Dear list,

This question might be a bit redundant and I apologize for it, if it is, but I can't find a clear answer to what I'm trying to do.

I am working on a set of 12 expression one-channel arrays.

My target file is basically as follows:
FileName Pairs Groups
Sample1        1 Group1
Sample2        1 Group1
Sample3        1 Group2
Sample4        1 Group2
Sample5        1 Group3
Sample6        1 Group3
Sample7        2 Group1
Sample8        2 Group2
Sample9        2 Group3
Sample10        3 Group1
Sample11        3 Group2
Sample12        3 Group3

There are several parameters to take into account:

- I want to produce all possible pairwise comparisons (Group3-Group2, Group2-Group1, Group3-Group1): "Groups" column

- I want my design to take into account the paired samples: "Pairs" column

- The last thing is that some samples are technical replicates (Sample1 with Sample2, Sample3 with Sample4, Sample5 with Sample6) and I would also like to take this into account.

I've read the "8.7 Multi-level experiments" chapter of limma user guide, which guided me into combining paired data and multiple comparisons, in which case I would do:
> design <- model.matrix(~0+factor(targets$Groups))
> colnames(design) <- unique(targets$Groups)
> corfit <- duplicateCorrelation(eset,design,block=targets$Pairs)
> fit <- lmFit(eset,design,block=targets$Pairs,correlation=corfit$consensus)

In theory to take into account technical replicates I would use:
> biolrep <- c(1,1,2,2,3,3,4,5,6,7,8,9)
> corfit <- duplicateCorrelation(eset, block = biolrep)
> fit <- lmFit(eset, block = biolrep, cor = corfit$consensus)

But how can I combine all of this?

Is there a way to somehow pass both paired and technical replication information into the duplicateCorrelation step? Or should I modify the design instead to take into account the paired design?

It is getting quite confusing for me.

Any help greatly appreciated!

Thanks in advance!




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