Hello all, I am planning the experimental design for a new microarray experiment. We are interested in studying the effect of a new drug on treated mice respect to untreated mice. In order to obtain an efficient experiment, we would like to use a balanced block design, that is to balance the samples respect to the dyes, as the following: Red Green array_1 treated_mice _1 untreated_mice_1 array_2 untreated_mice_2 treated_mice _2 array_3 treated_mice _3 untreated_mice_3 array_4 untreated_mice_4 treated_mice _4 array_5 treated_mice _5 untreated_mice_5 array_6 untreated_mice_6 treated_mice _6 array_7 treated_mice _7 untreated_mice_7 array_8 untreated_mice_8 treated_mice _8 array_9 treated_mice _9 untreated_mice_9 array_10 untreated_mice_10 treated_mice _10 Usually I use LIMMA package to perform statistical analysis but I looked LIMMA userguide up not finding anything... Does someone knows if LIMMA supports this experimental design? How do I have to consider (biological replicates, etc.) the arrays?...I think they are indipendent? Is it right? Do I have to calculate duplicate correlation among spot replicates on different arrays (I use arrays with only one spot per gene)? How can I correct for the dye bias if I do not have any pair of arrays with the same pair of samples dye-swapped? Any suggestions are appeciated. Best Regards Erika [[alternative HTML version deleted]]

Two small corrections: Treated <- rep(c(1,-1),5) design <- cbind(Dye=1,Treated=Treated) fit <- lmFit(MA, design) fit <- eBayes(fit) topTable(fit, coef="Treated") Gordon