user2294316 - 1 month ago 11

R Question

I have my matrix designed in the following way which I name as mat1:

`Probes sample1 sample1 sample2 sample2 sample3 sample3 sample4 sample4`

rep1 rep2 rep1 rep2 rep1 rep2 rep1 rep2

------------------------------------------------------------------------

gene1 5.098 5.076 5.072 4.677 7.450 7.456 8.564 8.555

gene2 8.906 8.903 6.700 6.653 6.749 6.754 7.546 7.540

gene3 7.409 7.398 5.392 5.432 6.715 6.724 5.345 5.330

gene4 4.876 4.869 5.864 5.981 4.280 4.290 4.267 4.255

gene4 3.567 3.560 3.554 3.425 8.500 8.564 6.345 6.330

gene5 2.569 2.560 8.600 8.645 5.225 5.234 7.345 7.333

I use the limma package to find the DEG's

`Group <- factor(c("p1", "p1", "p2", "p2","p3", "p4","p4")`

design <- model.matrix(~0 + Group)

colnames(design) <- gsub("Group","", colnames(design))

fit <- lmFit(mat1[,1:4],design)

contrast.matrix<-makeContrasts(p1-p2,levels=design)

fit2<-contrasts.fit(fit,contrast.matrix)

fit2<-eBayes(fit2)

sel.diif<-p.adjust(fit2$F.p.value,method="fdr")<0.05

deg<-mat1[,1:4][sel.diif,]

So will "deg" just give me those genes which are significant in sample one versus two. I am interested in those genes which are differentially expressed only in first sample but not in the second sample and am not sure if this is the right approach.

Or should I try something like this:

`contrast.matrix<-makeContrasts(contrasts="p1"-("p2"+"p3"+"p4")/3,levels=design)`

I am not sure how I should set the contrasts matrix to obtain the DEG's from sample 1 only but not in the other three.

Answer

Your example isn't reproducible, i.e. I can't reproduce the results. However, here are a few comments:

- You are correct regarding
`deg`

. It will look for genes that are different between the two samples. A contrast matrix of:

`makeContrasts(contrasts="p1-(p2+p3+p4)/3", levels=design)`

is how I would (probably) tackle this problem. However, this may cancel out effects. For example if

`p2`

was high and`p3`

was low.Alternatively, you could have something like:

`makeContrasts(contrasts=c("p1-p2", "p1-p3", "p1-p4"), levels=design)`

and look at overlapping genes.