverage: getting Tx plot info



if (FALSE) {
## 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()
}