>Date: Mon, 15 Aug 2005 15:30:34 -0700 (PDT) >From: Wenbin Liu <wnbnl at="" yahoo.com=""> >Subject: [BioC] limma and block effect >To: bioconductor at stat.math.ethz.ch > >Dear limma users, > >I have an experiment designed as follows: > >Samples were taken from 3 individuals(S1, S2 and S3), >and for the sample from each individual, there are >four treatments (A,B, C, and D). Each sample was >hybridized to an Affymetrix expression array. >Therefore, the individuals serve as blocks. > >Will the following code work? > >treat <- as.factor(rep(c("A", "B", "C", "D"), 3)) >block <- rep(1:3, rep(4,3)) >design <- model.matrix(~ -1 + treat) You cannot omit estimating the within-block correction. You must include the duplicateCorrelation() step here, as in the example in the User's Guide. >fit1 <- lmFit(dat, design=design, block=block) ># where dat is an ExprSet. > >In addition, what if I have a missing chip (11 chips >instead of the balanced 12 ones)? Makes no difference. > Will argument >method='gls' do the job? Not necessarly. Please follow the example in the User's Guide. Gordon >Any comment or hint will be greatly appreciated! > >Wenbin