[BioC] timecourse + factorial + replicates in LIMMA

aaron.j.mackey at gsk.com aaron.j.mackey at gsk.com
Tue Sep 11 22:38:42 CEST 2007


I have an experimental setup in which four strains (A, B, C and D) are 
given a treatment or control mock treatment, and observed (by Affy) over a 
post-treatment timecourse (4 timepoints); each strain/treatment/timepoint 
observation is performed in replicate.

At the end of the day, I'd like to answer two scientific questions:

1) which probesets are consistently (across all four strains) 
differentially expressed (treatment vs. control) at timepoints 2, 3 and 4?

2) which treatment-responsive probesets are consistently responsive within 
(but differentially responsive between) A&B and C&D strain groupings?

My target matrix looks like this:

array   strain   treatment   time
1          A        mock       1
2          A        mock       1
3          A        mock       1
4          A        mock       2
5          A        mock       2
6          A        mock       2
...
13         A      treated      1
14         A      treated      1
15         A      treated      1
16         A      treated      2
...
25         B        mock       1
26         B        mock       1
...
96         D      treated      4

I built my design matrix like so:

strain <- factor(target$strain); # etc. for treatment, time
design <- model.matrix(~0+strain*treatment*time)

And my "replicates" array looks like:

c(1,1,1, 2,2,2, 3,3,3, 4,4,4, 5,5,5, ..., 32,32,32)

Yet when I run duplicateCorrelation() to handle the replicates, I get a 
consensus correlation of 1, and "Inf" values for each correlation.

What have I done wrong?

(I haven't even gotten to building the contrast matrices to answer my 
questions of actual interest ...)

Thanks,

-Aaron



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