[BioC] GSEA, topGO, GOstats...? what's a good way to look at GO over-representation?
whuber at embl.de
Mon Feb 8 17:55:41 CET 2010
Chapter 13 of the 'Bioconductor Case Studies' book is a good start, as
and the vignette of the GSEABase package.
Let yourself not be confused by the fact that in some functions (eg
GOstats), there is support to make it easier to work with the
Bioconductor annotation packages (which are provided, among others, for
Affymetrix genechips). The concept of gene set enrichment analysis
itself is independent of where you get the gene sets from, and the
software above works with general gene lists.
And if you do not care so much about automation, reproducibility and
flexibility of your workflow, then using websites like mentioned by
Michael to copy-paste your gene lists into might be the way to go.
J.delasHeras at ed.ac.uk scripsit 02/08/2010 05:29 PM:
> 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.
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