Dear Tom
We did a direct head to head comparison of these and other microarray
gene selection approach a few years back.

Briefly, if you have very low sample size or high noise in the data,
then Rank Products does well as its difficult to estimate the true mean
or variance. Limma does better than classical statistical methods as it
uses a moderated variance estimate.

However as the data improves (greater sample size, higher signal:noise),
classical statistical tests that utilize both mean and variance estimate
do better than Rank Products. Limma also performs well in this case.

Overall, we recommended limma as it performs well in across each scenario.

Jeffery IB, Higgins DG, Culhane AC. (2006) Comparison and evaluation of
methods for generating differentially expressed gene lists from
microarray data. BMC Bioinformatics. 7:359

http://www.biomedcentral.com.ezp-prod1.hul.harvard.edu/1471-2105/7/359

Regards
Aedin

> Message: 1
> Date: Wed, 17 Feb 2010 15:10:49 +0100
> From: Juan Carlos Oliveros <oliveros@cnb.csic.es>
> To: bioconductor@stat.math.ethz.ch
> Subject: [BioC] limma and Rank Products: comparison of the number of
> 	results
> Message-ID: <4B7BF8E9.5050105@cnb.csic.es>
> Content-Type: text/plain; charset=ISO-8859-1; format=flowed
>
> Dear all
>
> When working with comparative experiment based on Affymetrix gene 
> expression arrays I usually apply one of the following combination of 
> methods:
>
> RMA + limma + FDR
>
> or
>
> RMA+ Rank Products
> (rank products "Percentage of false prediction" values are supposed to 
> be equivalent to FDR)
>
> Usually we obtain much more differentially expressed genes when using 
> Rank Products than when using limma at the same FDR threshold.
>
> I wonder if in your case is the same. Do you obtain many more results 
> with Rank Products than with limma at the same FDR cutoff?
>
> In a recent experiment we obtained the opposite (more results with 
> limma) and I'd like to know your experience when using both methods 
> regarding the number of results.
>
> best,
>
> Juan Carlos Oliveros, Ph.D.
> CNB-CSIC, Madrid, Spain
>
>
>
>
>
>   


-- 
Aedi'n Culhane,
Computational Biology and Functional Genomics
Harvard School of Public Health, Dana-Farber Cancer Institute

44 Binney Street, SM822C
Department of Biostatistics and Computational Biology,
Dana-Farber Cancer Institute
Boston, MA 02115
USA

Phone: +1 (617) 632 2468
Fax:   +1 (617) 582 7760
Email: aedin@jimmy.harvard.edu
Web URL: http://www.hsph.harvard.edu/research/aedin-culhane/



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