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)) 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? Basically I want to compare MUTA vs ConA, MUTN vs ConN, A vs N. 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. Thanks in advance. Matt

Why this error is coming: anyone had this..!!??? > R CMD BATCH Ex_R_Script1.R /usr/local/lib/R/bin/BATCH: line 55: 4686 Broken pipe cat ${in} Thanks bioC, Saurin

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