Dear list,

I'm using Agilent 44K microarray. We have 20 cases study. For each one we hybridized sample before and after treatment on the same array, in dye-swap.

I have the following design matrix (build manually, to follow the group-mean parametrization approach) : 

===
   Intercept liver other R N RepClass.Unk PD PR SD
1          1     1     0 1 0            0  0  0  1
2         -1     1     0 1 0            0  0  0  1
3          1     1     0 0 1            0  1  0  0
4         -1     1     0 0 1            0  1  0  0
5          1     1     0 1 0            0  0  0  1
6         -1     1     0 1 0            0  0  0  1
7          1     1     0 0 1            0  0  0  1
8         -1     1     0 0 1            0  0  0  1
9          1     1     0 1 0            0  0  0  1
10        -1     1     0 1 0            0  0  0  1
11         1     0     1 0 1            0  1  0  0
12        -1     0     1 0 1            0  1  0  0
13         1     0     1 0 0            1  0  0  1
14        -1     0     1 0 0            1  0  0  1
15         1     0     1 1 0            0  0  0  1
16        -1     0     1 1 0            0  0  0  1
17         1     0     1 0 1            0  1  0  0
18        -1     0     1 0 1            0  1  0  0
19         1     1     0 1 0            0  0  0  1
20        -1     1     0 1 0            0  0  0  1
21         1     0     1 1 0            0  0  0  1
22        -1     0     1 1 0            0  0  0  1
23         1     0     1 1 0            0  0  1  0
24        -1     0     1 1 0            0  0  1  0
25         1     0     1 1 0            0  0  0  1
26        -1     0     1 1 0            0  0  0  1
27         1     0     1 0 1            0  1  0  0
28        -1     0     1 0 1            0  1  0  0
29         1     1     0 1 0            0  1  0  0
30        -1     1     0 1 0            0  1  0  0
31         1     0     1 0 0            1  0  0  1
32        -1     0     1 0 0            1  0  0  1
33         1     0     1 0 1            0  1  0  0
34        -1     0     1 0 1            0  1  0  0
35         1     1     0 1 0            0  0  0  1
36        -1     1     0 1 0            0  0  0  1
37         1     1     0 0 0            1  0  0  1
38        -1     1     0 0 0            1  0  0  1
39         1     0     1 0 1            0  1  0  0
40        -1     0     1 0 1            0  1  0  0
===

Intercept : term to catch dye bias
Liver / other : main source of variability among my samples
R/N/Rep.Class.Unk : status of my samples (responder/nor responding/unknown status)
PD/PR/SD : Experimentally observed variable.

First, I want to get the gene differentially expressed between R and N : 
==
fit <- lmFit(X, design)
cont.matrix <- makeContrasts("RvsN" = R-N,levels=design)
fit2 <- contrasts.fit(fit, cont.matrix)
fit2 <- eBayes(fit2)
==
It works fine.


Then I want to get genes differentially expressed between PD and SD : 
===
fit <- lmFit(X, design)
cont.matrix <- makeContrasts("PDvsSD" = PD-SD,levels=design)
fit2 <- contrasts.fit(fit, cont.matrix)
===

Here I get an error : 

Error in contrasts.fit(fit, cont.matrix) : 
  trying to take contrast of non-estimable coefficient


I can't see why I raised this error in this particular case. 
Can you please provided any help on this ?




Many thanks.
--
Guillaume


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