Hi all, Refering to section 8.4 of limmas User guide (2 groups) I found a little inconsistency (which might be a feature though), when using only 2 versus 2 Affymetrix arrays and the two methods proposed in the userguide. (differences versus contrasts) Even though, the M values are perfectly the same, there a very different p-values and different t-values. There are still slight differences for 2 versus 3 arrays, and these disappear with more slides. Is this a known behaviour? Otherwise, maybe a warning should be issued. And yes - taking 2 arrays per condition/genotype is standing on very shaky ground, but that is not up for me to decide. Method1 > toptable(fita,coef="MUvsWT",adjust="BH") M t P.Value B 4901 3.007937 17.67274 0.04321081 2.022252 4634 3.810009 16.17843 0.04321081 1.897954 5365 -3.003438 -16.08612 0.04321081 1.889342 4282 3.263951 13.71665 0.05394461 1.620254 4900 2.820148 13.63034 0.05394461 1.608386 6224 2.557232 13.33234 0.05394461 1.566076 4609 -2.197100 -12.31159 0.06801382 1.403791 1388 -3.369013 -11.80720 0.07283498 1.312306 5217 2.703997 11.35791 0.07804811 1.223567 4902 2.184269 10.95941 0.08339903 1.138550 Method2 (using contrasts) toptable(fit2,adjust="BH") M t P.Value B 4901 3.0079369 49.70266 0.6066029 -1.427223 3141 -0.7710439 -35.26161 0.6066029 -1.448119 1359 -0.9824312 -28.13189 0.6066029 -1.471786 791 0.4815546 25.80750 0.6066029 -1.483894 5365 -3.0034379 -23.94627 0.6066029 -1.496136 4609 -2.1971003 -22.47249 0.6066029 -1.507965 885 0.3017316 18.37909 0.6066029 -1.556058 6224 2.5572321 18.22744 0.6066029 -1.558443 4899 0.6554947 17.48778 0.6066029 -1.570918 6037 0.6788083 17.38863 0.6066029 -1.572704 Here the code (As data I used the data from section 11.3 where I simply took the first two files for each genotype) (http://visitor.ics.uci.edu/genex/cybert/tutorial/index.html) ########################################################### library(affy) library(limma) #readin arrays fns <- list.celfiles(path="C:/foo/bar/",full.names=TRUE) abatch <- ReadAffy(filenames=fns) #normalize using rma eset<-rma(abatch) #method1 design<-cbind(WT=c(1,1,1,1),MUvsWT=c(1,1,0,0)) fita <- lmFit(eset, design) fita<-eBayes(fita) toptable(fita,coef="MUvsWT",adjust="BH") #method2 design<-cbind(WT=c(0,0,1,1),MU=c(1,1,0,0)) fit<-lmFit(eset,design) cont.matrix <- makeContrasts( MUvsWT=MU-WT,levels=design) fit2 <- contrasts.fit(fit, cont.matrix) fit2 <- eBayes(fit2) toptable(fit2,adjust="BH") ########################################################### Kind regards, and thanks for your help, Bj?rn Usadel MPI of Molecular Plant Physiology PS There is also a little typo on page 39 of the User Guide instead of design <- cbind(WT=c(1,1,0,0,0,MU=c(0,0,1,1,1)) it should be design <- cbind(WT=c(1,1,0,0,0),MU=c(0,0,1,1,1))