Hello bioconductor group, I am stuck with understanding how to create contrast matrix for extracting the comparisons I need. Some seem to be easy and some I could never understand. I am analyzing a 2factorial experiment and found the below post useful. I have the exact same questions and would greatly appreciate if any one can give detailed answers. My question from the post below pertains to AvsN=(MUTA+ConA-MUTN-ConN)/2, levels=design) is AvsN also equal to (MutA-ConA)-(MutN-ConN)? Also why did we divide the (MUTA+ConA-MUTN-ConN) by 2? I got this question when I looked at the following code in Limma user guide page 46 with the following code for exact same design > cont.matrix <- makeContrasts( + WT.SvsU=WT.S-WT.U, + Mu.SvsU=Mu.S-Mu.U,*+ Diff=(Mu.S-Mu.U)-(WT.S-WT.U)*, + levels=design) Hoping to get a response from the bioconductors, Thanks Jeremy At 04:53 AM 23/06/2004, Matthew Hannah wrote: >*I know this has been asked several times for various designs, and I*>*have searched and read the user guide but I'm getting nowhere fast.*>*I would be very grateful if someone could help me out with what is*>*probably a simple request to someone familar with lm and Limma.*>**>*I was following*>*8.4 Estrogen Data: A 2x2 Factorial Experiment with Affymetrix Arrays*>*but have got a bit confused - especially if*>* > cont.matrix <- cbind(E10=c(0,0,1,0),E48=c(0,0,0,1))*>*is not a typo and should read*>* > cont.matrix <- cbind(E10=c(0,1,0,0),E48=c(0,0,0,1))* No it is not a typo. >*Anyway rather than say more than I'm statistically inept, I would*>*appreciate some help on an appropriate design and contrast matrix*>*for the list below.*>**>* Exp Genotype Treatment*>*MUTA.1 1 MUT A*>*MUTA.2 2 MUT A*>*MUTA.3 3 MUT A*>*MUTA.4 4 MUT A*>*MUTN.1 1 MUT N*>*MUTN.2 2 MUT N*>*MUTN.3 3 MUT N*>*MUTN.4 4 MUT N*>*ConA.1 1 Con A*>*ConA.2 2 Con A*>*ConA.3 3 Con A*>*ConA.4 4 Con A*>*ConN.1 1 Con N*>*ConN.2 2 Con N*>*ConN.3 3 Con N*>*ConN.4 4 Con N*>**>*I already have it as pData (is there an easy way*>*to adapt this?). I tried this design (is it correct?) but also want*>*it with the experiment included.*>**>* >treatments <- factor(c(1,1,1,1,2,2,2,2 ,3,3,3,3,4,4,4,4),*>*labels=c("MUTA","MUTN","ConA","ConN"))*>* > contrasts(treatments) <- cbind(Treat=c(1,0,1,0),MUT=c(1,1,0,0),*>*Con=c(0,0,1,1))*>* >design <- model.matrix(~treatments)*>**>*Then I got very confused with the contrasts - in the example they only*>*look at the estrogen effect, what if you want to make the same contrasts*>*as in the design (eg: also include time in the estrogen example) do you*>*need another fit or do you just use the first one?* No you only need one fit. >*Basically I want to compare MUTA vs ConA, MUTN vs ConN, A vs N.* Perhaps the easiest for you is to use the makeContrasts() function: treatments <- factor(c(1,1,1,1,2,2,2,2,3,3,3,3,4,4,4,4)) design <- model.matrix(~ 0+treatments) colnames(design) <- c("MUTA","MUTN","ConA","ConN") fit <- lmFit(eset, design) cont.matrix <- makeContrasts(MUTA-ConA, MUTN-ConN, AvsN=(MUTA+ConA-MUTN-ConN)/2, levels=design) fit2 <- contrasts.fit(fit, cont.matrix) fit2 <- eBayes(fit2) >*Getting slightly more complicated the data is paired (eg: MUTA.1 with*>*MUTN.1) and was wondering if this pairwise nature could be taken into*>*account and compare the MUTA-MUTN changes vs ConA-ConN changes? I ask*>*this as I've found that the changes may be more reproducible than the*>*absolute values.* Now you are asking something which is a methodological research question, and you really should consider taking on a statistician as a full collaborator. Gordon >*Thanks in advance.*>*Matt* [[alternative HTML version deleted]]