[BioC] timecourse + factorial + replicates in LIMMA

[Naomi Altman]

Why would you want to use duplicateCorrelation?  This is for error 
correlation.  Presumably your replicates are biologically distinct, 
and required for the test statistic denominator.

However, to answer your question, this is due to removing the 
intercept.  With no intercept, the correlation is computed without 
removing the mean and this pretty much makes all the correlation 1.

--Naomi

At 04:38 PM 9/11/2007, aaron.j.mackey at gsk.com wrote:
>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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Naomi S. Altman                                814-865-3791 (voice)
Associate Professor
Dept. of Statistics                              814-863-7114 (fax)
Penn State University                         814-865-1348 (Statistics)
University Park, PA 16802-2111