[BioC] RNA-seq differentially expressed gene finding methods

Richard Friedman friedman at c2b2.columbia.edu
Fri Sep 5 18:52:35 CEST 2014

Dear Son,

	The t-test assumes a normal distribution,
which is appropriate for continous variables. RNAseq
data deals with counts (discrete entities). A negative binomial distribution
(EdgeR, Deseq) or a mean dependent variance (VOOM)
is much more approriate. Also the 3 methods mentioned
above estimate variablity better with information from all genes
using empirical Bayesian methods, than does the one-gene
at-a-time frequentist t-test.

Best wishes,
Richard A. Friedman, PhD
Associate Research Scientist,
Biomedical Informatics Shared Resource
Herbert Irving Comprehensive Cancer Center (HICCC)
Department of Biomedical Informatics (DBMI)
Educational Coordinator,
Center for Computational Biology and Bioinformatics (C2B2)/
National Center for Multiscale Analysis of Genomic Networks (MAGNet)/
Columbia Department of Systems Biology
Room 824
Irving Cancer Research Center
Columbia University
1130 St. Nicholas Ave
New York, NY 10032
(212)851-4765 (voice)
friedman at c2b2.columbia.edu

"There is nothing in my Contemporary Jewish Literature course that is
either contemporary, Jewish, or literature".

-Rose Friedman, age 17

On Sep 5, 2014, at 12:44 PM, Son Pham wrote:

> Dear all,
> I know that we have quite very good packages (edgeR, deseq) that calculate
> the list of differentially expressed genes in 2 conditions (with
> replicates) from raw counts. But I do not know what is wrong with the
> following simple approach (and whether other people have been using it):
> 1. Get the (estimated) tpm/fpkm for each gene in each sample
> 2. Do a t-test for two groups on each gene.
> 3. Adjust the p value for multiple tests (p-adj)
> Thanks,
> Son.
> 	[[alternative HTML version deleted]]
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