[BioC] Agi4x44Preprocess/Limma and number of significant p-values

Jarek Bryk bryk at evolbio.mpg.de
Mon Oct 31 22:15:26 CET 2011


> This was for one of the datasets, with 2 (four result sets) micorarrays, 1 for each treatment. For another similar dataset, 4 microarrays with 2 samples for each treatment, results are very similar.

Could you tell me again how many samples you had per group to compare?
And more details about the experimental design. For example, if you
grouped your samples so that you had your experimental groups on
separate slides, then some technical variation could have contributed
to the differences. If one of your groups had some very severe
treatment (or if samples were labeled in a different batch, or if you
compare wild vs inbred mice) that could also be the reason. 50% of
differentially expressed seem high at first sight. I am not sure, but
if it's real, the quantile normalization may break for such a
divergent set (others will weigh in on that).

Also, I didn't get if you talking about genes or probes, and whether
you did some non-specific filtering of your data.


 Jarek Bryk | www.evolbio.mpg.de/~bryk
 Max Planck Institute for Evolutionary Biology
 August Thienemann Str. 2 | 24306 Plön, Germany
 tel. +49 4522 763 287 | bryk at evolbio.mpg.de

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