[BioC] GSEA, topGO, GOstats...? what's a good way to look at GO over-representation?

[michael watson (IAH-C)]

These all are a little cryptic!

I have some sample code for topGO that doesn't use AFFY ids, it uses a dataset that I can't give out, but at least it's not affy.

I ended up writing my own code to do this that works from data.frames etc and sucks the latest annotation directly from the web, rather than using the bioc annotation packages.  Some of this was wrapped into our package CORNA (http://bioinformatics.iah.ac.uk/software/corna)

Also, are you devoted to R?  If not, then why not use something like FatiGo?  http://www.fatigo.org/

Mick

-----Original Message-----
From: bioconductor-bounces at stat.math.ethz.ch [mailto:bioconductor-bounces at stat.math.ethz.ch] On Behalf Of J.delasHeras at ed.ac.uk
Sent: 08 February 2010 16:30
To: bioconductor
Subject: [BioC] GSEA, topGO, GOstats...? what's a good way to look at GO over-representation?


Dear list,

I have a few gene lists derived from a human Illumina expression  
array. I just have Illumina IDs, I have gene names, and I have entrez  
gene IDs I obtained for them.

I would like to analyse the list to look for over-representation of  
some category, probably using gene ontologies.
I see there are several packages that seem to address this, although  
when I look at the examples I get the feeling they were designed with  
Affy arrays in mind and depend on an Affy array design...

I am sure I am not the only one wanting to do this type of work on  
non-Affy arrays... I would appreciate a nudge towards the right  
package, or a way to "persuade" it to work with non-Affy array data,  
after all I imagine that all the array design is used for is the  
definition of teh genelists/universe and retrieval of the relevant GO  
ids.

Thank you for any helpful comments.

Jose

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Dr. Jose I. de las Heras                      Email: J.delasHeras at ed.ac.uk
The Wellcome Trust Centre for Cell Biology    Phone: +44 (0)131 6513374
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University of Edinburgh
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