mosaics (version 2.10.0)

readBins: Import bin-level ChIP-sep data

Description

Import and preprocess all or subset of bin-level ChIP-sep data, including ChIP data, matched control data, mappability score, GC content score, and sequence ambiguity score.

Usage

readBins( type = c("chip", "input"), fileName = NULL, dataType = "unique", rounding = 100, parallel=FALSE, nCore=8 )

Arguments

type

Character vector indicating data types to be imported. This vector can contain "chip" (ChIP data), "input" (matched control data), "M" (mappability score), "GC" (GC content score), and "N" (sequence ambiguity score). Currently, readBins permits only the following combinations: c("chip", "input"), c("chip", "input", "N"), c("chip", "input", "M", "GC", "N"), and c("chip", "M", "GC", "N"). Default is c("chip", "input").

fileName

Character vector of file names, each of which matches each element of type. type and fileName should have the same length and corresponding elements in two vectors should appear in the same order.

dataType

How reads were processed? Possible values are either "unique" (only uniquely aligned reads were retained) or "multi" (reads aligned to multiple locations were also retained).

rounding

How are mappability score and GC content score rounded? Default is 100 and this indicates rounding of mappability score and GC content score to the nearest hundredth.

parallel

Utilize multiple CPUs for parallel computing using "paralle" package? Possible values are TRUE (use multiple CPUs) or FALSE (do not use multiple CPUs). Default is FALSE (do not use multiple CPUs).

nCore

Number of CPUs when parallel computing is utilized.

Value

Construct BinData class object.

Details

Bin-level ChIP and matched control data can be generated from the aligned read files for your samples using the method constructBins. In mosaics package companion website, http://www.stat.wisc.edu/~keles/Software/mosaics/, we provide preprocessed mappability score, GC content score, and sequence ambiguity score files for diverse reference genomes. Please check the website and the vignette for further details.

The imported data type constraints the analysis that can be implemented. If type=c("chip", "input") or c("chip", "input", "N"), only two-sample analysis without using mappability and GC content is allowed. For type=c("chip", "input", "M", "GC", "N"), user can do the one- or two-sample analysis. If type=c("chip", "M", "GC", "N"), only one-sample analysis is permitted. See help page of mosaicsFit.

When the data contains multiple chromosomes, parallel computing can be utilized for faster preprocessing if parallel=TRUE and parallel package is loaded. nCore determines number of CPUs used for parallel computing.

References

Kuan, PF, D Chung, G Pan, JA Thomson, R Stewart, and S Keles (2011), "A Statistical Framework for the Analysis of ChIP-Seq Data", Journal of the American Statistical Association, Vol. 106, pp. 891-903.

Chung, D, Zhang Q, and Keles S (2014), "MOSAiCS-HMM: A model-based approach for detecting regions of histone modifications from ChIP-seq data", Datta S and Nettleton D (eds.), Statistical Analysis of Next Generation Sequencing Data, Springer.

See Also

constructBins, mosaicsFit, BinData.

Examples

## Not run: 
# library(mosaicsExample)
# 
# constructBins( infile=system.file( file.path("extdata","wgEncodeSydhTfbsGm12878Stat1StdAlnRep1_chr22_sorted.bam"), package="mosaicsExample"), 
#     fileFormat="bam", outfileLoc="~/", 
#     PET=FALSE, fragLen=200, binSize=200, capping=0 )
# constructBins( infile=system.file( file.path("extdata","wgEncodeSydhTfbsGm12878InputStdAlnRep1_chr22_sorted.bam"), package="mosaicsExample"), 
#     fileFormat="bam", outfileLoc="~/", 
#     PET=FALSE, fragLen=200, binSize=200, capping=0 )
#     
# binTFBS <- readBins( type=c("chip","input"),
#     fileName=c( "~/wgEncodeSydhTfbsGm12878Stat1StdAlnRep1_chr22_sorted.bam_fragL200_bin200.txt",
#     "~/wgEncodeSydhTfbsGm12878InputStdAlnRep1_chr22_sorted.bam_fragL200_bin200.txt" ) )
# ## End(Not run)