Hi Jenny, No normalization method can remove probe-specific dye-effects. If they exist, as seems common, probe-specific dye-effects are systematic rather than random effects, so you need to include them as a coefficient in the linear model, not treat them as a blocking variable. You do this simply by including an intercept in the linear model, see Section 8.1.2 (Dye-Swaps) of the limma User's Guide. Probe-specific dye-effects seem real for all two-colour platforms, see for example http://www.biomedcentral.com/1471-2105/7/511 BTW, I don't see any good reason to use dye-swaps in a common reference design. If you use the same channel for the reference sample throughout, any dye-effects will cancel out of any analysis. Best wishes Gordon At 10:00 PM 11/01/2007, bioconductor-request at stat.math.ethz.ch wrote: >Date: Wed, 10 Jan 2007 10:42:23 -0600 >From: Jenny Drnevich <drnevich at="" uiuc.edu=""> >Subject: [BioC] continued dye effects, after normalization >To: bioconductor at stat.math.ethz.ch >Cc: Yoshi Oono <yoono at="" uiuc.edu="">, Satwik Rajaram <srajaram at="" uiuc.edu=""> >Message-ID: <6.2.1.2.2.20070110094334.035ab450 at express.cites.uiuc.edu> >Content-Type: text/plain; charset="us-ascii" > >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("re f >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