[BioC] Limma and logistic regression

Claus Mayer claus at bioss.ac.uk
Thu Mar 11 17:05:16 CET 2010

Hi Dan!

I am sure there are better references for this but you can find some
discussion on the topic logistic regression vs t-test at this link:
http://udel.edu/~mcdonald/statlogistic.html .

In an ideal world you assume in a regression model that the explanatory
variable is fixed by the experimenter as part of the experimental design and
the response then observed in the experiment.

So if you gave one group of patients a treatment, the other one a control
and then observed gene expression, the natural analysis would have treatment
as explanatory variable and gene expression as response.

I assume in your case the binary variable is not fixed but some outcome of
the study too (e.g is the treatment a success or failure). In that case you
should ask yourself, what are you really trying to achieve with the study.
If the aim is to predict (the probability of) the outcome by the gene
expression profile, logistic regression is more appropriate. In that case it
would also make sense to have a look at the literature about classification
in microarray experiments etc...

Best Wishes


> -----Original Message-----
> From: bioconductor-bounces at stat.math.ethz.ch 
> [mailto:bioconductor-bounces at stat.math.ethz.ch] On Behalf Of 
> Daniel Brewer
> Sent: 11 March 2010 11:42
> To: Bioconductor mailing list
> Subject: [BioC] Limma and logistic regression
> Hello,
> I have a situation where I have a microarray set and a binary 
> outcome that I want to examine.  If I was just looking at one 
> gene I would use glm with family=bionomial with the 
> expression levels as the explanatory variable.
> I would like to look at the whole set of genes and for that I 
> would normally use limma, but limma has the expression level 
> as the response variable and so the binary outcome would be 
> an explanatory variable.  Is this an equivalent approach?  Is 
> it valid and what are the differences, especially in assumptions?
> Thanks
> Dan
> --
> **************************************************************
> Daniel Brewer, Ph.D.
> Institute of Cancer Research
> Molecular Carcinogenesis
> Email: daniel.brewer at icr.ac.uk
> **************************************************************
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