cormotiffitsep: Individual Study Motif Fit

In kennethabarr/CormotifCounts: Correlation Motif Fit

Individual Study Motif Fit

Description

This function fits a mixture modified t-distribution model to each study seperately.

Usage

cormotiffitsep(exprs,groupid,compid, tol=1e-3, max.iter=100)

Arguments

exprs	a matrix, the expression data after normalization that is on log2 scale, each row of the matrix corresponds to a gene and each column of the matrix corresponds to a sample array.
groupid	the group label for each sample array, two arrays in the same study with same experinment condition(e.g. control) have the same groupid.
compid	the study design and comparison matrix, each row of the matrix corresponds to one study with the first column being the first experinment condition and the second column being the second experinment condition
tol	the relative tolerance level of error.
max.iter	maximun number of iterations.

Value

p.post	the posterior probability for each gene to be differentially expressed.
motif.prior	fitted values of the probability for genes to be differentially expressed in each study, a 1*D vector, where D is the number of studies
loglike	log-likelihood of the fitted model.

Author(s)

Hongkai Ji, Yingying Wei

References

Ji, H., Wei, Y.,(2011) Correlation Motif. Unpublished

Examples

data(simudata2)
n<-nrow(simudata2)
m<-ncol(simudata2)
#the expression data is from the second column to m
exprs.simu2<-as.matrix(simudata2[,2:m])

#prepare the group ID number for each sample array
data(simu2_groupid)

#prepare the design matrix for each group of samples
data(simu2_compgroup)

#fit seperate models to each study
motif.fitted.sep<-cormotiffitsep(exprs.simu2, simu2_groupid,simu2_compgroup)

kennethabarr/CormotifCounts documentation built on July 1, 2023, 5:58 p.m.