[BioC] Question about design in limma

[January Weiner]

Hello,

I've been presented with the following design:

   Tissue strain  Cy5  Cy3
1   tis1     KO   d0  d01
2   tis1     KO  d01   d0
3   tis1     KO   d0  d03
4   tis1     WT  d03   d0
5   tis1     WT   d0  d01
6   tis1     KO  d03   d0
7   tis1     WT  d01   d0
8   tis1     WT   d0  d03
9   tis2     KO   d0  d01
10  tis2     KO  d01   d0
11  tis2     KO   d0  d02
12  tis2     KO  d02   d0
13  tis2     WT   d0  d01
14  tis2     WT  d01   d0
15  tis2     WT   d0  d02
16  tis2     WT  d02   d0
17  tis3     WT   d0  d03
18  tis3     WT  d03   d0
19  tis3     KO  d03   d0
20  tis3     KO   d0  d03

In summary: there are three different tissues. There are two strains
and four measurement points for the experiment (d0, d1, d2, d3), with
d0 as reference. The two-channel arrays have been loaded with RNA
samples such that the "day 0" from the respective tissue is the
reference. There is a dye swap present.

I am looking for differences in the strains -- which genes are
regulated in a different manner between WT and KO. How should I do it
best? obviously, simply comparing WT with KO is *not* the way to go,
since change in d04 (as compared with d0) can be very different from
response in d02, and also response is very variable between tissues.
However, I am looking for genes that are, for example, differentially
expressed in the KO, but not changing in the WT.

One way of solving that would be splitting the data sets, fitting the
model separately to the subsets, creating lists for subsets (e.g. list
of genes diff. expressed in KO, list of DE genes in WT) and comparing
the subsets. It works, but I don't like it.

Best regards,

j.

-- 
-------- Dr. January Weiner 3 --------------------------------------
Max Planck Institute for Infection Biology
Charitéplatz 1
D-10117 Berlin, Germany
Web   : www.mpiib-berlin.mpg.de
Tel     : +49-30-28460514