[BioC] limma and Rank Products: comparison of the number of results

[Thomas Hampton]

Hello Aiden,

A quick question...

In your fine paper, you mention that the Empirical Bayes Statistic
returns the log odds that a gene is differentially expressed. This  
says to me that
eBayes returns the probability of the alternative hypothesis given the  
data.

Based on the F statistic, I would have thought  eBayes()
returns a p value of sorts, i.e., the probability of the data given  
the null.

If eBayes really gives the probability of differential expression,  
that would be great,
but that would require an a priori knowledge of differential  
expression, the very thing
we are investigating with the array.

Best,

Tom
On Feb 18, 2010, at 10:33 AM, Aedin Culhane wrote:

> 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 at cnb.csic.es>
>> To: bioconductor at stat.math.ethz.ch
>> Subject: [BioC] limma and Rank Products: comparison of the number of
>> 	results
>> Message-ID: <4B7BF8E9.5050105 at 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 at jimmy.harvard.edu
> Web URL: http://www.hsph.harvard.edu/research/aedin-culhane/
>
>
>
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>
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