[BioC] Methods in decideTests (limma)

Michal Kolář kolarmi at img.cas.cz
Fri Jun 5 15:38:19 CEST 2009

Dear Gordon,

thank you for your answer. I read the Section 10.3, yet I wanted to  
get little more details. I should have searched in the List, as now I  
have been able to find answers to many of my questions in previous  
posts (by you and James W. MacDonald). I, however, still have one  

In one of the posts James W. MacDonald writes:

>> From: jmacdon at med.umich.edu (James W. MacDonald)
>> Date: Tue, 10 Oct 2006 15:42:17 -0400
>> Subject: [BioC] Limma nestedF
>> ...
>> I am not sure the nestedF method is appropriate for this situation,
>> because you have interaction terms. When there is an interaction  
>> term in
>> the model, the usual thing to do is to check for significance of the
>> interaction term, and if it isn't significant, then you would drop it
>> from the model and check for significance of the main effects  
>> terms. The
>> nestedF method won't do this - it will treat all the contrasts as  
>> if equal.
>> ...

I have a 2*2 factorial design with surgery and treatment factors and  
their interaction

 > design <- model.matrix(~surgery*treatment).

The interaction term is potentially of interest. Do I gauge the  
importance of the interaction term using decideTests with the method  
"global" or "separate"? Or should I estimate the importance directly  
by observing the distribution of p-values for the interaction term?  
If so, should I remove all contrasts with, say, flat distribution of  
p-values from the decideTests("global") call?

Thank you for your help,

PS: When trying to understand the functions, I spotted a possible  
minor bug in classifyTestsF: A global variable 'contrasts' may be  
used in rare cases in the 'Return TestResults matrix' chunk of the  
code. (The bug is present in my version limma_2.16.5 as well as in  
the development version 2.19.1)

On 5 Jun 2009, at 03:47, Gordon K Smyth wrote:

> Dear Michal,
> No there is no publication on these or related methods.  But have  
> you read Section 10.3 "Testing across contrasts" in the limma  
> User's Guide which discusses these methods?  You might also find it  
> helpful though to search for my replies to previous questions about  
> decideTests on this mailing list.
> I recommend "global" whenever you have a series of contrasts that  
> you are analysing together, especially if you want to compare the  
> number of DE genes that you find.  There is no requirement that the  
> contrasts be of similar "strength".  An advantage of "global" is  
> that the same t-statistic cutoff is used for all contrasts (in the  
> absence of weights or NAs). However you need to be aware that the  
> number of DE genes for any contrast will depend on what other  
> contrasts it is tested with -- contrasts with lots of DE genes will  
> pull the others up.
> I recommend "separate" if you are analysing different contrasts for  
> different purposes, so the numbers of DE genes are not comparable.
> "nestedF" has a specialist purpose in trying to give more weight to  
> genes which are simultaneously DE in two or more several  
> contrasts.  Both this and "hierarchical" are somewhat experimental  
> in that I have not published the theory, and therefore I don't  
> strongly recommend them.
> Best wishes
> Gordon
>> From: Michal Kol?? <kolarmi at img.cas.cz>
>> Subject: [BioC] Methods in decideTests (limma)
>> To: bioconductor at stat.math.ethz.ch
>> Message-ID: <62C854CA-B73C-4B73-A383-51ED398CCCE3 at img.cas.cz>
>> Content-Type: text/plain; charset=UTF-8; delsp=yes; format=flowed
>> Dear list,
>> I would like to ask on the theory behind various methods in
>> decideTests. The method "separate" is clear to me, giving p-value
>> adjustment for each contrast separately. The method "global" may be
>> used presumably when there are two or more contrasts of similar
>> strength in the experiment. The method "nestedF" seems to me similar
>> to the "global", yet it considers for p-value adjustment only those
>> genes for which F-test is significant after p-value correction
>> (roughly said). Is that correct? And finally, the method
>> "hierarchical" considers for the final p-value adjustment only those
>> genes/contrasts for which adjusted p-value (of the gene in the
>> contrast treated separately) is smaller than some cut-off. Is that
>> right?
>> My next question is whether any of the methods "global",
>> "hierarchical" or "nestedF" can be used to correct p-values for
>> contrasts that have different strength (different number of expected
>> DEGs)? In my case 'different' means hundreds of DEGs for one contrast
>> and tens for the other.
>> Can I find more details on the methods in some reference?
>> Cheers,
>> Michal
>> --
>> -----------------------------------------------------
>> Michal Kolar
>> Academy of Sciences of the Czech Republic
>> Institute of Molecular Genetics

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