[BioC] intraSpotCorrelation consensus values for Single Channel analysis in limma

[Thierry Janssens]

Dear BioC,

while performing Single Channel Analysis in limma, according to chapter 
9 of the limma users guide, I notice that the R and G foreground 
intensities are not correlated at all. I did not find a thread about 
that problem on the forum. I am wondering what the cause could be...

The experiment is an unconnected/saturated design of 5 conditions, on 
whcih I want to perform t-tests between the conditions.


...
 > RGbc <- backgroundCorrect(RGlist, method = "edwards", offset = 30)
 > MA <- normalizeWithinArrays(RGbc[j, ], method ="loess")
 > targets
   Archive      Filename Cy5 Cy3
1     File  SSArray1.txt   A   B
2     File  SSArray2.txt   B   C
3     File  SSArray3.txt   C  AC
4     File  SSArray4.txt  AC  AB
5     File  SSArray5.txt  AB   A
6     File  SSArray6.txt   A   C
7     File  SSArray7.txt   C  AB
8     File  SSArray8.txt  AB   B
9     File  SSArray9.txt   B  AC
10    File SSArray10.txt  AC   A
 > #sorteren op duplo
 > o <- order(MA$genes$ProbeUID)
 > MAsorted <- MA[o,]
 > o <- order(MAbet$genes$ProbeUID)
 > MAbetsorted <- MAbet[o,]
 > r <- 0
 > for(q in seq(1, nrow(MAbetsorted), 3)) {
+    r <- as.numeric((identical(MAbetsorted$genes$probeUID[q], 
MAbetsorted$genes$probeUID[q+1]))
+    && (identical(MAbetsorted$genes$probeUID[q], 
MAbetsorted$genes$probeUID[q+2])) ) + r
+ }
 > r
[1] 5069
 > # r moet 5069 zijn
 > # Separate channel analysis in limma
 > MAbetsortedav <- avedups(MAbetsorted, ndups = 3, spacing =1)
 > targets <- readTargets("filelist.txt")
 > targetstest <- targetsA2C(targets)
 > u <- unique(targetstest$Target)
 > f <- factor(targetstest$Target, levels=u)
 > design <- model.matrix(~0+f)
 > colnames(design) <- u
 > design
   B A C AC AB
1  1 0 0  0  0
2  0 1 0  0  0
3  0 0 1  0  0
4  1 0 0  0  0
5  0 0 0  1  0
6  0 0 1  0  0
7  0 0 0  0  1
8  0 0 0  1  0
9  0 1 0  0  0
10 0 0 0  0  1
11 0 0 1  0  0
12 0 1 0  0  0
13 0 0 0  0  1
14 0 0 1  0  0
15 1 0 0  0  0
16 0 0 0  0  1
17 0 0 0  1  0
18 1 0 0  0  0
19 0 1 0  0  0
20 0 0 0  1  0
attr(,"assign")
[1] 1 1 1 1 1
attr(,"contrasts")
attr(,"contrasts")$f
[1] "contr.treatment"
 > corfit <- intraspotCorrelation(MAbetsortedav, design)
Warning messages:
1: In remlscore(y, X, Z) : reml: Max iterations exceeded
2: In remlscore(y, X, Z) : reml: Max iterations exceeded
 > corfit$consensus.correlation
[1] *0.06922669

*In previous threads I read that this correlation should be 0.8-0.9 
(after backtransformation with tanh). What now?


kind regards,

Thierry

-- 
Thierry K.S. Janssens
Vrije Universiteit Amsterdam
Faculty of Earth and Life Sciences
Institute of Ecological Science
Department of Animal Ecology,
De Boelelaan 1085
1081 HV AMSTERDAM, The Netherlands
Phone: +31 (0)20-5989147
Fax: +31 (0)20-5987123
thierry.janssens at ecology.falw.vu.nl