The edgeR vignette talks about additive models when dealing with paired samples (and then also when adjusting for batch effects.) Can we use additive models when adjusting for other variables? Take this example

Samples    Treatment    Sex
Sample_1    Control     M
Sample_2    Control     M
Sample_3    Control     M
Sample_4    Treatment   M
Sample_5    Treatment   M
Sample_6    Treatment   M
Sample_7    Control     F
Sample_8    Control     F
Sample_9    Control     F
Sample_10   Treatment   F
Sample_11   Treatment   F
Sample_12   Treatment   F

If we are only interested in looking at the effect of treatment, would a model matrix like this be appropriate?

model.matrix(~Sex + Treatment) 

Yes, it looks fine.