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Old 04-12-2012, 01:26 PM   #1
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Location: USA

Join Date: Apr 2012
Posts: 3
Default Gene set enrichment analysis of RNA-Seq data

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 ( need to use a hard
p-value threshold cutoff for determining the DE gene

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.

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Old 06-21-2012, 04:25 AM   #2
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Location: london

Join Date: Jun 2012
Posts: 8
Default Gsva

You could try the GSVA package in Bioconductor:

In the vignette 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.

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