On Wed, Apr 22, 2009 at 11:42 AM, Daniel Brewer <daniel.brewer@icr.ac.uk>wrote:

> Hello,
>
> I have five groups of samples that I have microarray data for and I
> would like to know what genes make that group distinct from the other
> groups.
>
> The initial approach I took was to use limma to do a comparison between
> one group and the rest, for example:
>
> design1 <- model.matrix(~pData(alignedSetlur)$group ==1)
> fit <- lmFit(alignedSetlur,design=design1)
> fitone <- eBayes(fit)
> topTable(fitone,coef=2,n=Inf,p.value=0.05)
>
> I did this for each group, but I found that there was a large amount of
> overlap between the significant genes in one group and another (over 50%
> in some cases) and this doesn't seem correct.
>
> What is the best way to answer this sort of question?  Some sort of
> multivariate analysis or PCA?
>

Hi, Dan.  You might want to look at decideTests() in limma.  Also, some of
the machine learning algorithms (randomForests, for example) will generate
class-specific importance measures for features.

Sean

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