[BioC] limma design

[Naomi Altman]

Yes, the limma and ANOVA models assume that the variance for a gene 
is constant across treatments, so adding more treatments (with 
replicates) improves the power for all comparisons.

--Naomi

At 04:26 AM 6/19/2007, Lev Soinov wrote:
>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 at 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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Naomi S. Altman                                814-865-3791 (voice)
Associate Professor
Dept. of Statistics                              814-863-7114 (fax)
Penn State University                         814-865-1348 (Statistics)
University Park, PA 16802-2111