[BioC] timecourse + factorial + replicates in LIMMA

Naomi Altman naomi at stat.psu.edu
Wed Sep 12 19:23:04 CEST 2007


In this situation (all technical replicates) you cannot really make 
biological conclusions about differences among strains.  You can only 
make conclusions about differences among organisms in which case the 
technical replicates are treated as though independent (but should 
come from independent samples from the organisms- i.e. independent 
RNA extractions).

In general, even if you want to get the means for each strain, you 
need to use the model with intercept for computing the correlations.

--Naomi

At 11:57 AM 9/12/2007, aaron.j.mackey at gsk.com wrote:
>"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
> >
> >
>
>_______________________________________________
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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



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