[BioC] GSEAbase and limma

[Javier Pérez Florido]

Dear Sunny,
Thanks for your reply regarding the use of parametric/nonparametric 
statistical tests.
What I wanted to mean is the use of a "global" parametric test such 
limma in the context of Gene Set Enrichment useful for finding 
biological themes in gene sets. My question is if limma is suitable when 
building groups of genes since eBayes function employs information from 
ALL genes, rather than individual genes.... :-)

Javier


Sunny Srivastava escribió:
> 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 at gmail.com 
> <mailto:jpflorido at 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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