stdPeakLocation {htSeqTools} | R Documentation

## Peak density with respect to closest gene.

### Description

`stdPeakLocation` plots the density of peaks with respect to the genomic feature (e.g. gene) in standardized gene coordinates so that genes with different lengths are comparable.

`PeakLocation` produces the same plot in non-standardized coordinates (i.e. distances are measured in base pairs).

`plotMeanCoverage` plots the mean coverage in a series of selected genomic regions.

### Usage

    stdPeakLocation(x, peakDistance=1000, startpos='start_position', endpos='end_position',
    strand='strand', distance, main='', xlab='Distance relative to feature length', xaxt='n',
      xlim=c(-1,2), densityType="kernel", nbreaks=10, ...)

    PeakLocation(x, peakDistance=1000, startpos='start_position', endpos='end_position',
    strand='strand', distance, main='', xlab='Distance (bp)',
      densityType="kernel", breaks, ...)

    plotMeanCoverage(cover, x, upstreambp=1000, downstreambp=5000,
    startpos='start_position', endpos='end_position', normalize=FALSE,
    smooth=FALSE, span=0.05, main='', xlab='(bp)', ylab='Average coverage', ...)

### Arguments

`x` | A `RangedData` or `data.frame` indicating peak start and end in `start` and `end`, and start and end of the closest genomic feature (e.g. gene) in `startpos` and `endpos`.
`cover` | An `RleList` object containing the coverage, as returned by the function `coverage`.
`peakDistance` | Peaks more than `peakDistance` bases upstream or more than `3*peakDistance` downstream of the closest feature are discarded.
`startpos` | Name of the variable storing the start position of the closest genomic feature.
`endpos` | Name of the variable storing the end position of the closest genomic feature.
`strand` | Name of the variable storing the strand for the closest genomic feature.
`distance` | Name of the variable indicating the distance between the peak and the closest genomic feature. If left missing the distance between the feature start and the mid-point of the peak is computed.
`main` | Graphical parameter passed on to `plot`.
`xlab` | Graphical parameter passed on to `plot`.
`ylab` | Graphical parameter passed on to `plot`.
`xaxt` | Graphical parameter passed on to `plot`.
`xlim` | In `stdPeakLocation` the x-axis limit is set to `xlim*peakDistance`.
`densityType` | If we eant a density plot or a histogram. Has to be one of "kernel" (for the density plot) or "hist" for the histogram.
`nbreaks` | Number of breaks to be used. It will not be used if `densityType` is different from "hist".
`breaks` | This parameter will be passed to the `hist` plotting function. It will not be used if `densityType` is different from "hist".
`upstreambp` | Number of bp upstream of the TSS where the coverage should be computed
`downstreambp` | Number of bp downstream of the TSS where the coverage should be computed
`normalize` | When set to `TRUE` the average coverage in each position is divided by the average across all positions. This is useful when trying to super-impose data from several experiments that had different read coverage.
`smooth` | If set to `TRUE`, the average coverage is smooth by calling `loess`.
`span` | Parameter controlling smoothing, passed on to `loess`. Larger values indicate more smoothing.
`...` | Further parameters passed on to `plot`.

### Value

This function produces a density plot.

### Methods

Methods for `stdPeakLocation`, `PeakLocation`

`signature(x = "data.frame")`

  The data frame should contain columns named `start` and `end` indicating the peak location, `txStart`, `txEnd` indicating transcription start/end of the closest gene and `strand` indicating the strand.

`signature(x = "RangedData")`

  `start(x)` and `end(x)` indicate the peak location. `x` should contain variables `x[['txStart']]`, `x[['txEnd']]` indicating the transcription start/end of the closest gene and `x[['strand']]` indicating the strand.

Methods for `plotMeanCoverage`

`signature(cover="RleList", x="RangedData")`

  `cover` contains the coverage and `x` the genomic regions of interest.

### Examples

    #Generate synthetic peaks
    set.seed(1)
    st <- runif(100,1,1000)
    en <- st+runif(length(st),25,100)
    peaks <- RangedData(IRanges(st,en),space='chr1')

    #Assign distance to closest gene
    #(typically one would call annotatePeakInBatch
    #from package ChIPpeakAnno to do this)
    peaks[['start_position']] <- start(peaks) + runif(nrow(peaks),-500,1000)
    peaks[['end_position']] <- peaks[['start_position']] + 500
    peaks[['distance']] <- peaks[['start_position']] - start(peaks)
    peaks[['strand']] <- sample(c('+','-'),nrow(peaks),replace=TRUE)
    PeakLocation(peaks,peakDistance=1000)

[Package htSeqTools version 1.30.0 Index]