Hi all, I'd like to investigate how significant GO terms are distributed over the GO graph for RNA-seq differentially expressed genes checked for over-reppresentation using the procedure available in the goseq package. The topGO package (see, for example, the chapter 7.4 of the included vignette) allows this kind of visualization for microarray data. Any idea how this kind of visualization can be produced for RNAseq data inspected using goseq? Any idea would be appreciated! Regards, Paolo [[alternative HTML version deleted]]

Hi Paolo, Not quite sure what you want from your post, but you might want to have a look at the vignette titled " Visualizing Data Using GOstats" in the GOstats package. It shows many ways to use GO.db and how to visualize the structure of the gene ontologies. You can see it here: http://www.bioconductor.org/help/bioc- views/devel/bioc/html/GOstats.html Marc On 03/28/2011 03:12 AM, Paolo Sonego wrote: > Hi all, > I'd like to investigate how signi�cant GO terms are distributed over the GO > graph for RNA-seq differentially expressed genes checked for > over-reppresentation using the procedure available in the goseq package. The > topGO package (see, for example, the chapter 7.4 of the included vignette) > allows this kind of visualization for microarray data. > Any idea how this kind of visualization can be produced for RNAseq data > inspected using goseq? > > Any idea would be appreciated! > > Regards, > Paolo > > [[alternative HTML version deleted]] > > > > _______________________________________________ > Bioconductor mailing list > Bioconductor@r-project.org > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor [[alternative HTML version deleted]]