[BioC] nested design in limma? -- revisit

[Shi, Tao]

Dear Jenny, Gordon, and the list,


This is a follow-up on the thread about handling nested design using LIMMA posted last year (please see https://stat.ethz.ch/pipermail/bioconductor/2006-February/012018.html 
).

I have a data set which has very similar design like Jenny's.  6 animals, 3 in the control group and 3 in the treated group.  2 biological samples were taken from each animal and each was hybdized to 2 arrays.  All arrays have duplicate sets of probes (so there are 4 technical replicates for each biological samples).  

After reading the posts by Jenny, Gordon and others, I'm a bit confused with what should be the final approach for this kind of design.  I do agree with Jenny that the different biological samples from the same animal (or in her case, different offsprings from the same dam) should not be treated as technical replicates, so I'm not quite sure about using duplicateCorrelation here.  What I did is first, summarized all the technical replicates (they are usually highly correlated) to get one measurement for each biological samples, then fitted LIMMA with a nested design model matrix (see below).  

Is this OK and like to hear more comments.  Thanks in advance.

...Tao


### after summarization of the technical replicates
> trt
 [1] control control control control control control treat   treat   treat   treat   treat   treat  
Levels: control treat
> animal
 [1] 1 1 2 2 3 3 1 1 2 2 3 3
Levels: 1 2 3
> 
> design.matrix <- model.matrix( ~ -1 +trt/animal)
> design.matrix
   trtcontrol trttreat trtcontrol:animal2 trttreat:animal2 trtcontrol:animal3 trttreat:animal3
1           1        0                  0                0                  0                0
2           1        0                  0                0                  0                0
3           1        0                  1                0                  0                0
4           1        0                  1                0                  0                0
5           1        0                  0                0                  1                0
6           1        0                  0                0                  1                0
7           0        1                  0                0                  0                0
8           0        1                  0                0                  0                0
9           0        1                  0                1                  0                0
10          0        1                  0                1                  0                0
11          0        1                  0                0                  0                1
12          0        1                  0                0                  0                1
attr(,"assign")
[1] 1 1 2 2 2 2
attr(,"contrasts")
attr(,"contrasts")$trt
[1] "contr.treatment"

attr(,"contrasts")$animal
[1] "contr.treatment"

> 
> fit <- lmFit(dat.temp, design=design.matrix)
....
contrast,
ebayes,
toptable,
.....







 
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