 Dear Limma users,

I am working on custom spotted 70mer oligo arrays, and use Bluefuse to
analyse the images. With the help of the excellent user guide and
Bioconductor user forum(GMANE), i have analysed my direct comparison
experiements. I also have common reference, time course and direct two color
design type experiments to analyse. I have read the recent posting in the
list  about using Rquantile or Gquantile for normalizing between arrays in
common reference experiments. I tried to do a common references analysis
using the discussed code.But the resulting gene list is different from the
expected list.i am also wondering how to account for dye swaps. I have
pasted the code which i used for common reference.

It will also be very useful if you any one could tell me how to use
normalization between arrays for direct two color designs.

My experiment design is
          Cy3   Cy5
____________________
Exp1  Ref    CpdA
Exp2  Ref    CpdA
Exp3  CpdA Ref

Exp4  Ref   CpdB
Exp5  Ref   CpdB
Exp6 CpdB Ref

Code which i used for analysing common referencec:
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library(limma)
targets <- readTargets("commonref.txt", row.names="Name")
RG <- read.maimages(targets$FileName, source="bluefuse")
RG$genes <- readGAL()
RG$printer <- getLayout(RG$genes)
spottypes <- readSpotTypes()
RG$genes$Status <- controlStatus(spottypes, RG)
isGene <- RG$genes$Status == "oligos"
MA.Gquantile <- normalizeBetweenArrays(RG[isGene,], method="Gquantile")
RG.Gquantile <- RG.MA(MA.Gquantile)
MA.dummy <- MA.Gquantile
MA.dummy$M <- log2(RG.Gquantile$R)
o <- order(MA.dummy$genes$ID)
MA.sorted <- MA.dummy[o,]
design <- modelMatrix(targets, ref="Ref")
fit <- lmFit(MA.sorted, design, ndups=2, spacing=1, correlation=0)
fit.eb <- eBayes(fit)
write.fit(fit.eb, file="data/commonref.xls", adjust="BH")
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thanks in advacne

with regards,
Vinoy......

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