Dear Mike,
Thanks for the confirmation, I remember talking to someone during the Bioc2013 lab saying that same thing on shrunken log2FC but I do not know why I thought that the value to use with pre-ranked was not the log2FC in the results object but another one due to the shrinking procedure.

On the other hand, on the workflow from the lab, I remember that in 5.1 log2FC was used to test whether particular gene sets had an average log2FC different from 0. That would use log2FC information but in a different way compared to how I understand GSEA pre-ranked analysis (the procedure published by the people in Broad). In the Broad pre-ranked strategy, the enrichment using log2FCs from the top (positive and negative) is giving you an enrichment score that should be more related, in my view, to biological relevance since it is linked to highest log2FCs observed, in absolute values, whereas the workflow in 5.1 would flag those gene sets that do show a different behaviour but maybe very low just because we are mathematically able to say so (because of the t-test used).

By the way, could the thresholded test implementation in DESeq2 >1.3, or something similar, be used in a similar way to filter for those gene sets with a more relevant “enrichment” using the 5.1 workflow?
Thanks again for the efforts

Jose



On 17 Jan 2014, at 16:10, Michael Love <michaelisaiahlove@gmail.com<mailto:michaelisaiahlove@gmail.com>> wrote:

hi Garcia,

For DESeq2, we do recommend using shrunken log fold changes for gene set tests, that is, the log2FoldChange column of a results object when the default DESeq2 pipeline has been used.

For a suggested workflow, see section 5.1 of the PDF for the presentation "DESeq2/DEXSeq / Alejandro Reyes, Wolfgang Huber", one of the Bioc2013 labs: http://bioconductor.org/help/course-materials/2013/BioC2013/

Mike



On Fri, Jan 17, 2014 at 3:30 AM, Garcia Manteiga Jose Manuel <garciamanteiga.josemanuel@hsr.it<mailto:garciamanteiga.josemanuel@hsr.it>> wrote:
Hi,
I wanted the same and this is what gsea people replied to me some months ago:


Hi,

Thank you for your interest in GSEA.

R GSEA does accept ranked list as an input.

Please note that R GSEA has not been actively maintained since 2005.

Regards,


I have not gone through the R GSEA again to check how pre-ranked lists can be accepted, but the last sentence in their message made me hold a minute and think in other possibilities.
For the moment I am still doing as you mentioned, using the rnk file with java, then getting back again from results of gsea to R.

By the way, I would like to known which metric should be best to be used for that kind of analysis when using RNA-Seq data coming from DESeq2 analysis. log2FC? p-values? Are they considered to be weighted, as GSEA pre-ranked names them?
Thanks
Regards



On 17 Jan 2014, at 00:18, Daniel Schmolze <bioconductor@schmolze.com<mailto:bioconductor@schmolze.com><mailto:bioconductor@schmolze.com<mailto:bioconductor@schmolze.com>>> wrote:

I want to do a GSEA entirely from within R, using genes ranked by my
own metric. At the moment I'm saving my ranked genes to a .rnk file,
then calling Broad's GSEA java program, then reading the resulting
output back into R (all I care about are the p-values in
gsea_report_for_na_pos_####.xls and gsea_report_for_na_neg_####.xls).
Cumbersome to say the least.

As far as I can tell, the Broad GSEA R script won't accept pre-ranked
genes, but maybe I'm wrong? If not, I'm interested in other options.
I'd like to specifically stick with the Broad GSEA algorithm if
possible.

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