[BioC] limma design

James W. MacDonald jmacdon at med.umich.edu
Mon Jun 18 16:22:03 CEST 2007

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.



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

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