Hello,

I want to analyze a recent Affymetrix Exon array dataset with the limma 
package; I imported values from APT and created and ExpressionSet with 
it, but the experiment's design is not the simplest and the user guide 
is not really helping - no real explanations of the 'how' to get there 
with the various presented use cases.

It's 27 arrays, 3 time points, 3 sample sources, 3 treatments (see data 
frame below) - I'm interested in comparing 'mock' vs 'pos' and 'mock' vs 
'neg' at all time points (24vs24, 48vs48, etc). The samples are paired; 
it's from the same blood donor, so i'd like to capture this effect in 
the data to improve p-values. I did a limma analysis using onechannelGUI 
(with great results) but it doesn't allow for complex designs (to 
capture the paired samples effect and improve p-values, increase power), 
so I have to do this manually.

So far I tried design <- model.matrix(~Treatment*Time*Sample, 
data=targets) as I seen on the mailing list, where Treatment <- 
factor(targets$Treatment), etc.

Data frame:

Sample ID 	Treatment 	Time 	Sample
1 	Mock 	24h 	A
2 	Neg 	24h 	A
3 	Pos 	24h 	A
4 	Mock 	24h 	B
5 	Neg 	24h 	B
6 	Pos 	24h 	B
7 	Mock 	24h 	C
8 	Neg 	24h 	C
9 	Pos 	24h 	C
10 	Mock 	48h 	A
11 	Neg 	48h 	A
12 	Pos 	48h 	A
13 	Mock 	48h 	B
14 	Neg 	48h 	B
15 	Pos 	48h 	B
16 	Mock 	48h 	C
17 	Neg 	48h 	C
18 	Pos 	48h 	C
19 	Mock 	72h 	A
20 	Neg 	72h 	A
21 	Pos 	72h 	A
22 	Mock 	72h 	B
23 	Neg 	72h 	B
24 	Pos 	72h 	B
25 	Mock 	72h 	C
26 	Neg 	72h 	C
27 	Pos 	72h 	C


Any help would be greatly appreciated,
Michael

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