I have an experiment design involving 2 varieties with 3 treatments each (6 sample types).  The 3 treatments were compared within each variety with a direct design for a total of 3 treatments x 2 dye swaps = 6  for a single variety and 12 for both varieties.  We also compared the 3 treatments across the varieties with a dye swap so 6 (3 x 2) more comparisons.  Each of the 6 sample types was therefore measured 6 times (3 with each dye).

I'm setting up the design matrix based on the direct design example in the limma guide so we can use more than just the direct comparisons to estimate the coefficients of interest, which are basically all of the direct comparisons.  I created one "basic" design matrix with just the direct comparisons to get an idea of what the coefficients should be.  Then I tried to do a design matrix to pull in the other indirect comparisons for the "within variety" comparisons but the results are sometimes far from the "basic" design results.

My arrays are:

N  R-Cy5  G-Cy3
1  v1.t1  v1.t2   
2  v1.t2  v1.t1
3  v1.t2  v1.t3   
4  v1.t3  v1.t2
5  v1.t3  v1.t1
6  v1.t1  v1.t3
7  v2.t1  v2.t2   
8  v2.t2  v2.t1
9  v2.t2  v2.t3   
10 v2.t3  v2.t2
11 v2.t3  v2.t1
12 v2.t1  v2.t3
13 v1.t1  v2.t1   
14 v2.t1  v1.t1
15 v1.t2  v2.t2   
16 v2.t2  v1.t2
17 v1.t3  v2.t3
18 v2.t3  v1.t3

Following the limma guide for the direct design comparisons (within varieties - using the t2 sample as the variety "reference") is in the first 4 columns.  The 3 comparisons between varieties are the last 3 columns.

      v1t1.v1t2 v1t3.v1t2 v2t1.v2t2 v2t3.v2t2 v1t1.v2t1 v1t2.v2t2 v1t3.v2t3
 [1,]         1         0         0         0         0         0         0
 [2,]        -1         0         0         0         0         0         0
 [3,]         0        -1         0         0         0         0         0
 [4,]         0         1         0         0         0         0         0
 [5,]        -1         1         0         0         0         0         0
 [6,]         1        -1         0         0         0         0         0
 [7,]         0         0         1         0         0         0         0
 [8,]         0         0        -1         0         0         0         0
 [9,]         0         0         0        -1         0         0         0
[10,]         0         0         0         1         0         0         0
[11,]         0         0        -1         1         0         0         0
[12,]         0         0         1        -1         0         0         0
[13,]         0         0         0         0         1         0         0
[14,]         0         0         0         0        -1         0         0
[15,]         0         0         0         0         0         1         0
[16,]         0         0         0         0         0        -1         0
[17,]         0         0         0         0         0         0         1
[18,]         0         0         0         0         0         0        -1

Is there a reason the coefficients generated by this design are so different in some cases from the "basic" design?  

Thanks for any help,
John Fernandes

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