Hello,

I am having troubles to understand interactions in edgeR, probably this is simple but I can get it. I will be referring to 3.3.4 topic of edgeR user`s guide. The experimental design of this section is:

targets

Treat Time Sample1 Placebo 0h Sample2 Placebo 0h Sample3 Placebo 1h Sample4 Placebo 1h Sample5 Placebo 2h Sample6 Placebo 2h Sample7 Drug 0h Sample8 Drug 0h Sample9 Drug 1h Sample10 Drug 1h Sample11 Drug 2h Sample12 Drug 2h

matrix is generated with

design <- model.matrix(~Treat * Time, data=targets) https://ibb.co/P5g70ZB

(Intercept) TreatDrug Time1h Time2h TreatDrug:Time1h TreatDrug:Time2h Sample1 1 0 0 0 0 0 Sample2 1 0 0 0 0 0 Sample3 1 0 1 0 0 0 Sample4 1 0 1 0 0 0 Sample5 1 0 0 1 0 0 Sample6 1 0 0 1 0 0 Sample7 1 1 0 0 0 0 Sample8 1 1 0 0 0 0 Sample9 1 1 1 0 1 0 Sample10 1 1 1 0 1 0 Sample11 1 1 0 1 0 1 Sample12 1 1 0 1 0 1

colnames(design)

[1] "(Intercept)" "TreatDrug" "Time1h" "Time2h" [5] "TreatDrug:Time1h". "TreatDrug:Time2h"

My questions are:

What represent the intercept? is an average of expression of all samples including drug, placebo at all times?.

In the following example:

glmQLFTest(fit, coef=2)

According to matrix coef2= TreatDrug = Drug at time 0,1 and 2. Using coef=2 , Do I comparing TreatDrug vs Intercept ?

In biological sense what genes will be detecting doing this comparison?

I really appreciate your help.

In that sense

coef= 3are the effects of placebo at 0 hours vs. placebo at 1 hour?coef= 4, are the effects of placebo at 0 hours vs. placebo at 2 hours? and "TreatDrug:Time1h" =coef=5will be placebo 1 hour vs. Drug 1 hour.I have been reading the User's Guide, it is well documented but I am slow learner,

Thanks

Section 3.3 of the User's Guide presents three design matrices to analyse this experimental design. We recommend that you define each treatment combination as a group (Section 3.3.1) because it is by far the most transparent and easier to interpret, especially if you are new to statistical formula.

We cover the interaction formula in Section 3.3.4 only because this is the traditional statistical approach. However the interaction formula is really only useful for testing interactions, represented by coefficients 5:6. The other coefficients are not any scientific use and it is probably not worth your while trying to understand what they mean. In practtice, you should not be conducting tests for any of the coefficients 1:4 from the interation formula. You can get any results you need in a clearer way from the group means formula in Section 3.3.1.