Hi, Im new to limma, and I'm trying to determine a design matrix for the following type of experiment. I dont see an example of this sort of experiment which is becoming increasingly common. I have Diseased vs control (two color). I have 30 diseased individuals, and each individual has had 4-6 technical replicates carried out with dye swaps involved. My question is, how to capitalize on the robustness of the technical reps per individual? Is there a way in limma of obtaining the least variable genes per technical rep set (which I guess violates independence somewhat as the 4-6 replicates are done on the same individual), and then comparing these results to all the other 29 diseased individuals (who will have had the same filtering done to identify the most robust differentially expressed genes compared to the control). Ulimatley this will result in the identification of the most robustly differentially expressed genes across all 30 individuals, but will have capitalized on the fact that each individual was technically replicated between 4-6 times. Maybe this is straightforward, but I cant figure out how to do it, please help! thanks Simon.

At 06:16 PM 14/12/2003, Simon Melov wrote: >Hi, >Im new to limma, and I'm trying to determine a design matrix for the >following type of experiment. I dont see an example of this sort of >experiment which is becoming increasingly common. I have Diseased vs >control (two color). I have 30 diseased individuals, and each individual >has had 4-6 technical replicates carried out with dye swaps involved. My >question is, how to capitalize on the robustness of the technical reps per >individual? Is there a way in limma of obtaining the least variable genes >per technical rep set (which I guess violates independence somewhat as the >4-6 replicates are done on the same individual), and then comparing these >results to all the other 29 diseased individuals (who will have had the >same filtering done to identify the most robust differentially expressed >genes compared to the control). Ulimatley this will result in the >identification of the most robustly differentially expressed genes across >all 30 individuals, but will have capitalized on the fact that each >individual was technically replicated between 4-6 times. Is the same control used throughout the experiment? I will assume that it is. Here is one way to answer you question. Make up a targets file something like this: Cy3 Cy5 Patient1 Control Control Patient1 Patient1 Control Patient2 Control Control Patient2 ... Then in R: targets <- readTargets() design <- designMatrix(targets, ref="Control") fit <- lmFit(MA, design) # estimate the diseased vs control differences for each patient cont.matrix <- matrix(1,30,1) fit <- eBayes(contrasts.fit(fit, cont.matrix)) # average the results over patients topTable(fit) Gordon >Maybe this is straightforward, but I cant figure out how to do it, please >help! > >thanks > >Simon.