[BioC] dye swaps of technical replicates and variable numbers of
rdiaz at cnio.es
Tue Aug 19 19:23:42 MEST 2003
I am analyzing some cDNA data; in the simplest case there are a total of 6
arrays, with three biological replicates; for each biological replicate, the
arrays are duplicated and arrayed using dye-swap. Of course, for some genes
there might be missing values in some of the replicates.
In addition, some genes are replicated within arrays 5 times, whereas other
genes are replicated twice (or three times, or four times, or six times), and
yet others are not replicated at all.
These are the two questions:
1. The limma package includes facilities for handling replicate spots within
arrays. However, from the help pages and the Bob mutant data example in the
limma manual, it seems to me that it expects a fairly regular structure.
I understand that my two options are:
a) take the easy way out, and compute a mean or a median of the replicates;
b) "adapt" dupcor.series to my situation to get an estimate of the correlation
of replicates, and then "adapt" gls.series (or call gls directly);
Is there any other option?
2. The dye-swap set up resembles the swirl example in the limma manual, but
here the dye swaps are of technical replicates. The first idea that came to
my mind is to fit (e.g., using the nlme package) a random effects model like:
lme(log.ratio ~ the.interesting.effect, random = ~1|the.biological.replicate)
but since I am only interested in the interesting effect (not the replicate
variation) I think I can get what I want with limma doing:
Efect R1 R2 R3
1 0 1 0 0
2 1 1 0 0
3 0 0 1 0
4 1 0 1 0
5 0 0 0 1
6 1 0 0 1
> lm.series(data, design)
Does this make sense? Does it make sense given the mess with the variable
number of replicates within arrays (question 1)?
Centro Nacional de Investigaciones Oncológicas (CNIO)
(Spanish National Cancer Center)
Melchor Fernández Almagro, 3
28029 Madrid (Spain)
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