Hi James, thanks for the explanation. I do not really understand the columns yet. Shouldn't the FC be printed for every comparison as is done for the Coef-columns? I just get one A-column. Is there any way of printing the results to a file with the same columns as in topTable()? What I'm really want to have in the output is a p-value column, an FC column and an adjusted p-value column. Best regards On Tue, Aug 28, 2012 at 5:41 PM, James W. MacDonald wrote: > Hi Jorge, > > > On 8/28/2012 11:20 AM, Jorge Miró wrote: > >> Hi, >> >> I have run the commands below to get an analysis of differential >> expressions in my Affymetrix arrays >> >> #Prepare the design and contrast matrices for my comparisons of the three >> groups in a loop-manner. >> >>> design<- model.matrix(~0+factor(c(1,1,**1,2,2,2,3,3,3))) >>> colnames(design)<- c('GroupA', 'GroupB', 'GroupC') >>> contrast.matrix<- makeContrasts(GroupB-GroupA, GroupC-GroupA, >>> >> GroupC-GroupB, levels=design) >> >> #Check design and contrast matrices >> >>> design >>> >> GroupA GroupB GroupC >> 1 1 0 0 >> 2 1 0 0 >> 3 1 0 0 >> 4 0 1 0 >> 5 0 1 0 >> 6 0 1 0 >> 7 0 0 1 >> 8 0 0 1 >> 9 0 0 1 >> attr(,"assign") >> [1] 1 1 1 >> attr(,"contrasts") >> attr(,"contrasts")$`factor(c(**1, 1, 1, 2, 2, 2, 3, 3, 3))` >> [1] "contr.treatment" >> >> contrast.matrix >>> >> Contrasts >> Levels GroupB - GroupA GroupC - GroupA GroupC - GroupB >> GroupA -1 -1 0 >> GroupB 1 0 -1 >> GroupC 0 1 1 >> >> #Fitting the eset to to the design and contrast >> >>> fit<- lmFit(exprs, design) >>> fit2<- contrasts.fit(fit, contrast.matrix) >>> >> #Computing the statistics >> >>> fit2<- eBayes(fit2) >>> >> >> Then I check the results with topTable and get the following columns in >> the >> output >> >>> topTable(fit2) >>> >> GroupB...GroupA GroupC...GroupA GroupC...GroupB AveExpr F >> P.Value adj.P.Val >> 25031 2.3602203 2.4273830 0.06716267 5.021412 29.06509 >> 7.844834e-05 0.9587773 >> 12902 -0.4572897 -0.5680943 -0.11080467 7.516681 25.41608 >> 1.365021e-04 0.9587773 >> 7158 -0.4478660 -0.4296077 0.01825833 7.057833 23.48871 >> 1.880100e-04 0.9587773 >> 18358 -0.1002647 0.3304903 0.43075500 7.352807 22.78417 >> 2.125096e-04 0.9587773 >> 28768 -0.7695883 -1.3837750 -0.61418667 3.983044 22.47514 >> 2.244612e-04 0.9587773 >> 28820 -0.1708800 -0.9939680 -0.82308800 5.470826 18.25071 >> 5.081473e-04 0.9587773 >> 15238 -0.4850297 -0.4658157 0.01921400 7.071662 17.15191 >> 6.440979e-04 0.9587773 >> 24681 -0.3759717 -0.3486450 0.02732667 9.281578 16.47813 >> 7.493077e-04 0.9587773 >> 19246 -0.8675393 -0.5082140 0.35932533 8.123538 16.27776 >> 7.845150e-04 0.9587773 >> 28808 0.2601277 0.6909140 0.43078633 4.814602 16.21283 >> 7.963487e-04 0.9587773 >> >> I want to export my results and write >> >> results<- decideTests(fit2) >>> write.fit(fit2, results, "limma_results.txt", adjust="BH") >>> >> Now don't get the same columns as when using topTable which is quite >> confusing. Why don't I get the FC for the comparisons between the >> different >> groups as if I run topTable with the coef parameter ( "topTable(fit2, >> coef=1)" )? The columns I get are the following >> > > The simple answer is that they are two different functions with different > goals. But note that you do get the same information. > > > >> A >> >> Coef.GroupB - GroupA >> Coef.GroupC - GroupA >> Coef.GroupC - GroupB >> >> t.GroupB - GroupA >> t.GroupC - GroupA >> t.GroupC - GroupB >> >> p.value.GroupB - GroupA >> p.value.GroupC - GroupA >> p.value.GroupC - GroupB >> >> p.value.adj.GroupB - GroupA >> p.value.adj.GroupC - GroupA >> p.value.adj.GroupC - GroupB >> >> F >> F.p.value >> >> Res.GroupB - GroupA >> Res.GroupC - GroupA >> Res.GroupC - GroupB >> >> >> Could some body please try to explain what do the columns A, Coef, F, >> F.p.value and Res mean? >> > > A - are your log fold change values > Coef - are your coefficients (you set up a cell means model, so these are > the sample means) > F - is an F-statistic, which tests the null hypothesis that none of the > sample means are different > F.p.value - is the p-value for the F-statistic > Res - is the results matrix you passed into write.fit(), showing which > contrast(s) were significant > > Best, > > Jim > > > >> >> >> #Session info >> >>> sessionInfo() >>> >> R version 2.15.0 (2012-03-30) >> Platform: i386-pc-mingw32/i386 (32-bit) >> >> locale: >> [1] LC_COLLATE=Swedish_Sweden.1252 LC_CTYPE=Swedish_Sweden.1252 >> LC_MONETARY=Swedish_Sweden.**1252 LC_NUMERIC=C >> LC_TIME=Swedish_Sweden.1252 >> >> attached base packages: >> [1] stats graphics grDevices utils datasets methods base >> >> other attached packages: >> [1] limma_3.12.1 Biobase_2.16.0 BiocGenerics_0.2.0 >> >> loaded via a namespace (and not attached): >> [1] affylmGUI_1.30.0 IRanges_1.14.4 oneChannelGUI_1.22.10 >> stats4_2.15.0 tcltk_2.15.0 >> Best regards >> JMA >> >> [[alternative HTML version deleted]] >> >> ______________________________**_________________ >> Bioconductor mailing list >> Bioconductor@r-project.org >> https://stat.ethz.ch/mailman/**listinfo/bioconductor >> Search the archives: http://news.gmane.org/gmane.** >> science.biology.informatics.**conductor >> > > -- > James W. MacDonald, M.S. > Biostatistician > University of Washington > Environmental and Occupational Health Sciences > 4225 Roosevelt Way NE, # 100 > Seattle WA 98105-6099 > > [[alternative HTML version deleted]]