RLEBindScore-class {ChIPseqR} | R Documentation

## Run-length Encoded Binding Site Scores

### 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)

[Package ChIPseqR version 1.36.0 Index]