[BioC] continued dye effects, after normalization

[Kevin R. Coombes]

Hi,

What kind of arrays are these?

We had a similar problem with Agilent arrays, which contain a few 
hundred positive controls that are the brightest spots in the green 
channel but are invisibly dark in the red channel.  Any normalization in 
use today makes it look like there is a strong dye effect if you leave 
the controls in.  However, if you first remove the controls and then 
normalize, the dye effect disappears.

Best,
	Kevin

Jenny Drnevich wrote:
> Hi all,
> 
> I've been analyzing a spotted array experiment that used a common 
> reference with a 2X2 factorial design. There were no technical dye 
> swaps, but half of the 6 replicates in each group had the ref in Cy3 and 
> half had the ref in Cy5. Now that Jim has modified plotPCA to accept 
> matrices, I was checking for any unsuspected groupings that might 
> indicate block effects. To my surprise, the arrays were still grouping 
> based on the reference channel, even after inverting the M-values so 
> that the reference channel was always in the denominator! Attached is a 
> figure with 2 PCA plots, and hopefully it is small enough to make it 
> through; the code that created them is below.  Has anyone else noticed 
> this, and what have you done about it? I went back and checked some 
> other experiments that used a common reference, and they also mostly 
> showed a continued dye grouping. A between-array scale normalization, 
> either on the regular M-values or on inverted M-values, failed to remove 
> the dye effect as well. I didn't try other normalizations, but instead 
> included 'ref dye' as a blocking variable. The consensus correlation 
> from duplicateCorrelation was 0.154, which when included in the lmFit 
> model increase the number of genes found significantly different.
> 
> I have been working with a physics professor and his student who have 
> developed a different data mining algorithm, which shows these dye 
> effects even more strongly than PCA. They are suggesting another 
> normalization is needed to remove the ref dye effect, and they want to 
> normalize the ref dye groups separately. Doing a separate normalization 
> doesn't seem like a good idea to me, and I wanted to get other opinions 
> on the dye effect, my approach, and other normalization options.
> 
> Thanks!
> Jenny
> 
> code:
> 
> RG <- read.maimages(targetsb$FileName,path="D:/MA Jenny",
>                 source="genepix.median",names=targetsb$Label,wt.fun=f)
> 
> RG.half <- backgroundCorrect(RG,method="half")
> 
> MA.half <- normalizeWithinArrays(RG.half)
> 
> temp <- MA.half
> temp$M[,targetsb$Cy3=="ref"] <- -1 * temp$M[,targetsb$Cy3=="ref"]
> 
> layout(matrix(1:2,2,1))
> plotPCA(MA.half$M,groups=rep(c(1,2,1,2,1,2,1,2),each=3),groupnames=c("ref 
> G","ref R"))
>         # PC1 divides the arrays by which channel the ref was in
> plotPCA(temp$M,groups=rep(c(1,2,1,2,1,2,1,2),each=3),groupnames=c("ref 
> G","ref R"))
>         # after inverting the M-values for half the arrays, PC1 divides 
> the arrays by one of the treatments, but
>         # the dye effect still shows up in PC2
> 
> 
> MA.half.scale <- normalizeBetweenArrays(MA.half,method="scale")
> 
> design <- modelMatrix(targetsb,ref="ref")
> 
> block <- rep(c(1,2,1,2,1,2,1,2),each=3)
> 
> corfit <- duplicateCorrelation(MA.half.scale[RG$genes$Status=="cDNA",], 
> design, ndups=1, block=block)
> 
> corfit$consensus
>     #[1] 0.1537080
> 
> 
> 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 uiuc.edu
> 
> 
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