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

[Jenny Drnevich]

Hi Thierry,

What kind of dual-channel arrays are these? I've seen very low 
intra-spot correlations from Agilent arrays, which have extremely 
high array to array spot consistency. Old-style pin-tip printed 
arrays had high intra-spot correlations because the amount of probe 
per spot could not be controlled very well from array to array.

Cheers,
Jenny

At 10:16 AM 5/13/2009, Thierry Janssens wrote:
>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
>
>
>
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Jenny Drnevich, Ph.D.

Functional Genomics Bioinformatics Specialist
W.M. Keck Center for Comparative and Functional Genomics
Roy J. Carver Biotechnology Center
University of Illinois, Urbana-Champaign

330 ERML
1201 W. Gregory Dr.
Urbana, IL 61801
USA

ph: 217-244-7355
fax: 217-265-5066
e-mail: drnevich at illinois.edu