mosaicsFit: Fit MOSAiCS model

In mosaics: MOSAiCS (MOdel-based one and two Sample Analysis and Inference for ChIP-Seq)

Description

Fit one-sample or two-sample MOSAiCS models with one signal component and two signal components.

Usage

mosaicsFit( object, ... )
## S4 method for signature 'BinData'
mosaicsFit( object, analysisType="automatic", bgEst="rMOM",
    k=3, meanThres=NA, s=2, d=0.25, trans="power", truncProb=0.999, parallel=FALSE, nCore=8 )

Arguments

object	Object of class BinData, bin-level ChIP-seq data imported using method readBins.
analysisType	Analysis type. Possible values are "OS" (one-sample analysis), "TS" (two-sample analysis using mappability and GC content), and "IO" (two-sample analysis without using mappability and GC content). If analysisType="automatic", this method tries to make the best guess for analysisType, based on the data provided.
bgEst	Parameter to determine background estimation approach. Possible values are "matchLow" (estimation using bins with low tag counts) and "rMOM" (estimation using robust method of moment (MOM)). If bgEst="automatic", this method tries to make the best guess for bgEst, based on the data provided. Default is bgEst="rMOM".
k	Parameter for estimating background distribution. It is not recommended for users to change this value.
meanThres	Parameter for estimating background distribution. Default is 1 for analysisType="TS" and 0 for analysisType="OS". Not relevant when analysisType="IO".
s	Parameter for estimating background distribution. Relevant only when analysisType="TS". Default is 2.
d	Parameter for estimating background distribution. Relevant only when analysisType="TS" or analysisType="IO". Default is 0.25.
trans	Transformation of matching control tag count. Possible values are "log" (logarithm transformation) and "power" (power transformation). Relevant only when analysisType="IO". Default is trans="power".
truncProb	Parameter for estimating background distribution. Relevant only when analysisType="IO".
parallel	Utilize multiple CPUs for parallel computing using "parallel" package? Possible values are TRUE (utilize multiple CPUs) or FALSE (do not utilize multiple CPUs). Default is FALSE (do not utilize multiple CPUs).
nCore	Number of CPUs when parallel computing is utilized.
...	Other parameters to be passed through to generic mosaicsFit.

Details

The imported data type constraints the analysis that can be implemented. If only data for ChIP sample and matched control sample (i.e., either type=c("chip", "input") or type=c("chip", "input", "N") was used in method readBins), only two-sample analysis without using mappability and GC content (analysisType="IO") is allowed. If matched control data is available with mappability score, GC content score, and sequence ambiguity score, (i.e., type=c("chip", "input", "M", "GC", "N") was used in method readBins), user can do all of three analysis types (analysisType="OS", analysisType="TS", or analysisType="IO"). If there is no data for matched control sample (i.e., type=c("chip", "M", "GC", "N") was used in method readBins), only one-sample analysis (analysisType="OS") is permitted.

Parallel computing can be utilized for faster computing if parallel=TRUE and parallel package is loaded. nCore determines number of CPUs used for parallel computing. meanThres, s, d, trans, and truncProb are the tuning parameters for estimating background distribution. The vignette and Kuan et al. (2011) provide further details about these tuning parameters. Please do not try different value for k argument.

Value

Construct MosaicsFit class object.

Author(s)

Dongjun Chung, Pei Fen Kuan, Rene Welch, Sunduz Keles

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

readBins, mosaicsFitHMM, MosaicsFit.

Examples

## Not run: 
library(mosaicsExample)
data(exampleBinData)
exampleFit <- mosaicsFit( exampleBinData, analysisType="IO" )

## End(Not run)

mosaics documentation built on Nov. 8, 2020, 6:59 p.m.