Dear all, We have 63 arrays that are either diseased or normal. We wish to select genes adjusting for two covariates : gender (male/female) and experimental batch (one/two/three). >From biological knowledge, we expect the batch effect to be significant but we wish to quantify it numerically. Here is a way that we tried and the problems we faced. We searched the archives without much success. library(limma) # version 2.3.3 dd <- model.matrix( ~ disease + gender + batch, cl ) head( dd ) (Intercept) diseasenormal gendermale batchtwo batchthree 2405 1 1 0 0 0 2408 1 1 0 0 0 2410 1 1 0 0 0 GER15 1 0 0 0 0 GER20 1 0 0 0 1 GER22 1 0 0 0 0 fit <- lmFit(mds.rma, dd) 1) Is the following the correct way of setting up contrasts to find the three pairwise comparison between batches ? contr.m <- cbind( Batch2minus1=c(0,0,0,1,0), Batch2minus1=c(0,0,0,0,1), Batch3minus2=c(0,0,0,-1,1) ) 2) From reading the LIMMA user guide, we think decideTests() could be potentially useful. fit2 <- eBayes( contrasts.fit( fit, contr.m ) ) a <- decideTests( fit2, method="global" ) summary(a) Batch2minus1 Batch2minus1 Batch3minus2 -1 15151 13838 4919 0 26574 30561 46703 1 12950 10276 3053 Can we say that somewhere between 8000 - 27000 genes are affected by at least one batch. Or is there a nicer/proper way of explaining this to a biologist. 3) Ideally we would like to use method="nestedF" as suggested but we get the following error message. Can anyone explain what might possibly be going wrong. b <- decideTests( fit2, method="nestedF" ) Error in if (crossprod(crossprod(Q, x)) > qF[i]) { : missing value where TRUE/FALSE needed Any hints will be much appreciated. Regards, -- Adaikalavan Ramasamy ramasamy at cancer.org.uk Centre for Statistics in Medicine http://www.csm-oxford.org/ Wolfson College Annexe http://www.stats.ox.ac.uk/~ramasamy/ Linton Road Tel : 01865 284 408 Oxford OX2 6UD Fax : 01865 284 424