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

Pekka Kohonen pkpekka at gmail.com
Thu Oct 24 12:00:46 CEST 2013

Dear Juliet, Gordon,

I am also looking into using pre-computed camera statistics, both to
speed up computation for a webservice and also to enable statistics,
such as F-statistic to be used that are not currently supported by the
limma/camera package (AFAIK). So I am trying to de-compose the
limma/camera-function to be able to make use of pre-computed
statistics. I wonder if someone has already done so? Could the
F-statistic (as estimated by the write.fit function for instance) be
used in camera directly, or are there some statistical assumptions
that are violated? Probably using the rank-based version is the safest

It seems to me that in order to use as much as possible pre-computed
statistics in limma (when the gene sets are not known in advance) you
can pre-compute the limma/ebayes gene wise statistics and array
weights. But you have to still estimate the variance inflation factor
for each gene set. But the same factor can be used for all the
comparisons in the linear model.

It would be nice to have a "write.fit" type function for the gene-set
tests as well. It is one of my favorite functions in limma.

I have used GSVA to perform linear modelling for gene set testing as
well, but don't completely trust the statistical validity of the
results. Maybe setting the trend=TRUE would alleviate some
considerations about assumptions about normality being violated. Also
it needs at least 10 samples (apparently) to estimate the distribution
of gene set statistics. But that is OK for dose-response modelling.

Thank you Gordon for your work on the limma! I am also finding the
"voom" to be a really nice function and have used it to analyze
laber-free proteomics experimetns as well.

Best Regards,


2013/8/30 Gordon K Smyth <smyth at wehi.edu.au>:
> 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
>> Message: 19
>> Date: Thu, 29 Aug 2013 20:43:04 -0400
>> From: Juliet Hannah <juliet.hannah at gmail.com>
>> To: Robert Castelo <robert.castelo at upf.edu>
>> Cc: Bioconductor mailing list <bioconductor at r-project.org>
>> Subject: Re: [BioC] enrichment packages that accept t-stat (or related
>>         stat) as input
>> 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
>> On Thu, Aug 29, 2013 at 2:43 AM, Robert Castelo
>> <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
>>>>         [[alternative HTML version deleted]]
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>>> --
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>>> Associate Professor
>>> Dept. of Experimental and Health Sciences
>>> Universitat Pompeu Fabra (UPF)
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>>> telf: +34.933.160.514
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