[BioC] RE : gsea (gene set enrichment analysis) for ranked lists

[Asta Laiho]

Hi Martin,

Thanks for your advice. I tried looking at the vignette but couldn't directly see how I could carry out my mission using this package. Could you point out to me a bit more specifically which functions I should use for example to test for GO BP enrichment (and listing p-values for top GO BP terms) in my ranked list of human genes (input are rank values instead of test statistics). 

Many thanks in advance!

- Asta

On Apr 6, 2011, at 9:44 PM, Martin Morgan wrote:

> On 04/06/2011 11:41 AM, Simon Noël wrote:
>> Hi,
>> 
>> I do use the R version of GSEA but I don't know any bioconductor package or other tool that do that.  If you find any, let me know.  I am looking for that to.
>> 
>> Simon Noël
>> CdeC
>> ________________________________________
>> De : bioconductor-bounces at r-project.org [bioconductor-bounces at r-project.org] de la part de Asta Laiho [asta.laiho at btk.fi]
>> Date d'envoi : 5 avril 2011 09:35
>> À : bioconductor at r-project.org
>> Objet : [BioC] gsea (gene set enrichment analysis) for ranked lists
>> 
>> Hi,
>> 
>> I have been using Broad Institute's GSEA tool for gene set enrichment analysis tool in analyzing preranked lists.  This allows me to perform statistical testing between the sample groups without coupling this directly to the enrichment analysis but rather to do these steps in a modular way. This also enables me to sort the genes according to my preferred logic and then analyze gene enrichment in a way that ignores the direction of the differential expression (up/down).  The drawback of the Broad GSEA implementation is that all the annotations used are human based. I have been trying to search for an alternative approach within R/Bioconductor but haven't been able to find one so far that would fully meet the following criterion:
>> 
>> - Allows one to test gene enrichment for preranked gene lists (works with ordered lists of gene symbols/identifiers rather that actual expression value matrixes and thus is not connected to a certain way of gene expression testing between sample groups)
>> - Is available for a number of organisms and gene set annotations (at least GO and KEGG)
>> - Allows one to ignore the direction of the regulation and concentrate on generally differentially expressed genes
>> 
> 
> The vignette in the Categories package provides a reasonable starting point for customizing analyses. Martin
> 
>> If someone is aware of a tool that would meet all these criterion, I would be very happy to know. Otherwise this can be regarded as a wish for such a method to be implemented in R/Bioconductor environment.
>> 
>> Greetings,
>> Asta
>> 
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