Hi,

I have used bump hunter function for two populations (control and disease represented by status column) using the following formula

`designMatrix <- model.matrix(~ status)`

For each of control and disease populations,within each population, I have subpouations treated with different drugs

I would like to identify DMRs between control and disease considering these conditions (drugs)

I made the design matrix as follows:

designMatrix <- model.matrix(~ status+condition)

I found that the out put is very similar. I would like to know is it possible to identify DMRs between disease and normal considering different treatments using bump hunter?

Thank you.

Thank you James .

Using the coefficient which is value column in the table of bump hunter output, the sign can tell if the region is hypo/hyper methylated in the disease relative to normal. How to infer which region of disease is hypomethylated in one condition relative to other condition?

would you please explains a bit how the coefficient (value) is calculated?

Thank you again for help

It's a bit complicated for an interaction term. As you note already, for the design matrix you originally specified, the sign of the coefficient indicates up or down methylation. But the interaction is computed as:

(Drug1_disease - Drug1_control) - (Drug2_disease - Drug2_control)

And the sign simply indicates that the term in the first set of parentheses is larger than the term in the second set of parentheses. The methylation may be lower in disease vs control in both instances (just less so when treating with Drug1), and you would still get a positive sign. So the only way to interpret a coefficient for an interaction is to either look at the underlying data, or to do plots.

I personally like to use Gviz for that sort of thing, as you can easily plot any region with differential methylation (genomic region I mean), along with the methylation status of each group.