[BioC] Using eBayes to find P values for individual contrasts

[Jenny Drnevich]

Hi Jason,

In short, topTable can only give you the adjusted p-values for a 
single contrast at a time (or a series of contrasts, but it's 
calculating the overall F-value, not individual t-values). Instead, 
see write.fit(). This only writes out to a file, but I often just 
read it back in to R.  You could also just do this:

indiv.P.values<-apply(fit2$p.values, 2, p.adjust, method="fdr");

Cheers,
Jenny

At 08:58 PM 11/17/2010, Jason Shoemaker wrote:
>Dear Mark,
>
>Thank you for the warning! I was worried about asking something 
>silly. So if I may ask, how can I get topTable to display not just a 
>single adjusted p value for one contrast, but the adiusted P values 
>for all contrasts? I don't seem to see this option. Thus I have been 
>applying p.adjust to the raw P values to adjust the values for each 
>contrast of interest.
>
>Thank you!
>Jason
>
>On 11/17/2010 10:32 AM, Mark Cowley wrote:
>>Hi Jason,
>>I think you're in danger of reinventing the wheel.
>>
>>The adj.P.Val column in the topTable is the corrected p value. 
>>Don't forget about the coef topTable parameter to control which 
>>coefficient to look at. You can control what method to use via the 
>>adjust.method parameter.
>>
>>then take a look at the decideTests method to work out which genes 
>>are significant  for which contrasts.
>>
>>cheers,
>>mark
>>
>>On 16/11/2010, at 7:28 PM, Jason Shoemaker wrote:
>>
>>>Dear all,
>>>
>>>I have searched the archives but not found any advice on this 
>>>issue. I am using the LIMMA package to determine differentially 
>>>expressed genes. I have been using eBayes plus topTable to find 
>>>the most differentially expressed genes, but now I want to 
>>>determine the adjusted p values specific for each contrast. Should 
>>>I simply do as follows (following the example from 
>>>http://matticklab.com/index.php?title=Single_channel_analysis_of_Agilent_microarray_data_with_Limma):
>>>
>>>contrast.matrix<- makeContrasts("Treatment1-Treatment2", 
>>>"Treatment1-Treatment3", "Treatment2-Treatment1", levels=design);
>>>fit2<- contrasts.fit(fit, contrast.matrix)
>>>fit2<- eBayes(fit2)
>>>
>>>P.values<-p.adjust(fit2$p.values,methods="fdr");
>>>
>>>in doing so- can I make fair comparisons between p values for each 
>>>contrast? Or more precisely, if a get a p value of 0.01 for 
>>>"Treatment1-Treatment2" and large value (P>0.1) for the remaining 
>>>2 contrasts, is that gene significant only for "Treatment1-Treatment2"?
>>>Thank you!
>>>Jason
>>>
>>>_______________________________________________
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>>
>>
>>-----------------------------------------------------
>>Mark Cowley, PhD
>>
>>Peter Wills Bioinformatics Centre
>>Garvan Institute of Medical Research, Sydney, Australia
>>-----------------------------------------------------
>>
>
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