[BioC] timecourse + factorial + replicates in LIMMA

[aaron.j.mackey at gsk.com]

"Naomi Altman" <naomi at stat.psu.edu> wrote on 09/11/2007 05:23:59 PM:

> 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.

Sorry, I didn't explain myself very well.  The replicates are technical 
replicates - same biological organism, not distinct (there were four 
distinct organisms, from strains A, B, C and D).  I guess since I only 
have one biological replicate per strain, that the distinction between 
technical and biological replicates might not matter in this case.

> 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.

Thanks.  I removed the intercept because I wanted to be able to model each 
strain independently (with the intercept, I only get strains B, C and D as 
factors; A is subsumed by the intercept).

-Aaron

> 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
> 
>