I am new to DESeq2. The vignette (LINK) focuses on a simple set : "treated vs untreated ".
My experimental design contains 2 genotypes (A and B) vs 2 conditions (Control / Treated).
My coldata :
In order to answer my biological questions, I would expect to do something like this:
- Genotype effect = (A Control + A Treated) - (B Control + B Treated)
- Treatment effect = (A Treated + B Treated) - (A Control + B Control)
I remember that in edgeR we could define such a list via:
my.contrasts <- makeContrasts().
In DESeq2, according to the vignette and these topics (DESEq2 comparison with mulitple cell types under 2 conditions and DESeq2 likelihood ratio test (LRT) design - 2 genotypes, 4 time points) I did:
dds <- DESeqDataSetFromMatrix(countData = cts, colData = coldata, design = ~ Genotype + Treatment + Genotype:Treatment)
dds$group <- factor(paste0(dds$Genotype, dds$Treatment))
design(dds) <- ~ group
dds <- DESeq(dds)
The output of "resultsNames(dds)" was :
"Intercept", "group_ATreated_vs_AControl", "group_BControl_vs_AControl" and "group_BTreated_vs_AControl".
I expected to get something like "A_vs_B" and "Treated_vs_Control." But I saw this post (A: DESeq2 resultsNames output) that says: it is easy to test if the interaction effect is significant, by testing a single term: for example "groupY.conditionB".
However, which one of the “resultsNames(dds)” shortcuts should I use in the next step "Shrinkage of effect size" ? I am confused in regards to my biological questions.
Then, do I have to ask each question separately in DESq2 and compare them together later (outside DESeq2) ? I mean “A Treated vs A Control”, “B Treated vs B Control”, etc…
Tanks in advance.