RLEBindScore-class: Run-length Encoded Binding Site Scores

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

Description

This class provides a memory efficient representation of binding site scores.

Objects from the Class

Objects can be created by calls of the form BindScore(functionCall, score, pvalue, peaks, cutoff, nullDist, names, start, digits, compress=TRUE) or through calls to callBindingSites.

Slots

functionCall:

Object of class "call" storing the function call used to initiate the analysis.

score:

Object of class "list". The binding site score. One run-length encoded numeric vector per chromosome.

pvalue:

Object of class "list". The (adjusted and run-length encoded) p-values corresponding to the scores in slot score.

peaks:

Object of class "list" giving the location of significant peaks in the binding site score. These correspond to the location of predicted binding sites.

cutoff:

Object of class "numeric" with entries ‘pvalue’ and ‘score’ giving the significance threshold used for peak calling in terms of p-value and score.

nullDist:

Object of class "numeric" providing the parameters of the null distribution used to determine p-values.

start:

Object of class "integer" indicating the index corresponding to the first entry in score (assumed to be the same for all chromosomes).

Extends

Class "BindScore", directly.

Methods

decompress

signature(x = "RLEBindScore"): conversion to BindScore object.

Author(s)

Peter Humburg

See Also

BindScore, Rle

Examples

showClass("RLEBindScore")

set.seed(1)

## determine binding site locations
b <- sample(1:1e6, 5000)

## sample read locations
fwd <- unlist(lapply(b, function(x) sample((x-83):(x-73), 20, replace=TRUE)))
rev <- unlist(lapply(b, function(x) sample((x+73):(x+83), 20, replace=TRUE)))

## add some background noise
fwd <- c(fwd, sample(1:(1e6-25), 50000))
rev <- c(rev, sample(25:1e6, 50000))

## 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(150000, 150000))))

## create object of class ReadCounts
readPile <- strandPileup(reads, chrLen=1e6, extend=1, plot=FALSE)

## predict binding site locations
## the artificial dataset is very small so predictions may not be very reliable
bindScore <- simpleNucCall(readPile, bind=147, support=20, plot=FALSE, compress=TRUE)

## number of binding sites found
length(bindScore)

## the first few predictions, by score
head(bindScore)

## score and p-value cut-off used
cutoff(bindScore)

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