All,
I am looking for an Bioconductor enrichment package that does something similar to GSEA for pre-computed test statistics. This method would not rely on a cutoff. That is, rather than passing an expression matrix, one can compute summarizes outside of the package (such as a limma t), and then pass these. Any suggestions?
Thanks,
Juliet
Dear Juliet,
Why not use the enrichment functions that are already part of the limma package? See
?roast
?camera
?romer
and references there-in.
Best wishes
Gordon
Hi,
Regarding GSEA; do you know about the so-called 'pre-ranked' analysis that will do exactly what you describe?
http://www.broadinstitute.org/gsea/doc/GSEAUserGuideFrame.html?GSEA_Preranked_Page
Guido
Juliet,
i think the first 5 pages in the vignette entitled "Using Categories to Analyze Microarray Data" from the Category package:
http://www.bioconductor.org/packages/release/bioc/html/Category.html
may be doing what you are looking for.
cheers,
robert.
Robert Castelo, PhD
Associate Professor
Dept. of Experimental and Health Sciences
Universitat Pompeu Fabra (UPF)
Barcelona Biomedical Research Park (PRBB)
Dr Aiguader 88
E-08003 Barcelona, Spain
telf: +34.933.160.514
fax: +34.933.160.550
Hi Robert,
Thanks for your response. I will look into it.
Also is it correct GSVA always requires an expression matrix. It seems that it integrates with limma, so if I have done an analysis in limma does this mean that I should be able to use GSVA for an enrichment analysis.
Thanks,
Juliet
hi Juliet,
the output of GSVA can be used as input to limma but the converse is not true. GSVA calculates sample-wise gene set summaries of gene expression, and therefore, it starts from a gene-by-sample expression data matrix and produces a geneset-by-sample expression data matrix.
limma moderated t-statistics give you a measure of differential expression for every gene over the entire set of samples, i.e., not sample-wise, so it can be seen as one-dimensional vector whose values represent summaries of differential expression for every gene.
to get *geneset* summaries of limma genewise t-statistics you may use the approach i mentioned below (which you can find in the vignette of the Category package). to gather more understand on what this approach is doing, as well as seeing an alternative for detecting not only shifts in mean expression but also in scale, you may consult the following paper, others in the list may give you more appropriate bibliographic references:
Irizarry, Rafael A.; Wang, Chi; Zhou, Yun; and Speed, Terence P., "GENE SET ENRICHMENT ANALYSIS MADE SIMPLE" (April 2009). Johns Hopkins University, Dept. of Biostatistics Working Papers. Working Paper 185. http://biostats.bepress.com/jhubiostat/paper185
i do not know of what R package implements these calculations in a user-friendly way. if you don't find any, i can paste for you here the few lines of code that make the calculations described in this paper.
cheers,
robert.
--
Robert Castelo, PhD
Associate Professor
Dept. of Experimental and Health Sciences
Universitat Pompeu Fabra (UPF)
Barcelona Biomedical Research Park (PRBB)
Dr Aiguader 88
E-08003 Barcelona, Spain
telf: +34.933.160.514
fax: +34.933.160.550
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version 2.3.6
Hi Guido,
Thanks for your response. Yes, I should have used the 'pre-ranked' descriptor. I am looking for a Bioconductor implementation. It seems that in the places I've looked (gsealm, GSVA, Graphite), an expression matrix must be provided (or I missed the other options).
Regards,
Juliet