[BioC] enrichment packages that accept t-stat (or related stat) as input

[Robert Castelo]

hi Juliet,

On 08/30/2013 02:43 AM, Juliet Hannah wrote:
> 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.

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.

> Thanks,
>
> Juliet
>
>
> On Thu, Aug 29, 2013 at 2:43 AM, Robert Castelo <robert.castelo at upf.edu
> <mailto:robert.castelo at upf.edu>> wrote:
>
>     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
>     <http://www.bioconductor.org/packages/release/bioc/html/Category.html>
>
>     may be doing what you are looking for.
>
>     cheers,
>     robert.
>
>
>     On 08/28/2013 08:04 PM, Juliet Hannah wrote:
>
>         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
>
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>
>     --
>     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 <tel:%2B34.933.160.514>
>     fax: +34.933.160.550 <tel:%2B34.933.160.550>
>
>

-- 
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