Dear January, You design seems ammenable to a pretty standard analysis, which would go like: experiment <- factor(experiment) time <- factor(time) design <- model.matrix(~time+experiment) fit <- eBayes(lmFit(y,design)) summary(decideTests(fit)) T2vsT1: topTable(fit,coef=2) T3vsT1: topTable(fit,coef=3) etc Here experiment takes on values A, B, C, and time times on values T1, T2 etc. Best wishes Gordon > Date: Thu, 28 Oct 2010 12:56:08 +0200 > From: January Weiner <january.weiner at="" mpiib-berlin.mpg.de=""> > To: BioC <bioconductor at="" stat.math.ethz.ch=""> > Subject: [BioC] Limma time series analysis question > > Dear all, > > I am wondering what would be the optimal approach for limma to the > following setup: > > I have an experiment repeated three times (A, B, C). Each repeat is > measured at five different time points (T1...T5) in three replicates > (R1...R3): > > A T1 R1 > A T1 R2 > A T1 R3 > A T2 R1 > ... > > C T5 R1 > C T5 R2 > C T5 R3 > > Thus, A T1 is matched with A T2, T3, T4 and T5, but not with the B > series. So it is a mixed design. > > I want to see genes that are differentially expressed relative to T1. > How do I do it correctly in limma? > > Best regards, > j. > > -- > -------- Dr. January Weiner 3 -------------------------------------- > Max Planck Institute for Infection Biology > Charit?platz 1 > D-10117 Berlin, Germany > Web?? : www.mpiib-berlin.mpg.de > Tel? ?? : +49-30-28460514 ______________________________________________________________________ The information in this email is confidential and intend...{{dropped:4}}