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*

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