[BioC] Design/Contrast for Two-Channel Experimental Setup

Joseph Shaw [guest] guest at bioconductor.org
Sat Jan 4 15:43:02 CET 2014

Hi all,

I'm currently looking at data collected from a two-channel microarray experiment; the experimental design is as follows:

 - The data represents the results of a competitive hybridization process between control RNA and treatment RNA.
 - The data comprises n*m slides (*n* biological replicates and *m* technical replicates for each biological replicate).
 - The control label dye (cy5) treatment label dye (cy3) remain the same across all slides - hence, **there is no dye-swap aspect to the experiment**.
 - The data were generated by ScanArray Express and slide data are stored in separate .csv files. 

I'm very new to the limma package. Is it possible to use the limma package to identify differentially expressed genes for this experimental setup?

If so,

 - how can the design matrix be specified? will a "dye effect" term still be required even if there is no dye-swap?
 - is a contrast matrix necessary for this procedure?
 - are there any specialist normalisation techniques required for this setup?

My code so far is as follows:

># Assuming the contents of the targets file have been identified:
> RG<-read.maimages(targets, source="scanarrayexpress", sep=",")
> RGbk <- backgroundCorrect(RG, method="normexp", offset=50)
> MA <- normalizeWithinArrays(RGbk, method="loess")
> MA.b=normalizeBetweenArrays(MA, method="quantile")
> design <- modelMatrix(targets, ref="control") # nmx1 matrix; all elements set to  -1. 
> fit <- lmFit(MA, design)
> fit <- eBayes(fit)
> topTable(fit, coef=1, adjust="fdr")

Any assistance with the above would be greatly appreciated.


 -- output of sessionInfo(): 

> sessionInfo()
R version 3.0.2 (2013-09-25)
Platform: x86_64-apple-darwin10.8.0 (64-bit)

[1] en_IE.UTF-8/en_IE.UTF-8/en_IE.UTF-8/C/en_IE.UTF-8/en_IE.UTF-8

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

other attached packages:
[1] limma_3.18.7

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