ZIMHMM: Zero Inflated Mixed Effects Hidden Markov Model (ZIMHMM)

In plbaldoni/ZIMHMM: Improved Detection of Epigenomic Marks With Mixed Effects HMMs

Description

This function runs ZIMHMM.

Usage

ZIMHMM(ChIP, Control, offset, random, control)

Arguments

ChIP	M*N matrix of ChIP read counts, where M is the number of windows in the analyzed genome and N is the number of replicates
Control	M*N matrix of log-transformed Control read counts
offset	M*N matrix of offsets. If no offset is used, use offset = matrix(0,nrow=M,ncol=N)
random	either 'intercept' for random intercept model or 'slope' for random slope model
control	list of control arguments from controlPeaks()

Value

A list with components

Pi	Vector of initial probabilities of the HMM
Gamma	Matrix of transition probabilities of the HMM
Psi	Vector of component-specific parameters of the HMM
Sigma2	Variance component
U	Vector of random effects
Zeroinfl	M*N Matrix with zero-inflation probabilities
Prob	Mx2 Matrix with posterior probabilities
LogF	Mx2 Matrix with log-forward probabilities
LogB	Mx2 Matrix with log-backward probabilities
Loglik	Mx2 Matrix with window-based probabilities
Parhist	Matrix with paramater estimates across EM iterations
Mean	M*(N*2) Matrix with NB means for every replicate and HMM component. The first two columns of Mean are the background and enrichment means of replicate 1, respectively, and so on
Viterbi	Predicted sequence of Viterbi states

Author(s)

Pedro L. Baldoni, [e-mail]

References

https://github.com/plbaldoni/ZIMHMM

Examples

data(Huvec)
ChIP = SummarizedExperiment::assay(Huvec,'ChIP')
Control = log(SummarizedExperiment::assay(Huvec,'Control')+1)
offset = matrix(0,nrow = nrow(ChIP),ncol = ncol(ChIP))
## Not run: ZIMHMM(ChIP = ChIP,Control = Control,offset = offset,random = 'intercept',control = controlPeaks())

plbaldoni/ZIMHMM documentation built on Dec. 5, 2019, 11:43 p.m.