windowCounts: Summarize read counts in a sliding window

In ChIPseqR: Identifying Protein Binding Sites in High-Throughput Sequencing Data

Description

Read counts are summarized in a sliding window of variable size with variable overlap between windows.

Usage

windowCounts(reads, window = 1000, shift = 500, method = sum)

Arguments

reads	Numeric vector of read counts.
window	Width of window.
shift	Distance between consecutive window start positions.
method	Function used to produce a summary for each window. It should accept a single numeric vector as argument.

Value

If method returns a single value a vector of all window summaries is returned, otherwise the return value is a list with one component for each window.

Author(s)

Peter Humburg

Examples

## generate some very simple artificial read data
set.seed(1)
fwd <- sample(c(50:70, 250:270), 30, replace=TRUE)
rev <- sample(c(197:217, 347:417), 30, replace=TRUE)
## create data.frame with read positions as input to strandPileup
reads <- data.frame(chromosome="chr1", position=c(fwd, rev), 
	length=25, strand=factor(rep(c("+", "-"), times=c(30, 30))))

## create object of class ReadCounts
readPile <- strandPileup(reads, chrLen=501, extend=1, plot=FALSE, compress=FALSE)

## get number of reads in sliding window
wdwCount <- windowCounts(apply(readPile[[1]], 1, sum), window=10, shift=5)

ChIPseqR documentation built on Nov. 8, 2020, 6:49 p.m.