Hi,
Maybe a naive question, but is it possible to do a 'one sample t-test' in limma while including a covariate in the model? If so, how?

I have an experiment in which 32 volunteers were profiled on array after and before a (short-term) intervention (physical exercise). Some volunteers used a supplement, others not.
I would like to identify genes responding to the intervention, while correcting for supplement use.

I manually subtracted the 'before' from the 'after' expression data, so I got delta values (delta.data; a matrix).
Then I checked for differential gene expression using the one sample t-test approach to check whether differences are significant from 0  (thus without defining a design using contrast.matrix).

> fit <- lmFit(delta.data)
> fit2 <- eBayes(fit)
> topTable(fit2)
              ID      logFC         t      P.Value    adj.P.Val        B
4426         xxx  0.4229133  9.496956 3.149698e-12 5.193054e-08 17.34925
14955      yyy  0.4817134  9.314950 5.598957e-12 5.193054e-08 16.82030

So far so good, but how can I include the covariate 'supplement' [being 0 or 1 for 38ppl] in the model?

If I try to do this the usual way by defining the design using contrast.matrix, a got an error:

> design <- model.matrix(~0+Intervention+Supplement)
Error in `contrasts<-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]) :
  contrasts can be applied only to factors with 2 or more levels

The error is obviously correct because 'Intervention' is 1 for all samples.


Thanks,
Guido

---------------------------------------------------------
Guido Hooiveld, PhD
Nutrition, Metabolism & Genomics Group
Division of Human Nutrition
Wageningen University
Biotechnion, Bomenweg 2
NL-6703 HD Wageningen
the Netherlands
tel: (+)31 317 485788
fax: (+)31 317 483342
email:      guido.hooiveld@wur.nl<mailto:guido.hooiveld@wur.nl>
internet:   http://nutrigene.4t.com
http://scholar.google.com/citations?user=qFHaMnoAAAAJ
http://www.researcherid.com/rid/F-4912-2010


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