[BioC] use of duplicateCorrelation in Limma with agilent one-color arrays

[Jabez Wilson]

Hi, everyone, I am using limma to analyse an agilent one-color array experiment, and have run into difficulties with duplicateCorrelation.
My experiment is as follows: single color agilent arrays, 4 WT samples, and 3 samples of each of 4 treatment (treatments 1-4). I also have technical replicates (replicated once) for each sample. There are therefore 32 files. The targets file looks like this:
 
   SampleNumber                  FileName Condition Notes
1             1  RH_02_1_77_Oct11_1_1.txt    Treat1   1.1
2             2  RH_02_1_77_Oct11_2_1.txt    Treat1   1.2
3             3  RH_04_1_77_Oct11_1_1.txt    Treat1   2.1
4             4  RH_07_1_77_Oct11_1_1.txt    Treat1   2.2
5             5  RH_04_1_77_Oct11_1_2.txt    Treat1   3.1
6             6  RH_07_1_77_Oct11_1_2.txt    Treat1   3.2
7             7  RH_04_1_77_Oct11_1_3.txt    Treat2   4.1
8             8  RH_07_1_77_Oct11_1_3.txt    Treat2   4.2
9             9  RH_04_1_77_Oct11_1_4.txt    Treat2   5.1
10           10  RH_07_1_77_Oct11_1_4.txt    Treat2   5.2
11           11  RH_04_1_77_Oct11_2_1.txt    Treat2   6.1
12           12  RH_07_1_77_Oct11_2_1.txt    Treat2   6.2
13           13 US0_05_1_77_Oct11_1_1.txt    Treat3   7.1
14           14  RH_01_1_77_Oct11_1_1.txt    Treat3   7.2
15           15 US0_05_1_77_Oct11_1_2.txt    Treat3   8.1
16           16  RH_01_1_77_Oct11_1_4.txt    Treat3   8.2
17           17  RH_02_1_77_Oct11_1_2.txt    Treat3   9.1
18           18  RH_02_1_77_Oct11_2_2.txt    Treat3   9.2
19           19 US0_05_1_77_Oct11_1_3.txt    Treat4  10.1
20           20  RH_01_1_77_Oct11_1_2.txt    Treat4  10.2
21           21 US0_05_1_77_Oct11_1_4.txt    Treat4  11.1
22           22  RH_01_1_77_Oct11_1_3.txt    Treat4  11.2
23           23  RH_04_1_77_Oct11_2_2.txt    Treat4  12.1
24           24  RH_07_1_77_Oct11_2_2.txt    Treat4  12.2
25           25 US0_05_1_77_Oct11_2_1.txt        WT  13.1
26           26  RH_01_1_77_Oct11_2_1.txt        WT  13.2
27           27 US0_05_1_77_Oct11_2_2.txt        WT  14.1
28           28  RH_01_1_77_Oct11_2_2.txt        WT  14.2
29           29 US0_05_1_77_Oct11_2_3.txt        WT  15.1
30           30  RH_01_1_77_Oct11_2_3.txt        WT  15.2
31           31 US0_05_1_77_Oct11_2_4.txt        WT  16.1
32           32  RH_01_1_77_Oct11_2_4.txt        WT  16.2

I run the following commands to process the data and create the design:
RG <- read.maimages(targets, columns = list(G = "gMedianSignal", Gb = "gBGMedianSignal", R = "gProcessedSignal",Rb = "gIsPosAndSignif"), annotation = c("Row", "Col","FeatureNum","ControlType","ProbeName"))
RG <- backgroundCorrect(RG, method="normexp", offset=1)
E <- normalizeBetweenArrays(RG, method="Aquantile")
E.avg <- avereps(E, ID=E$genes$ProbeName)
f <- factor(targets$Condition, levels = unique(targets$Condition))
design <- model.matrix(~0 + f)
colnames(design) <- levels(f)
 
The problem arises when I do the duplicateCorrelation. 
biolrep <- c(1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,11,11,12,12,13,13,14,14,15,15,16,16)
corfit <- duplicateCorrelation(E.avg, design, ndups=1,block=biolrep)
fit <- lmFit(E.avg$A, design, block=biolrep, cor=corfit$consensus.correlation)
contrast.matrix <- makeContrasts("Treat1-WT",levels=design)                                                            fit2 <- contrasts.fit(fit, contrast.matrix)
fit2 <- eBayes(fit2)
topTable(fit2, adjust="BH", coef="Treat1-WT", genelist=E.avg$genes, number=10)

Whereas I would expect the corfit$consensus.correlation to be generally very positive, I get the value 0.01385223. Does anyone have any suggestions? Any help would be appreciated
 
Jabez