Gene set enrichment analysis of RNA-Seq data

[jel4h]

I was wondering if anyone is aware of a gene
set enrichment algorithm for RNA-Seq data that:

1) does not require a specification of differentially
expressed (DE) genes (i.e.no need to use a hard
p-value threshold cutoff for determining the DE gene
list)

2) uses subject sampling instead of gene sampling
to obtain the p-value (i.e.this would maintain
gene-gene correlations)

Basically, I'm looking for a
self-contained/subject sampling method (e.g.
SAM-GS for microarray data) or a "hybrid" method
(e.g. GSEA for microarray data). The only gene set
enrichment algorithm that I am aware of for RNA-Seq
data is GOSeq, but it uses a competitive/gene
sampling method (i.e. Fisher's Exact Test).
Note, the ideas of self-contained vs competitive and
subject sampling vs gene sampling come from the
following paper: Goeman JJ, Bühlmann P.Analyzing
gene expression data in terms of gene sets:
methodological issues. Bioinformatics. 2007 Apr 15;23(8)

Something like GSEA-SNP is close to what I want.
It uses a test-statistic that is suitable for discrete data
and uses subject sampling to calculate the p-values.

Thanks,
Julie

[ninni]

Gsva

Hi!
You could try the GSVA package in Bioconductor:

http://www.bioconductor.org/packages/2.10/bioc/html/GSVA.html

In the vignette http://www.bioconductor.org/packages...t/doc/GSVA.pdf in section 6 they explain how to use it on RNA-seq data. You do not need any DE genes. For the detection of DE gene sets they use limma from which you will obtain a p-value. They do not seem to offer one.

HTH
Ninni