mixHMM: Three-state HMM with mixture model for differential peak...

In plbaldoni/mixHMM: Differential Peak Calling via Hidden Markov Model With Mixture of Negative Binomial Distributions

Description

This function fits a three-state HMM with mixture model of Negative Binomials for differential peaks across conditions with replicates

Usage

mixHMM(ChIP, Control = NULL, offset, group, B, 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 conditions*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)
group	vector of length N with condition (numeric) labels (e.g. c(1,1,2,2,3,3) for three conditions and two replicates each)
B	number of mixture distributions in the differential component
control	list of control arguments from controlPeaks()

Details

For ChIP, Control, and offset matrices, columns should be ordered by condition and replicates, respectively. For instance, the first column should have data from the first replicate of the first condition, the second column should have data from the second replicate of the first condition, and so on. The same applies for the elements of group.

Value

A list with components

Author(s)

Pedro L. Baldoni, [e-mail]

References

https://github.com/plbaldoni/mixHMM

Examples

data(H3K36me3)
ChIP = SummarizedExperiment::assay(H3K36me3)
offset = ZIMHMM::createOffset(ChIP,method = 'loess',span = 1)
group = c(1,1,1,2,2,2,3,3,3)
B = 2^(length(unique(group)))-2
## Not run: output = mixHMM(ChIP,Control=NULL,offset,group,B,control = controlPeaks())

plbaldoni/mixHMM documentation built on Nov. 8, 2019, 8:05 p.m.