RLEBindScore-class {ChIPseqR} | R Documentation |
This class provides a memory efficient representation of binding site scores.
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
.
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).
Class "BindScore"
, directly.
signature(x = "RLEBindScore")
: conversion to BindScore
object.
Peter Humburg
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)