[BioC] nested design in limma?
smyth at wehi.edu.au
Wed Feb 22 12:16:01 CET 2006
Four levels of random variation (spots within arrays within
dye-swap-pairs within dams)! My approach to designs like this, at
least as a start, is to try duplicateCorrelation() on each of the
levels separately to get an idea of the strength of the correlation
at each level. Very often, some of the levels are so weak that they
can be ignored.
Just as an aside, I am continually amazed at how common technical
dye-swaps are. As far as I can see, they just complicate the analysis
to no advantage, yet they have captured the imagination of many
biologists. My guess is that this an attempt to balance the dyes,
although this can be better achieved without introducing technical replication.
At 04:27 AM 22/02/2006, Jenny Drnevich wrote:
>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.
>At 01:37 AM 2/21/2006, Gordon K Smyth wrote:
>>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
>>Hence you can use the duplicateCorrelation() function in limma with
>>dam as the block.
>>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
>1201 W. Gregory Dr.
>Urbana, IL 61801
>e-mail: drnevich at uiuc.edu
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