Hi Everyone, Im working on creating a comparison of ( Group1_T - Group2_T ) - (Group1_NT -Group2_NT ) to identify the DMRs that are found between my treatment comparison (Group1_T and Group2_T), and are not found in my control comparison (Group1_NT and Group2_NT). However, Im wondering if its possible to include factorial and continuous covariates in this, as my groups are not perfectly sex or age matched, and I want to include blood cell composition PCs.

Group <- factor(targets$Group)
Treat <-  factor(targets$Treatment)
Sex <- factor(targets$Sex) # covariates
Age <- as.integer(targets$Age)
PC1 <- as.numeric(targets$PC1)
PC2 <- as.numeric(targets$PC2)
PC3 <- as.numeric(targets$PC3)

# My incorrect attempt at a standard interaction effect with a 2x2x2 design, plus the five covariates:
design <- model.matrix(~(Group*Treat)+Sex+Age+PC1+PC2+PC3, data=targets) 
design

which gives me this design:

 (Intercept) PopGroup2 TreatT Sex Age          PC1         PC2          PC3      PopGroup2:TreatT
1         1     1   2  18 -0.806896196  0.10589096 -0.154088271       1
1         0     0   2  24  2.206815782  0.19763909  1.546195905       0
1         0     0   2  19 -2.111827930  0.28779334  0.312779390       0

I also tried to create this matrix an alternative way, where I created a Group_Treatment variable with the four levels: Group1_T, Group2_T, Group1_NT, Group2_NT. However, Im not sure if my covariates are still being factored into my "Interaction" comparison.

Group_Treatment <- factor(targets$Group_Treatment)
design<- model.matrix(~0+Group_Treatment+Sex+Age+PC1+PC2+PC3, data=targets)

contr <- makeContrasts(Interaction=(Group1_T -Group2_T)-(Group1_NT - Group2_NT), 
    levels = design)

myAnnotation <- cpg.annotate("array", object=mVals, what = "M",
    arraytype = "EPICv2",  analysis.type="differential", 
    contrasts = TRUE, cont.matrix = contr, design = design,
     coef="Interaction",  epicv2Filter = "mean")

Is my second attempt actually the correct comparison that Im looking for, with the 5 coefficients factored in? Or am I approaching this incorrectly?

Any help would be appreciated!

The second way is how you should do it.