Dear all

unfortunately I did not get any reply on my post, so thats why I am asking
again,
assuming that lots of people already came across that problem.

Working with an array set ( cDNA or any single color platform) just means
that the probes you are interested in, are spread out over more than one
array
(usually due to space limitations),
So sample samples, but different features.

But actually that kind of separation of the probes is rather random.
The question arises at which level of the analysis the arrays should be
aggregated.

I think the normalization and also the model fitting should be done
separately.

But as we do not only consider contrasts within each array of the array set,
but at the contrast,
we want to look at the results of all arrays at the same time, the p-values
must be adjusted somehow for
this array-effect.

To do this in a "global" manner similar to the "global method" of
decide.tests will probably result in being overly
conservative.

Any suggestions?

Best,
Tefina

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