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

[Jason Shoemaker]

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
> -----------------------------------------------------
>
>