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 [[alternative HTML version deleted]]

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 > > > > [[alternative HTML version deleted]] > > _______________________________________________ > Bioconductor mailing list > Bioconductor@stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor > [[alternative HTML version deleted]]

Hi Javier, It's ok, as long as you repeat the whole eBayes procedure for each permutation. The smoothed standard errors are statistically independent of the moderated t-statistics, hence independent of your category inference. You might also consider the roast() and romer() functions which use the empirical Bayes statistics explicitly. Best wishes Gordon > Date: Tue, 24 Nov 2009 10:16:46 +0100 > From: Javier P?rez Florido <jpflorido at="" gmail.com=""> > Subject: Re: [BioC] GSEAbase and limma > To: Sunny Srivastava <research.baba at="" gmail.com=""> > Cc: bioconductor at stat.math.ethz.ch > Message-ID: <4B0BA47E.8010103 at gmail.com> > Content-Type: text/plain; charset=ISO-8859-1; format=flowed > > 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 escribi?: >> 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 P?rez 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