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 >>> ----------------------------------------------------- >>> >>> >> >> _______________________________________________ >> 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 >> > >