[BioC] nested design in limma?

[Jenny Drnevich]

Hi Gordon,

I didn't know a nested design would be handled the same as duplicate spots, 
since duplicate spots are technical replicates but multiple offspring are 
independent replicates. I guess when I have some free time I'll look into 
the math of how the block and correlation are used in lmFit... 
Unfortunately, this solution doesn't help me in this case because there are 
also both duplicate spots and technical replicates of arrays! If 
duplicateCorrelation can only be used once, I was going to average the 
duplicate spots, use duplicateCorrelation for the dye-swapped tech reps, 
fit a coefficient for each dam, and then extract the difference between 
sets of dams as a contrast. I know this will treat dam as a fixed effect, 
rather than as a random effect, but I'm not sure if there's a better way to 
do it.

Cheers,
Jenny

At 01:37 AM 2/21/2006, Gordon K Smyth wrote:
>Hi Jenny,
>
>This design is qualitatively the same as the "duplicate spot" situation, 
>where the treatment is
>applied at the array level but the measurements are made on multiple spots 
>per array.  In your
>case, treatments are applied to dams but measurements are made on multiple 
>offspring.
>
>Hence you can use the duplicateCorrelation() function in limma with dam as 
>the block.
>
>Best wishes
>Gordon
>
>On Tue, February 21, 2006 6:03 am, Jenny Drnevich wrote:
> > Hello,
> >
> > I was wondering if there was any (easy) way to handle a nested design in
> > limma. I looked in the Bioconductor archives, but the only references to
> > nested designs weren't really nested - one was just a factorial design, and
> > the other was a repeated measurement design, which could be done in limma
> > as a blocking variable. In this experiment design, the treatments (infected
> > and control) were made on the dams, but the effects were measured on
> > multiple offspring per dam; hence dam is nested within treatment. In SAS
> > terminology (forgive me...), the model would look like this:
> > log2_expression = treatment + dam(treatment) , with dam as a random
> > variable. The test statistic for treatment should now be formed using the
> > variance due to dam(treatment) and not the error variance. Can limma be
> > made to handle this sort of design?
> >
> > Thanks,
> > Jenny
> >
> > Jenny Drnevich, Ph.D.
> >
> > Functional Genomics Bioinformatics Specialist
> > W.M. Keck Center for Comparative and Functional Genomics
> > Roy J. Carver Biotechnology Center
> > University of Illinois, Urbana-Champaign
> >
> > 330 ERML
> > 1201 W. Gregory Dr.
> > Urbana, IL 61801
> > USA
> >
> > ph: 217-244-7355
> > fax: 217-265-5066
> > e-mail: drnevich at uiuc.edu

Jenny Drnevich, Ph.D.

Functional Genomics Bioinformatics Specialist
W.M. Keck Center for Comparative and Functional Genomics
Roy J. Carver Biotechnology Center
University of Illinois, Urbana-Champaign

330 ERML
1201 W. Gregory Dr.
Urbana, IL 61801
USA

ph: 217-244-7355
fax: 217-265-5066
e-mail: drnevich at uiuc.edu