Dear Fernando,
Date: Tue, 10 May 2011 13:40:23 -0500
From: "Biase, Fernando" <bi...@illinois.edu>
To: "bioc-sig-sequencing@r-project.org"
<bioc-sig-sequencing@r-project.org>
Subject: [Bioc-sig-seq] interaction factor in edgeR
Dear list users,
I am not a statistician, so pardon my ignorance.
When using edgeR package to analyse RNA-seq data the number of
differential expressed genes vary depending on whether I use an
interaction factor in the design. Can anyone suggest why does it happen?
Well, you fit a different model, and test a different hypothesis, so the
results change. No doubt the residual dispersion has changed as well.
Wouldn't you be worried if the results didn't change?
Example:
if I use:
design <- model.matrix(~ a + b , data=targets)
I have:
summary(decideTests_eset_b_tmm)
[,1]
-1 2855
0 12346
1 4928
if I use:
design <- model.matrix(~ a + b + a:b , data=targets)
then:
summary(decideTests_eset_b_tmm)
[,1]
-1 3343
0 9490
1 4191
You haven't actually told us which coefficient you're testing for.
When having more than one factor, is it more appropriate to have the
interaction factor in the design?
Do you expect the effect of experimental factor b to be same for each
level of a? If yes, then maybe you don't need the interaction term. It
depends on your experiment and on the questions you want to ask.
Thanks a lot
Best,
Fernando
BTW, I would much prefer it if you would post questions about edgeR to the
main Bioconductor mailing list rather than to bioc-sig-sequencing. The
questions relate more to the general problem of analysing gene expression
experiments rather than to details of particular sequencing technologies.
Best wishes
Gordon
---------------------------------------------
Professor Gordon K Smyth,
Bioinformatics Division,
Walter and Eliza Hall Institute of Medical Research,
1G Royal Parade, Parkville, Vic 3052, Australia.
Tel: (03) 9345 2326, Fax (03) 9347 0852,
sm...@wehi.edu.au
http://www.wehi.edu.au
http://www.statsci.org/smyth
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