Dear List,
  Thank you for your replies. I understand Jim's point about SSR estimate, below. However, in plain language, is it correct to expect that by adding more treatments to the linear fit model (assuming that all of them are related to the same biological object) we increase the accuracy of our inferences about differential expression of genes? Do I understand correctly that the model assumes that the signal variance dos not change for a gene across treatments? If yes, than the only thing that changes for the gene from treatment to treatment is the average expression level and consequently, it is quite obvious that adding more data points would just improve the estimate of the denominator which Jim described in his e-mail.
   
  Thank you a lot for your kind help!
  With kind regards,
  Lev.
   
  "James W. MacDonald" <jmacdon@med.umich.edu> wrote:
  Lev Soinov wrote:
> Dear Gordon and List,
> 
> I would very much appreciate your comment on the experiment design in
> LIMMA. It is about processing of experiments with multiple
> treatments.
> 
> Let's say we have a simple Affy experiment with 16 samples collected
> from a cell line (treated/untreated) in two time points: - 4 treated,
> 4 untreated - time point 1 - 4 treated, 4 untreated - time point 2 We
> are interested in differential expression between treated and
> untreated cells, in point1 and point2 separately. When we process all
> samples together (normalise them together and fit linear fit models
> using the whole dataset) in LIMMA we will get results different from
> when we process data for points 1 and 2 separately (normalise them
> together but fit liner models separately).
> 
> I do understand that it should be like this (more information for
> priors), but I do not know whether there is some kind of a criterion
> helping decide whether to process them separately or in one go. It
> seems that adding more treatments into the mix increases statistical
> power and thus, increases the number of genes classified as
> differentially expressed. The latter seems a bit strange to me,
> because the number of genes classified as differentially expressed in
> one comparison (contrast) should not depend on the genes
> differentially expressed in some other comparison (contrast).

Yes, but you are fitting a linear model and then computing contrasts in 
one instance, and fitting two independent t-tests in the other. In the 
former, your denominator will be based on the SSR from the linear model 
(which is computed using data from _all_ samples, not just those being 
compared). In the latter the denominator is based on just those samples 
under consideration.

Best,

Jim



-- 
James W. MacDonald, M.S.
Biostatistician
Affymetrix and cDNA Microarray Core
University of Michigan Cancer Center
1500 E. Medical Center Drive
7410 CCGC
Ann Arbor MI 48109
734-647-5623


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