[BioC] paired experiments in limma

[Pedro Jares]

I am using affymetrix arrays to study primary and
metastic tumors from the same patients (five patients,
so five primary and five metastatic tumors). As we
have two classes and paired experiments I have been
using a paired t-test. However, I would like to use
limma with my arrays. Somewhere, I found that I could
introduce a block in order to get the lmFit. I have
been doing that for a while and getting pretty nice
results. However, recently in the new limma manual
appears that the block option should be used for
technical replicates, and you need to calculate
duplicateCorrelation. As far as I understood using
block argument alone in lmFit  you assume a default
correlation of 0.75. 
However, if I do duplicateCorrelation, the correlation
is 0.5. When I compare the top 50 genes that I got
doing block without dupCorr and with dupCorr, I see
that the p values and B values are worst for block
with dupCorr,  moreover if I do a hierarchical cluster
of the samples using the two list of genes the one
obtained by doing block without dupCorr seems to
performs much better. 
I would like to know what to do in order to analyze
paired experiments (this will allow us to remove
inter-person variability!!!,) to compare two classes
with single color arrays in limma. I have the
impression that paired experiments are clearly
different to technical replicates. So, I would
appreciate if anybody can tell if I should use
duplicateCorrelation or  something else. 
 
The commands I have been using  are:  
 
fit<-lmFit(eset,design,block=c(1,1,2,2))
 
 
or
 
corfit<-duplicateCorrelation(eset,design,block=c(1,1,2,2))

fit<-lmFit(eset,design,block=c(1,1,2,2),correlation=corfit$consensus)


Thank you very much for your help and your time,

Best wishes,

Pedro