Hello everyone, I've been working on a RNASeq project requiring an accurate DEG analysis. I've always used a pipeline involving edgeR, and, more specifically, the function glmQLFTest (as explained in the workflow RNASeqEdgeRQL). However, I'd like to perform my anaylsis with DESEQ2 as well . Basically, edgeR seems to perform pairwise analysis between couples of samples, for example, given the following experimental design:

Sample Variable1 Variable2

1 A x

2 A x

3 A x

4 A y

5 A y

6 A y

7 B x

8 B x

9 B x

10 B y

11 B y

12 B y

group <- paste(targets$Variable1, targets$Variable2, sep=".")
group <- factor(group)
design <- model.matrix(~0+group)

Contrasts <- makeContrasts(A.x-B.x, levels=design)
res <- glmQLFTest(fit, contrast=Contrasts)

Contrasts2 <- makeContrasts(A.x-B.y, levels=design)
res2 <- glmQLFTest(fit, contrast2=Contrasts2)

Contrasts3 <- makeContrasts(A.y-B.x, levels=design)
res3 <- glmQLFTest(fit, contrast3=Contrasts3)

Contrasts4 <- makeContrasts(A.y-B.y, levels=design)
res4 <- glmQLFTest(fit, contrast4=Contrasts4)

I've read the vignette several times, but I'm not sure about how to perform this simple analysis with DESEQ2.

I think I may work on the 'Interactions' in DESEQ2, so my question is...

Is the following code performing 4 pairwise comparisons, like edgeR did?

I wrote this (from Interactions paragraph in DESEQ2 vignette)

dds$group <- factor(paste0(dds$Variable1, dds$Variable2))
design(dds) <- ~ group
dds <- DESeq(dds)
resultsNames(dds)
results(dds, contrast=c("group", "Ax", "Ay", "Bx", "By"))

Also, it's not clear, since I have to define the dds object before this, how the design formula has to be... maybe like:

dds <- DESeqDataSetFromMatrix(countData = cts, colData = coldata, design = ~ Variable1 + Varible2)

or possibly

dds <- DESeqDataSetFromMatrix(countData = cts, colData = coldata, design = ~ Variable1 + Varible2 + Variable1:Variable2 )

From the help of this specific function, while defining the design formula, I read:

*a formula which expresses how the counts for each gene depend on the variables in colData. Many R formula are valid, including designs with multiple variables, e.g., ~ group + condition, and designs with interactions, e.g., ~ genotype + treatment + genotype:treatment

What is exactly the difference between ~ Variable1 + Variable2 and ~ Variable1 + Variable2 + Variable1:Variable2?

Thanks in advance for your help!

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Pairwise analysis is an FAQ at the end of our vignette.