I find it pretty astonishing (but also symptomatic of the entire R universe) that the official DESeq2 tutorials neither bother to explain the nomenclature by themselves, or even provide pointers as to where one can read up on it." Analyzing RNA-seq data with DESeq2" mentions ANOVA exactly once, and quite a distance away from where they introduce designs. And when they talk about designs, they just sort of head into it without any asides. I understand that formulae like these are pretty standard for R, but with packages like these, you likely get a lot of non-programmer crowd, Python crowd etc, who have never seen this and would not know what they need to google to find out...

Hey, To the best of my knockledge A)

`design = ~Strain + Time`

means that deseq2 will test the effect of the Time (the last factor), controlling for the effect of Strain (the first factor), so that the algorithm returns the fold change result only from the effect of time. B)`design = ~Time`

here the algorithm will return the fold change that result from time without correcting for fold change that result from strain C)`design = ~Strain`

same as aboveSo in my understanding Deseq2 treats the first factor as a co-variate and tries to eliminate the fold change that result because of this co-variate.

Thank you so much for your answer. So what does

`design = ~Strain + Time + Strain:Time`

mean? Also, do you know what are the outputs of`resultsNames(ddsTC)`

comparing?Regarding 'design = ~Strain + Time + Strain:Time` ,

Here you added an interaction term (how time is interacting with stain in relation to regulation of gene expression). So this design will return the effect of time on the reference level of strain (1 or 2 depends on your setting). Using contrast () you can look for the effect of time on the other level in "strain"

Alternatively you can group the strain (with its different levels) and time ((with its different levels) into one factor, lets call it ALL. and by using contrast () you can look for the difference in log2 fold change between any combination of levels.