[BioC] dye swaps of technical replicates and variable numbers of replicate spots

[Gordon Smyth]

At 02:23 AM 20/08/2003, Ramon Diaz-Uriarte wrote:
>Dear all,
>
>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.
>
>...

>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:
>
> > design
>   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)

You're actually overparametrized here. (I hadn't turned my brain on 
properly in my previous email.) The differences between the three 
biological replicates contribute only two degrees of freedom, not three, 
and your first effect will not estimate what you want. You need something 
equivalent to like:

 > design
   Efect R2 R3
1     -1   0   0
2      1   0   0
3     -1   1   0
4      1   1   0
5     -1   0   1
6      1   0   1

The three coefficients will represent: (1) The RNA comparison that you're 
interested in, (2) The difference between biological replicate 2 versus 1, 
and (3) The difference between biological replicate 3 versus 1.

Gordon

>Does this make sense?
>
>Thanks,
>
>Ramón