Dear Javier,
I am pretty sure more experienced member would have a lot and deeper things
to say about your question.

Here is my 25 cent:
Model based statistic (moderated t statistic) and permutation tests are two
different flavors of testing the Null Hypothes[es|is]. Comparing these two
flavors, in my case, will be equivalent to comparing apple and oranges.

Each of these methods have their own advantages. If the model suits well -
moderated/unmoderated t - statistic should be preferred. If you have no idea
of what the model is OR/AND if you are not sure if the model assumptions
hold for the data then - permutation test would be a "wiser" (but not
necessarily better) choice.

A lot can be said to the above discussion - but permutation test will always
exist but might not give superior results to what you model based test
statistic would give (t-test is quiet robust to assumptions).

This should apply to your example as well. You are allowed to used moderated
t statitic

Please correct if I am wrong. I am also learning my statistics :-)

Thanks and Best Regards,
S.

2009/11/23 Javier Pérez Florido <jpflorido@gmail.com>

> Dear list,
> I'm new using GSEAbase and I've seen some examples given in
> "Bioconductor case studies" book. A data example is given according to
> the following steps:
>
>    * Nonspecific filtering on expression data object.
>    * Building the GeneSetCollection using KEGG (for example).
>    * Compute the per gene test statistics using t-test
>    * Use of a permutation test to assess which genes have an unusually
>      large absolute value of the distribution.
>
> My question is: can we use any kind of statistic? For example, moderated
> t-statistic using limma?I know that limma uses the eBayes function,
> which employs information from all genes to arrive at more stable
> estimates of each individual gene's variance and I don't know if, in
> GSEA context, it is correct to use this moderated statistic which takes
> into account all the genes (it is not like the "standard" per gene
> statistic t-test).
>
> Thanks,
> Javier
>
>
>
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