[BioC] Using eBayes to find P values for individual contrasts
Jason Shoemaker
jshoe at ims.u-tokyo.ac.jp
Fri Nov 19 02:49:01 CET 2010
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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>
>
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