[BioC] continued dye effects, after normalization

[Henrik Bengtsson]

Hi Jenny,

try affine normalization, which corrects for both global background
(offset) as well as non-linear effects on the log-scale (read
intensity dependent effects) in one step.  See normalizeAffine(X,
constraint=0.05) in the aroma.light package, where X is a matrix. By
adjusting the 'constraint' argument you can adjust the amount of
background subtracted.

Try also to stratify your PCR by different intensities.  It is more
likely that you see a "dye bias" at lower intensities than at higher,
which is mainly because the background correction was not perfect.

Cheers

Henrik

On 1/11/07, Jenny Drnevich <drnevich at uiuc.edu> 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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