[BioC] A question about Limma

[Naomi Altman]

Reducing the model based on removing nonsignificant effects is called 
"pre-test estimation".  It is known to increase the false-positive rate, 
even in the classical setting.  In the microarray setting, there is no 
compelling reason to use pre-test estimators that differ from gene to gene.

--Naomi Altman

At 10:57 PM 1/3/2005 +1100, Gordon K Smyth wrote:
> > Date: Sun, 2 Jan 2005 14:05:15 -0800 (PST)
> > From: "Fangxin Hong" <fhong at salk.edu>
> > Subject: [BioC] A question about Limma
> > To: bioconductor at stat.math.ethz.ch
> > Message-ID: <1867.66.75.240.64.1104703515.squirrel at 66.75.240.64>
> > Content-Type: text/plain;charset=iso-8859-1
> >
> > Hi Bioconductor users;
> > I have a general question about limma model.
> > In limma package, usually one linear model applies to all genes, and error
> > variances from all genes are modified simultaneously. What if some
> > factors, for example, one main effect, is only significant for some genes.
> > Then if we want identify genes based on the significance of another main
> > effect (of interest). What is the best way to do it? Currently I juse
> > leave this factor in the model which is applied to all genes,
>
>That's what I do, leave all terms in the models for all the genes.  I 
>don't see a strong case for
>doing a separate model selection process for every gene.
>
> > 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.
>
>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.
>
>Gordon
>
> > I am sorry if this question has been asked/answered here before, I
> > wouldn't find it through searching the archive. Any comment, suggestion or
> > experience is appreciated.
> >
> > 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
>
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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