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

[Mark Cowley]

Hi Jason,
you normally generate one topTable per contrast of interest. so if you have 3 coefficients of interest, you would call topTable 3 times, each time choosing coef=1, then coef=2, then coef=3 in 3 

I usually just write a for loop for each coef of interest & store the topTables in a list.

cheers,
Mark

On 18/11/2010, at 1:58 PM, 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
>> -----------------------------------------------------
>> 
>> 
>