This is actually the sort of situation where you might be better off with
limma, since the tools for dealing with technical replicates in a nested
factorial design (cultivar*resistance) are much better documented in the
Limma user's guide.

CMA, in my experience, excels at one-way classification problems, but it
sounds like you are looking to model which genes change expression according
to cultivar, which change according to resistance, and which change as a
function of both.  Using lmFit() and eBayes() then ranking the top results
for each factor might be a more fruitful approach than shoehorning your
problem into CMA.

But, that's just my opinion, based on my understanding of your experiment.

--t

On Fri, Jun 24, 2011 at 3:03 AM, Clara Pons <cpons@upvnet.upv.es> wrote:

> Hi to all,
> I'm new using R and CMA. I want to create some classifiers/biomarker from
> peach microarray data using using the different classification methods
> implemented in CMA. My problem is that I don't kown how to prepare data and
> introduce class labels given all examples are based in the prepared dataset
> golub, but no in a raw gene expression matrix.
> My goal is to create classifiers based in genotypes and the observed
> phenotypes. I have a gene expression matrix with 3533 genes (rows) and 48
> microarrays corresponding to 16 samples (replicates each). Out of them 8 are
> from a sensitive  and 8 from a tolerant cultivars. Samples are taken at
> harvest and ripe and two different treatments (3 points each).
> Please can somebody tell me wich commands I should type to define labels?
>
> Thanks a lot in advance
>
> Clara
>
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