[BioC] question about edgeR and Poisson model: common dispersion is too low
Gordon K Smyth
smyth at wehi.EDU.AU
Wed Apr 11 04:57:24 CEST 2012
Dear Galina,
Getting a small dispersion is good, not bad. Your experiment is giving a
bioligical CV of about 14% = sqrt(0.01859). In our experience, that is
typical or even on the high side for controlled experiments. See comments
in the discussion of the following paper:
http://nar.oxfordjournals.org/content/early/2012/02/06/nar.gks042
Note that, while a dispersion 0f 0.019 may look small, it is still vastly
different from assuming Poisson variability.
You give a quote from the first case study of the edgeR User's Guide.
That data was from an observational study on human tumours, obviously
highly variable data.
Best wishes
Gordon
------------ original message --------------
[BioC] question about edgeR and Poisson model: common dispersion is too low
Glazko, Galina V GVGlazko at uams.edu
Tue Apr 10 16:26:03 CEST 2012
Dear list, dear Dr. Smyth
I am analyzing RNA-Seq data for the first time and I am confused with the
value of common dispersion. I have only two replicates between conditions
(A1,A2: normal; B1,B2: oxidative stress) and the value of common
dispersion is
> d2<-estimateCommonDisp(d2)
> d2$common.dispersion
[1] 0.01859112
As far as I understand it is too low, is it?
In the User Guide it is written that
'a common dispersion estimate of 0.2 means that there is a lot more
variability in the data that can be accounted for by the Poisson model'
My estimate is much lower. Does it mean that I have to use Poisson model
instead of NB? If I have to use Poisson model, is it possible to do with
edgeR package?
I would appreciate your advices!
Best regards
Galina
> sessionInfo()
R version 2.15.0 (2012-03-30)
Platform: i386-pc-mingw32/i386 (32-bit)
locale:
[1] LC_COLLATE=English_United States.1252 LC_CTYPE=English_United
States.1252 LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C LC_TIME=English_United
States.1252
attached base packages:
[1] splines stats graphics grDevices utils datasets methods
base
other attached packages:
[1] edgeR_2.6.0 limma_3.12.0 BiocInstaller_1.4.3
loaded via a namespace (and not attached):
[1] tools_2.15.0
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