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.
Character vector indicating data types to be imported.
This vector can contain
Character vector of file names, each of which matches each element of
How reads were processed? Possible values are
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.
Utilize multiple CPUs for parallel computing using
Number of CPUs when parallel computing is utilized.
Bin-level ChIP and matched control data can be generated
from the aligned read files for your samples using the method
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.
type=c("chip", "input") or
c("chip", "input", "N"),
only two-sample analysis without using mappability and GC content is allowed.
type=c("chip", "input", "M", "GC", "N"),
user can do the one- or two-sample analysis.
type=c("chip", "M", "GC", "N"), only one-sample analysis is permitted.
See help page of
When the data contains multiple chromosomes,
parallel computing can be utilized for faster preprocessing
parallel package is loaded.
nCore determines number of CPUs used for parallel computing.
BinData class object.
Dongjun Chung, Pei Fen Kuan, Rene Welch, Sunduz Keles
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.
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## 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)
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