[BioC] Question about best analysis method for a complex array expriment design

[Agnes Paquet]

Dear List,

I need to analyze an experiment with a design more complex than usual 
for our facility, and I am not sure about the best way to analyze this 
dataset. I would really appreciate your advice on whether the method I 
am planning to use is correct, or if there is a better way to analyze 
this data.

The experiment design is the following:
We have 10 patients, and we are using one-color Agilent arrays. Each 
patient performed a physical test twice: once without anything added, 
and once taking a drug during the test. Samples are collected before and 
after the physical test, for a total of 4 samples by patients. The drug 
was administered randomly during the first or second test.

Here is the top of my target file:

Patient.ID	TimePoint	Drug	TestOrder	Drug.Included.In.Test
Pt1	Before	no	test1	control
Pt1	After	no	test1	control
Pt1	Before	no	test2	test
Pt1	After	yes	test2	test
Pt2	Before	no	test2	control
Pt2	After	no	test2	control
Pt2	Before	no	test1	test
Pt2	After	yes	test1	test

We are interested in finding:
- DE genes related to physical test only
- DE genes related to the addition of the drug only
- Genes differentially regulated by the drug during the physical test

I usually use limma for differential analysis, so following the limma 
user’s guide, I was planning to use a design with blocks of size 4 for 
patients, and a variable with 4 levels combining Drug.Included.In.Test 
and TimePoint.

Is this approach correct?
I read in the user’s guide patient information could also be modeled as 
random effect using the duplicateCorrelation function. Would this method 
be more appropriate?

Is there a better way to model the data, that would estimate the 
physical test effect and the drug effect directly?

Thank you very much for your help,

Agnes