[BioC] Spike-ins normalization for Agilent one color arrays

Sarah Bonnin Sarah.Bonnin at crg.eu
Wed Jul 13 15:55:01 CEST 2011

Dear list,

I'm trying to normalize Agilent gene expression one-color arrays using the "spike-ins" probes.

I have followed section 7: "Normalisation with 'spike-in' probes" of the vsn vignette to do so.

I am having some doubts and I would like to be sure that what I do is correct, or be corrected if it's not! (I have read some threads about the subject but I don't manage to find exactly what I want, sorry if the question is redundant).

Can this method be applied to single channel arrays?
What would you advice to do to check if that kind of normalization is needed and then successful on this type of arrays?
Is this normalization to be processed after background correction, and is it to be combined with another type of normalization (quantile?)?
Is there another method for spike-ins normalization that I don't know about? GeneSpring proposes apparently this option but I don't know how exactly they apply it.

Please find below my code and session info, and I would be glad for any feedback or advice you could give me...

Thanks a lot!


# read in feature extraction files
Rawdata <- read.maimages(Files, source="agilent", green.only=TRUE, columns=list(G="gMedianSignal", Gb="gBGMedianSignal"), annotation=c("ProbeName", "ControlType"))
G.bkg <- backgroundCorrect(Rawdata, method="normexp", offset=50)

# apply vsn2
spfit = vsn2(as.matrix(G.bkg)[grep("\\(\\+\\)E1A",Rawdata$genes$ProbeName),], lts.quantile=0.75)
new.G <- predict(spfit, newdata=as.matrix(G.bkg))

R version 2.13.0 (2011-04-13)
Platform: x86_64-pc-mingw32/x64 (64-bit)

[1] LC_COLLATE=English_United States.1252  LC_CTYPE=English_United States.1252   
[3] LC_MONETARY=English_United States.1252 LC_NUMERIC=C                          
[5] LC_TIME=English_United States.1252    

attached base packages:
[1] grid      stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] limma_3.8.1       vsn_3.20.0        Biobase_2.12.1    VennDiagram_1.0.1

loaded via a namespace (and not attached):
[1] affy_1.30.0           affyio_1.20.0         KernSmooth_2.23-4     lattice_0.19-23      
[5] preprocessCore_1.14.0 tools_2.13.0    

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