[BioC] decideTests with nestedF

[Ariel Chernomoretz]

Hi Pedro,

you can check if something went wrong using the following code:
 
  padj=apply(limma.fit$p.value,2,p.adjust,method="fdr")
  maux=padj*abs(resDecideTest)
 
 #report genes with at least one pvalue significant
  i<-apply(maux,1,function(x){any(x)})       
  cat(paste("# DE genes:",sum(i),"\n"))
 
 #report how many contrasts classified as +1 or -1 by decideTests
  #get a separated adjusted pvalue >0.05
  i<-apply(maux,1,function(x){!any(x<0.05)})
  cat(paste("#inconsistencies:",sum(i),"\n")) 



  limma.fit is your limma fit result
  resDecideTest is the output of decideFunction (with "nestedF" option 
specified)

Hope this helps

Ariel./


Pedro López Romero wrote:

>Hi Jim, 
>
>  
>
>>Not sure I understand your point. Are you saying that a particular 
>>contrast that appears to be significant using your method ends up having 
>>a very large p-value if you use nestedF?
>>    
>>
>
>
>Not exactly. The problem is that using decideTests(..., method="nestedF")
>with the whole set of genes  (not filtered by any method), some of the
>selected genes have an adjusted p-value quite large, corresponds to genes
>with a very small M value.- Theses genes are in the list of differentially
>exprssed genes  selected with decideTests(..., method="nestedF").
>
>For example, here I show you two of the genes for a particular contrast that
>would be selected using decideTests(...,method="nestedF" )
>
>
>	M	A		t		P.Val		adj.P.Val
>1.064610409	13.0019494	4.18633292	0.000240894	0.239943977
>0.489386597	8.228402648	2.86816475	0.007621841	0.817992583
>
>
>I am a bit confused with this result?, Any clue? 
>
>Pedro.-
>
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