[BioC] Data sets conducted in different labs

[Naomi Altman]

I would probably normalize together and include "lab" as a factor in the 
experiment.  Alternatively, there is a nice result by Steve Marron at UNC 
about how to combine datasets which would probably work quite well in this 
situation.  You might have a look at his web page.

--Naomi

At 01:55 PM 10/19/2004 -0700, you wrote:
>Hi there;
>I am sorry if my question doesn't qualify for BioC mail list.
>
>Have you met the situation that two labs carried out the same/similar
>experiment, but came out with quite different results in term of
>differentially expressed genes identified.  Have anyone  had done the
>studies on this problem, any reference/observations?
>
>The usual way is to identify genes based on two lab's data, respectively,
>then compare the results. What about make one model for the combined data
>from two labs which takes lab as one potential factor. In this case, how
>to do the pre-processing part, normalize all data together or two lab's
>data separately? Any recommendations?
>
>What I observed is: I observed clearly systematic difference in the data
>from two lab. But after I normalize all data ( I used rma )together, you
>still can tell the different origin of the data after normalization, and
>the model test (limma) that the lab factor is significant for about 50%
>genes. My question is: in this case (normalize all data together), should
>I include the lab as one factor? It seems normalizing procedure can't
>cancel lab effects.
>
>But if I normalize two lab's data separately, they will have different
>variation. Even with a lab factor, I can't use model two lab's data into
>one model.
>
>Any comments/suggestions will be appreciated.
>
>Bests;
>Fangxin
>
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Naomi S. Altman                                814-865-3791 (voice)
Associate Professor
Bioinformatics Consulting Center
Dept. of Statistics                              814-863-7114 (fax)
Penn State University                         814-865-1348 (Statistics)
University Park, PA 16802-2111