NarrowPeaks-package: Shape-based Analysis of Variation in ChIP-seq using...

In NarrowPeaks: Shape-based Analysis of Variation in ChIP-seq using Functional PCA

Description

The package applies a functional version of principal component analysis (FPCA) to: (1) Postprocess data in wiggle track format, commonly produced by generic ChIP-seq peak callers, by applying FPCA over a set of read-enriched regions (ChIP-seq peaks). This is done to study variability of the the peaks, or to shorten their genomic locations accounting for a given proportion of variation among the enrichment-score profiles. (2) Analyse differential variation between multiple ChIP-seq samples with replicates. The function 'narrowpeaksDiff' quantifies differences between the shapes, and uses Hotelling's T2 tests on the functional principal component scores to identify significant differences across conditions. An application of the package for Arabidopsis datasets is described in Mateos, Madrigal, et al. (2015) Genome Biology: 16:31.

Details

Package:	NarrowPeaks
Type:	Package
Version:	1.13.4
Date:	2015-06-01
License:	Artistic-2.0
LazyLoad:	yes

Author(s)

Pedro Madrigal, with contributions from Pawel Krajewski [e-mail]

Maintainer: Pedro Madrigal [e-mail]

References

Mateos JL, Madrigal P, et al. (2015) Combinatorial activities of SHORT VEGETATIVE PHASE and FLOWERING LOCUS C define distinct modes of flowering regulation in Arabidopsis. Genome Biology 16: 31.
Bailey T, Krajewski P, Ladunga I, Lefebvre C, Li Q, Liu T, Madrigal P, Taslim C, Zhang J (2013) Practical Guidelines for the Comprehensive Analysis of ChIP-seq data. PLOS Comput Biol. 9 (11): e1003326.

Examples

owd <- setwd(tempdir())

##For this example we will use a subset of the AP1 ChIP-seq data (Kaufmann et
##al., 2010)
##The data is obtained after analysis using the CSAR package available in 
##Bioconductor 
data("NarrowPeaks-dataset")
writeLines(wigfile_test, con="wigfile.wig")

##Write binary files with the WIG signal values for each chromosome 
##independently and obtain regions of read-enrichment with score values greater
##than 't', allowing a gap of 'g'. Data correspond to enriched regions found up
##to 105Kb in the Arabidopsis thaliana genome
wigScores <- wig2CSARScore(wigfilename="wigfile.wig", nbchr = 1, 
chrle=c(30427671))
gc(reset=TRUE) 
library(CSAR)
candidates <- sigWin(experiment=wigScores$infoscores, t=1.0, g=30)

##Narrow down ChIPSeq enriched regions by functional PCA
shortpeaks <- narrowpeaks(inputReg=candidates, 
scoresInfo=wigScores$infoscores, lmin=0, nbf=150, rpenalty=0, 
nderiv=0, npcomp=2, pv=80, pmaxscor=3.0, ms=0)

###Export GRanges object with the peaks to annotation tracks in various 
##formats. E.g.:
library(GenomicRanges)
names(elementMetadata(shortpeaks$broadPeaks))[3] <- "score"
names(elementMetadata(shortpeaks$narrowPeaks))[2] <- "score"
library(rtracklayer)
export.bedGraph(object=candidates, con="CSAR.bed")
export.bedGraph(object=shortpeaks$broadPeaks, con="broadPeaks.bed")
export.bedGraph(object=shortpeaks$narrowPeaks, con="narrowpeaks.bed")

setwd(owd)

NarrowPeaks documentation built on April 28, 2020, 6:51 p.m.