Hi, this is not a very interesting question but it has given me enough trouble to get me to mail the list so I hope somebody has time to reply. I have several two-colour arrays to analyze. Each probe is present three times on each chip and they are spaced 112 spots apart (not my decision). The consensus correlation returned by duplicateCorrelation is typically around zero which is surprising since the spots are close together and the data looks good in MA plots (even before normalization). A histogram of the individual correlations (cor$all.correlations from duplicateCorrelation) supports the conclusion that the within-chip replicates are poorly correlated. I am concerned that the numbers that are being handed to duplicateCorrelation are incorrect somehow but I am not sure what I am doing wrong (code below). I have looked at the code for duplicateCorrelation and cannot follow it so I was wondering if anyone can suggest a way to verify the correlations it is calculating. Ideally I would like to be able to select a specific gene, calculate the correlation between replicates myself and verify that this is the same as I obtain from duplicateCorrelation. Thanks in advance, Devin library(limma) targets <- readTargets() targets SlideNumber Name FileName Cy3 Cy5 13 13 60H_9:12 13.csv WT1 60H1 17 17 60H_12:9 17.csv 60H1 WT1 flag.check <- function(x) as.numeric(x$Flags >= 3) RG <- read.maimages(targets$FileName, sep=",", columns=list(Rf="Ch1 Median",Gf="Ch2 Median",Rb="Ch1 B Median",Gb="Ch2 B Median"), wt.fun=flag.check) RG$genes <- readGAL() RG$printer <- getLayout(RG$genes) RG.bgc <- backgroundCorrect(RG, method="normexp", offset=50) MA <- normalizeWithinArrays(RG.bgc, method="loess") design <- cbind(c(1,-1)) cor <- duplicateCorrelation(MA, design, ndups=3)