MosaicsFit-class {mosaics} | R Documentation

## Class "MosaicsFit"

### 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)

[Package mosaics version 2.25.0 Index]