deDGE {edgeR}R Documentation

Compute moderated differential expression scores for digital gene expression (DGE) data

Description

Runs weighted likelihood calculation for moderated estimates of dispersion, and tests for differences in 'tag' abundance between groups

Usage

 
deDGE(object,alpha=500,doPoisson=FALSE,verbose=TRUE)

Arguments

object DGEList containing elements data (matrix: rows-tags, columns-libraries), lib.size group indicating class
alpha weight to put on the individual tag's likelihood
doPoisson logical, whether to fit Poisson model instead of Negative Binomial, default FALSE
verbose logical, whether to write comments, default TRUE

Value

deDGEList with elements lr (likelihood ratio test), r (estimates of 1/overdispersion), ps (list containing proportion estimates)

Author(s)

Mark Robinson

References

Robinson MD, Smyth GK. 'Small-sample estimation of negative binomial dispersion, with applications to SAGE data.' Biostatistics. 2008 Apr;9(2):321-32.

Robinson MD, Smyth GK. 'Moderated statistical tests for assessing differences in tag abundance.' Bioinformatics. 2007 Nov 1;23(21):2881-7.

Examples

# generate raw data from NB, create list object
y<-matrix(rnbinom(20,size=1,mu=10),nrow=5)
d<-DGEList(data=y,group=rep(1:2,each=2),lib.size=rep(c(1000:1001),2))

# find alpha and call main procedure to find differences
alpha<-alpha.approxeb(d)
ms<-deDGE(d,alpha=alpha$alpha)

[Package edgeR version 1.0.4 Index]