NarrowPeaks (version 1.12.0)

narrowpeaksDiff: Differential Analysis of Transcription Factor Binding using FPCA

Description

Shape-based differential binding analysis and testing for ChIP-Seq datasets using Functional Principal Component Analysis and Hotelling's T2 tests.

Usage

narrowpeaksDiff(bedFile,  headerBed= TRUE, flank=100, bigwigs , conditions , 
nbasis=50, pcs = 10, bigWigSummaryPath=getwd(), variation = 0.6)

Arguments

bedFile

Text file in BED format. It should contain at least 3 columns (chr, start, end), in which case the reference point for the FD analysis is calculated as the central point. A 4-th column can be provided containing the reference point.

headerBed

'TRUE' if the first row in the BED file contain the name of the columns. 'FALSE' otherwise.

flank

Length (in bp.) that is considered upstream and downstream the reference point (or central point, if reference point is not given) for functional principal component analysis.

bigwigs

Vector contaning the name of the bigWig files to be used in the analysis.

conditions

Vector of characters with the Labels for the bigWig files. Biological replicates must have the same label.

nbasis

Number of order-4 B-Spline basis functions for functional data analysis.

pcs

Number of principal components to be computed (default is 10).

bigWigSummaryPath

Path to the UCSC utility bigWigSummary (in case it is differente from the current directory). The tool can be downloaded for Linux and macOSX from the UCSC website: http://hgdownload.cse.ucsc.edu/admin/exe/

variation

Minimum proportion of varition that is considered to select the number of functional principal component scores used in the Hotelling's T2 tests (0-1, defualt is 0.6).

Value

A list containing the following elements:

fdaprofiles

A list of matrices corresponding to the data of regions of interest (BED file) in the bigWig files.

p.values

A list of pairwise comparisons between experimental conditions (taking into account replicates) for each region in the BED file. P-values are computed using the Hotelling's T2 test.

Details

Detailed information can be found in Madrigal and Krajewski (in preparation).

References

Madrigal P, Krajewski P (in preparation) Shape-based Dimensionality Reduction Analyses by Functional PCA Reveal Associations between First and Higher Order Components in Next-Generation Sequencing Coverage Profiles. 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. Ramsay, J.O. and Silverman, B.W. (2005) Functional Data Analysis. New York: Springer.

See Also

narrowpeaks, NarrowPeaks-package

Examples

##library(NarrowPeaks)
##bigwigs <- c("A_rep1.bw","A_rep2.bw","A_rep3.bw","B_rep1.bw","B_rep2.bw","B_rep3.bw")
##conds   <- c("A","A","A","B","B","B")
##x <- narrowpeaksDiff(bedFile="regions.bed", bigwigs=bigwigs, conditions=conds, variation = 0.8)
##x$p.values