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Last data update: 2014.03.03

R: Detect peaks (local maximum) from values series

peakDetection

R Documentation

Detect peaks (local maximum) from values series

Description

This function allows a efficient recognition of the local maximums (peaks) in a given numeric vector.

It's recommended to smooth the input with filterFFT prior the detection.

Usage

## S4 method for signature 'list'
peakDetection(data, threshold="25%", width=1, score=TRUE, mc.cores=1)
## S4 method for signature 'numeric'
peakDetection(data, threshold="25%", width=1, score=TRUE, mc.cores=1)

Arguments

`data`	Input numeric values, or a list of them
`threshold`	Threshold value from which the peaks will be selected. Can be given as a percentage string (i.e., `"25%"` will use the value in the 1st quantile of `data`) or as an absolute coverage numeric value (i.e., `20` will not look for peaks in regions without less than 20 reads (or reads per milion)).
`width`	If a positive integer > 1 is given, the peaks are returned as a range of the given width centered in the local maximum. Useful for nucleosome calling from a coverage peak in the dyad.
`score`	If TRUE, the results will be scored using `peakScoring` function
`mc.cores`	If `multicore` support, the number of cores available

Value

The type of the return depends on the input parameters:

numeric (or a list of them) if width==1 & score==FALSE containing the position of the peaks

data.frame (or list of them) if width==1 & score==TRUE containing a 'peak' column with the position of the peak plus a 'score' column with its score.

IRanges (or IRangesList) if width>1 & score==FALSE containing the ranges of the peaks.

RangedData if width>1 & score==TRUE containing the ranges of the peaks and the assigned score.

Note

If width > 1, those ranges outside the range 1:length(data) will be skipped

Author(s)

Oscar Flores oflores@mmb.pcb.ub.es

Examples

	
	#Generate a random peaks profile
	reads = syntheticNucMap(nuc.len=40, lin.len=130)$syn.reads
	cover = coverage(reads)
	
	#Filter them
	cover_fft = filterFFT(cover)
	
	#Detect and plot peaks (up a bit the threshold for accounting synthetic data)
	peaks = peakDetection(cover_fft, threshold="40%", score=TRUE)
	plotPeaks(peaks, cover_fft, threshold="40%", start=10000, end=15000)

	#Now use ranges version, which accounts for fuzziness when scoring
  peaks = peakDetection(cover_fft, threshold="40%", score=TRUE, width=147)
  plotPeaks(peaks, cover_fft, threshold="40%", start=10000, end=15000)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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Type 'contributors()' for more information and
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Type 'demo()' for some demos, 'help()' for on-line help, or
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Type 'q()' to quit R.

> library(nucleR)
Loading required package: ShortRead
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
    get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
    rbind, rownames, sapply, setdiff, sort, table, tapply, union,
    unique, unsplit

Loading required package: BiocParallel
Loading required package: Biostrings
Loading required package: S4Vectors
Loading required package: stats4

Attaching package: 'S4Vectors'

The following objects are masked from 'package:base':

    colMeans, colSums, expand.grid, rowMeans, rowSums

Loading required package: IRanges
Loading required package: XVector
Loading required package: Rsamtools
Loading required package: GenomeInfoDb
Loading required package: GenomicRanges
Loading required package: GenomicAlignments
Loading required package: SummarizedExperiment
Loading required package: Biobase
Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/nucleR/peakDetection.Rd_%03d_medium.png", width=480, height=480)
> ### Name: peakDetection
> ### Title: Detect peaks (local maximum) from values series
> ### Aliases: peakDetection peakDetection,list-method
> ###   peakDetection,numeric-method
> ### Keywords: manip
> 
> ### ** Examples
> 
> 	
> 	#Generate a random peaks profile
> 	reads = syntheticNucMap(nuc.len=40, lin.len=130)$syn.reads
> 	cover = coverage(reads)
> 	
> 	#Filter them
> 	cover_fft = filterFFT(cover)
> 	
> 	#Detect and plot peaks (up a bit the threshold for accounting synthetic data)
> 	peaks = peakDetection(cover_fft, threshold="40%", score=TRUE)
> 	plotPeaks(peaks, cover_fft, threshold="40%", start=10000, end=15000)
> 
> 	#Now use ranges version, which accounts for fuzziness when scoring
>   peaks = peakDetection(cover_fft, threshold="40%", score=TRUE, width=147)
>   plotPeaks(peaks, cover_fft, threshold="40%", start=10000, end=15000)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>