[BioC] Nested design in limma

[caroline.truntzer at chu-lyon.fr]

Dear list,
My question is a follow-up of the thread about handling nested design using
limma posted by Tao Shi (please see
https://stat.ethz.ch/pipermail/bioconductor/2007-January/015717.html).
I have a data set which has a similar design as Tao Shi: 14 patients (7 in
one group, 7 in another group), 2 biological samples for each patients
(corresponding to 2 different extractions), and each extraction is
hybridized to 2 arrays and I have triplicate sets of probes. I would like
to identify genes that have differential expression between the 2 groups.
I read the responses written to Tao on how to analyse this data set, but
there are some things I didn't understand. 
The advice was to use avedups() to average over the triplicate probes, and
then to treat the patients as biological replicates (as blocks using
duplicateCorrelation). But by doing so I do not understand how the two
other replication levels are treated, that is extraction and hybridization.
Is it possible to keep the information of this two replication levels in
the analysis? Is it possible to set different levels in blocks (given the
help for the duplicateCorrelation fonction I think it is not possible but
perhaps someone found a mean to do that)?
Moreover I think I'm confused with what should be put in the design matrix
and what should rather be put in the blocks vector. I'm sorry for this
naive question...
Thanks in advance for your help
Caroline