[BioC] intraspotCorrelation vs duplicateCorrelation

[Naomi Altman]

I was having problems with intraspotCorrelation for a single channel 
analysis, so I decided to reorganize the data to use 
duplicateCorrelation.  I thought I would get the same answer, but I 
did not.  I hope someone can tell me why.  (As you will see below, I 
like the answer with duplicateCorrelation better!)

MAp is the normalized 2-channel data.
I have a targets list called targetp with the treatment 
information.  The code is below.

##############################################################
# separate channel approach
#############################################################
targetSingle <- targetsA2C(targetp)
rep=c(0,0,1,1,0,0,1,1,0,0,1,1)
designp=model.matrix(~-1+targetSingle$Target+rep,levels=unique(c(cy3,cy5)))
corfit =intraspotCorrelation(MAp, designp)
corfit$cor
NULL
##########################################################
# the warning was
#In remlscore(y, X, Z) : reml: Max iterations exceeded
##########################################################

############################################################
# approach treating channels as separate arrays
############################################################
RGp=RG.MA(MAp)
Rp=log2(RGp$R)
Gp=log2(RGp$G)
exprP=cbind(Rp[,1],Gp[,1],Rp[,2],Gp[,2],Rp[,3],Gp[,3],Rp[,4],Gp[,4],Rp[,5],Gp[,5],Rp[,6],Gp[,6])
corfitP = duplicateCorrelation(exprP, designp,  block = 
factor(c(1,1,2,2,3,3,4,4,5,5,6,6)))
corfitP$cor
[1] 0.4921254

sessionInfo()
R version 2.11.1 (2010-05-31)
i386-pc-mingw32

locale:
[1] LC_COLLATE=English_United States.1252  LC_CTYPE=English_United 
States.1252    LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C                           LC_TIME=English_United 
States.1252

attached base packages:
[1] tools     stats     graphics  grDevices 
utils     datasets  methods   base

other attached packages:
[1] statmod_1.4.8    rat2302cdf_2.6.0 
limma_3.4.5      affy_1.26.1      Biobase_2.8.0

loaded via a namespace (and not attached):
[1] affyio_1.16.0         preprocessCore_1.10.0


Thanks,
Naomi