On Thu, Nov 18, 2010 at 7:07 AM, Mark Cowley <m.cowley@garvan.org.au> wrote:

> 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.
>>
>>
Hi, Jason.  Be sure to take a look at the decideTests() function that Mark
mentioned in one of his previous replies.  I can't tell exactly what you are
after, but a common use case of needing to decide what contrasts are
significant when multiple contrasts are being evaluated simultaneously is
handled by decideTests().

Sean


> 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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