[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
More information about the Bioconductor
mailing list