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

[Jason Shoemaker]

Dear all,

Great! Thanks for all the advice. I was doing 
exactly as Jenny recommended, but I've switched 
the code to simply cycle through the topTable 
coefficients and stack the results into a large 
data frame (as Mark recommended).  I also played 
with the decideTests() which proofed useful in 
constructed a scenario plots (#genes significant 
for each contrast, recommended by Sean).
Thank you all!
Jason

On 11/19/2010 6:56 AM, Jenny Drnevich wrote:
> 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
>>>>
>>>> _______________________________________________
>>>> Bioconductor mailing list
>>>> Bioconductor at stat.math.ethz.ch
>>>> https://stat.ethz.ch/mailman/listinfo/bioconductor 
>>>>
>>>> Search the archives: 
>>>> http://news.gmane.org/gmane.science.biology.informatics.conductor 
>>>>
>>>
>>>
>>> ----------------------------------------------------- 
>>>
>>> Mark Cowley, PhD
>>>
>>> Peter Wills Bioinformatics Centre
>>> Garvan Institute of Medical Research, Sydney, 
>>> Australia
>>> ----------------------------------------------------- 
>>>
>>>
>>
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>
>