edgeRun (version 1.0.7)

edgeRun-package: Unconditional exact testing for two-group digital expression data

Description

This package provides functions for testing for differential expression for digital data (e.g. RNA-Seq, CAGE-Seq, ChIP-Seq, etc.). Exact unconditional tests based on the negative binomial distribution are provided. Similar functionality exists in the Bioconductor package edgeR where a similar but conditional exact test is implemented. The unconditional test is more powerful, especially for lowly expressed genes or when sample size is extremely small.

Details

ll{ Package: edgeRun Type: Package Version: 1.0.03 Date: 2014-04-09 License: MIT } Users familiar with edgeR can use edgeRun functionality by simply using the UCexactTest function instead of edgeR's exactTest function. We recommend the edgeR workflow that takes input count data and ends up with a DGEList. Please refer to edgeR documentation on how to use that workflow.

References

Robinson MD, et al. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 2010;26:139-140.

See Also

UCexactTest,DGEList

Examples

# Example taken from edgeR documentation:
# generate raw counts from NB, create list object
y <- matrix(rnbinom(50*4,size=1/0.2,mu=10),nrow=50,ncol=4)
d <- DGEList(counts=y, group=c(1,1,2,2), lib.size=colSums(y))
d <- calcNormFactors(d)
d <- estimateCommonDisp(d)
d <- estimateTagwiseDisp(d)

de.edgeR <- exactTest(d) #using edgeR CONDITIONAL exact test
topTags(de.edgeR)

de.edgeRun <- UCexactTest(d) #using edgeRun, UNCONDITIONAL exact test
topTags(de.edgeRun)


# same p-values using low-level function directly (edgeR)
p.value.edgeR <- exactTestDoubleTail(y[,1:2], y[,3:4]
,dispersion=d$tagwise.dispersion)

p.value.edgeRun <- pvalue(s1=rowSums(y[,1:2,drop=FALSE])
, s2=rowSums(y[,3:4,drop=FALSE]), phi=d$tagwise.dispersion, n1=2, n2=2)