plotCorrelation: plot Correlations of multiple samples

In ATACseqQC: ATAC-seq Quality Control

Description

plot PCA or heatmap for multiple bamfiles. The correlation is calculated by the counts in promoter regions.

Usage

plotCorrelation(
  objs,
  txs,
  seqlev = intersect(seqlevels(objs[[1]]), seqlevels(txs)),
  upstream = 2000,
  downstream = 500,
  type = c("heatmap", "PCA"),
  ...
)

Arguments

objs	an object of GAlignmentsList
txs	GRanges of transcripts
seqlev	A vector of characters indicates the sequence levels.
upstream	numeric(1) or integer(1). Start position of promoter. Default is 2000
downstream	numeric(1) or integer(1). End position of promoter. Default is 500
type	Figure type, heatmap or PCA plot.
...	parameters could be passed to downstream functions such as plot for pca or heatmap for heatmap.

Details

The correlation will be calculated by the correlation of insertion sites within promoter regions. Even the sequencing is paired-end, please treat it as single ends.

Value

A invisible object of GRanges with counts

Author(s)

Jianhong Ou

Examples

library(GenomicRanges)
library(GenomicAlignments)
path <- system.file("extdata", package="ATACseqQC", mustWork=TRUE)
bamfiles <- dir(path, "*.bam$", full.name=TRUE)
gals <- lapply(bamfiles, function(bamfile){
               readBamFile(bamFile=bamfile, tag=character(0), 
                           which=GRanges("chr1", IRanges(1, 1e6)), 
                           asMates=FALSE)
        })
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
txs <- transcripts(TxDb.Hsapiens.UCSC.hg19.knownGene)
plotCorrelation(GAlignmentsList(gals), txs, seqlev="chr1")

ATACseqQC documentation built on Nov. 8, 2020, 11 p.m.