[BioC] A question about Limma

[Fangxin Hong]

Hi Gordon;
Thanks for the reply. That make me feel confident about my thoughts.

>> but this
>> might under-estimate the total number of genes on which the effect of
>> interest is significant.
>
> Why do you think so?  The only disadvantage of keeping a non-significant
> term in the model is a
> reduction in residual degrees of freedom, with some consequential loss of
> power, but this
> disadvantage is mitigated by the empirical Bayes moderation process.
In model selection framework, deleting one in-significant effect from the
model might make other effect become significant(for example P<0.05).
However, since the empirical Bayes moderation process is able to modify
the error variance, that should be fine.

> Perhaps someday someone will work out a model selection theory for
> massively parallel regression
> situations like microarray experiments, but there isn't such a theory now.
>  It seems safer to me
> to have the same model for every gene, keeping all the 'a priori'
> important predictors in the
> model.
I agree.


Fangxin
-- 
Fangxin Hong, Ph.D.
Plant Biology Laboratory
The Salk Institute
10010 N. Torrey Pines Rd.
La Jolla, CA 92037
E-mail: fhong at salk.edu