Re: Any problem with the design matrix or the contrasts? Thanks
Federico Lasa <[email protected]>
| Newsgroups | gmane.science.biology.informatics.conductor |
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| Message-ID | <CAE8W1T02fx-80G3faJdoeAAj6D_KMnggX-arxR9QVAS6L4xfnQ@mail.gmail.com> |
The difference is that chip is treated as a numeric variable in one case and as a factor variable in the other. I believe the second case is more aproppiate. On Aug 8, 2014 4:03 PM, "Rao,Xiayu" <XRao-Q/UuGF2ZTDRxtahE/[email protected]> wrote: > Thank you for your kind suggestion, Federico! > > I tried your suggested design, and found the result is different. There > are 10 sig DEGs in the end. > > design <- model.matrix(~0+AR+gender+chip,data=targets) > > design > ARneg ARpos genderM chip > 1 0 1 0 1 > 2 1 0 0 1 > 3 1 0 1 2 > 4 0 1 1 2 > 5 1 0 0 2 > 6 0 1 1 2 > 7 0 1 1 2 > 8 0 1 1 3 > 9 0 1 1 3 > 10 0 1 1 3 > 11 0 1 1 3 > 12 0 1 0 4 > 13 0 1 0 4 > 14 0 1 1 4 > 15 0 1 1 4 > 16 0 1 0 5 > 17 0 1 1 5 > 18 0 1 1 5 > > I also tried this design, which I originally thought would be the same as > yours, but is different. There are 3 sig DEGs in the end. Could you please > explain the cause of the difference ?? > > gender <- factor(targets$gender) > > AR <- factor(targets$AR) > > chip <- factor(targets$chip) > > design <- model.matrix(~0+AR+gender+chip) > > design > ARneg ARpos genderM chip2 chip3 chip4 chip5 chip6 > 1 0 1 0 0 0 0 0 0 > 2 1 0 0 0 0 0 0 0 > 3 1 0 1 1 0 0 0 0 > 4 0 1 1 1 0 0 0 0 > 5 1 0 0 1 0 0 0 0 > 6 0 1 1 1 0 0 0 0 > 7 0 1 1 1 0 0 0 0 > 8 0 1 1 0 1 0 0 0 > 9 0 1 1 0 1 0 0 0 > 10 0 1 1 0 1 0 0 0 > 11 0 1 1 0 1 0 0 0 > 12 0 1 0 0 0 1 0 0 > 13 0 1 0 0 0 1 0 0 > 14 0 1 1 0 0 1 0 0 > 15 0 1 1 0 0 1 0 0 > 16 0 1 0 0 0 0 1 0 > 17 0 1 1 0 0 0 1 0 > 18 0 1 1 0 0 0 1 0 > 19 1 0 1 0 0 0 0 1 > 20 1 0 1 0 0 0 0 1 > > Actually, what I did after my post was to treat the chip as a random > effect instead of a fixed batch effect (although I though the code in my > last post was correct), because I thought the within batch data is not well > balanced and applying a mixed model may be safer. There are 11 sig DEGs > generated from the mixed model, including the above 10 and 3 sig genes. > Please add any thoughts you have so that I can learn more. Thank you very > much ! > > Thanks, > Xiayu > > > > > -----Original Message----- > From: Federico Lasa [mailto:[email protected]] > Sent: Friday, August 08, 2014 3:22 PM > To: Rao,Xiayu > Cc: bioconductor-J/1JLT8/[email protected] > Subject: Re: [BioC] Any problem with the design matrix or the contrasts? > Thanks > > Both a question and a quick suggestion.. > > For the first case, should/does > > design <- model.matrix(~0+AR +gender +chip, data=targets) > cm<-makeContrasts(ARpos-ARneg, levels=design) > > produce the same results you have? > > On Fri, Aug 1, 2014 at 10:07 AM, Rao,Xiayu <XRao-Q/UuGF2ZTDRxtahE/[email protected]> wrote: > > Hello, > > > > I learned from posts in the forum and analyzed my data using the > suggested design and contrasts. It turns out there are no sig genes or few > in the end. Could you please help check if the design does not fit the data > or the contrasts made are incorrect?? > > > > #the research questions are 1) to compare between pos and neg for AR, > and 2) Male.pos vs. Female.pos. > > > > Treat <- factor(paste(targets$gender,targets$AR,sep=".")) > > chip <- factor(targets$chip) > > design <- model.matrix(~0+Treat+chip) > > colnames(design)[1:4] <- levels(Treat) fit <- lmFit(y,design) > > > > cm <- makeContrasts(posVSneg=(F.pos+M.pos-F.neg-M.neg)/2, > > MposVSFpos=M.pos-F.pos, levels=design) > > fit2 <- contrasts.fit(fit,cm) > > fit2 <- eBayes(fit2) > > topTable(fit2, coef="posVSneg", sort.by="p") #0 sig genes > > topTable(fit2, n=20,coef="MposVSFpos", sort.by="p") #4 sig > genes > > > > targets.txt > > sample gender AR chip > > s1 F pos 1 > > s2 F neg 1 > > s3 M neg 2 > > s4 M pos 2 > > s5 F neg 2 > > s6 M pos 2 > > s7 M pos 2 > > s8 M pos 3 > > s9 M pos 3 > > s10 M pos 3 > > s11 M pos 3 > > s12 F pos 4 > > s13 F pos 4 > > s14 M pos 4 > > s15 M pos 4 > > s16 F pos 5 > > s17 M pos 5 > > s18 M pos 5 > > s19 M neg 6 > > s20 M neg 6 > > s21 F neg 6 > > s22 F pos 6 > > s23 F pos 6 > > s24 F neg 6 > > s25 F pos 6 > > s26 F pos 6 > > s27 F pos 6 > > s28 M neg 6 > > > > > > Thanks, > > Xiayu > > > > [[alternative HTML version deleted]] > > > > _______________________________________________ > > Bioconductor mailing list > > [email protected] > > https://stat.ethz.ch/mailman/listinfo/bioconductor > > Search the archives: > > http://news.gmane.org/gmane.science.biology.informatics.conductor > [[alternative HTML version deleted]] _______________________________________________ Bioconductor mailing list [email protected] https://stat.ethz.ch/mailman/listinfo/bioconductor Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor