Do I now understand Limma contrast matrices??
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@matthew-hannah-621
Last seen 9.6 years ago
Hi, Now I've finally had some time to look practically at contrast matrices I've made some progress but still have a couple of questions. I was initially wary of the contrast fit as I thought there was some special type of notation, but it seems it's basic arithmetic, is this right, or are there any exceptions. This is what I've done #3 genotypes(Con, Mut, Mut2), 2 treatments(A, N), 4 reps treatments <- factor(c(1,1,1,1,2,2,2,2,3,3,3,3,4,4,4,4,5,5,5,5,6,6,6,6)) design <- model.matrix(~ 0+treatments) colnames(design) <- c("ConA","ConN","MutA","MutN","Mut2A","Mut2N") fit <- lmFit(esetgcrma, design) cont.matrix <- makeContrasts( treatment.1=(ConA+MutA+Mut2A-ConN-MutN-Mut2N)/3, treatment.2=((ConA+MutA+Mut2A)-(ConN+MutN+Mut2N))/3, treatment.3=((ConA-ConN)+(MutA-MutN)+(Mut2A-Mut2N))/3, ,levels=design) con.fit <- contrasts.fit(fit, cont.matrix) I thought treatment.1 and 2 would NOT take the genotype into effect, whilst 3 would. But all 3 produce exactly the same results, coefs, sd, df, p.values....etc. This may seem obvious to some but I was wondering if the contrasts could be written in different ways to take the genotype effect into account when looking at treatment effects, but initially/simply it seems not, is this right? I've compared the results to unpaired t-test (limma increases t-value changes), and to paired t (more similar, distributed around slope of 1). >From these I guess the genotype effect is taken into account, but I cannot be sure. Basically what I'm asking is would the above fit find the following consistent treatment effect, despite the genotype effect (that would mask it in a simple unpaired t)? Con / Mut / Mut2 Untreated - 1,2,3 / 4,4,6 / 1,1,2 Treated - 3,4,4 / 5,6,7 / 2,3,3 Finally it has been mentioned there are lots of ways of producing the design and contrast matrices. In the above situation is there an alternative matrix that I should be comparing? Thanks, Matt
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