MosaicsFit-class: Class "MosaicsFit"

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

Description

This class represents MOSAiCS model fit.

Objects from the Class

Objects can be created by calls of the form new("MosaicsFit", ...).

Slots

mosaicsEst:

Object of class "MosaicsFitEst", representing estimates of MOSAiCS model fit.

mosaicsParam:

Object of class "MosaicsFitParam", representing tuning parameters for fitting MOSAiCS model.

chrID:

Object of class "character", a vector of chromosome IDs.

coord:

Object of class "numeric", a vector of genomic coordinates.

tagCount:

Object of class "numeric", a vector of tag counts of ChIP sample.

mappability:

Object of class "numeric", a vector of mappability score.

gcContent:

Object of class "numeric", a vector of GC content score.

input:

Object of class "numeric", a vector of tag counts of matched control sample.

bic1S:

Object of class "numeric", Bayesian Information Criterion (BIC) value of one-signal-component model.

bic2S:

Object of class "numeric", Bayesian Information Criterion (BIC) value of two-signal-component model.

seqDepth:

Object of class "numeric", a vector of sequencing depth of length 2, where the first and second elements correpond to sequencing depths of ChIP and control samples, respectively. If there is not control sample, the second element is set to NA.

Methods

estimates

signature(object = "MosaicsFit"): extract estimates from MOSAiCS model fit.

mosaicsPeak

signature(object = "MosaicsFit"): call peaks using MOSAiCS model fit.

plot

signature(x = "MosaicsFit", y = "missing"): draw Goodness of Fit (GOF) plot.

print

signature(x = "MosaicsFit"): (not supported yet)

show

signature(object = "MosaicsFit"): provide brief summary of the object.

seqDepth

signature(object = "MosaicsFit"): provide a vector of sequencing depth.

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

mosaicsFit, mosaicsPeak, mosaicsFitHMM, estimates.

Examples

showClass("MosaicsFit")
## Not run: 
library(mosaicsExample)

data(exampleBinData)
exampleFit <- mosaicsFit( exampleBinData, analysisType="IO" )

exampleFit
plot(exampleFit)
estimates(exampleFit)

examplePeak <- mosaicsPeak( exampleFit, signalModel = "2S", FDR = 0.05 )

## End(Not run)

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