continued dye effects, after normalization
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Jenny Drnevich ★ 2.2k
@jenny-drnevich-382
Last seen 7.1 years ago
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 Normalization Normalization • 743 views ADD COMMENT 0 Entering edit mode @kevin-r-coombes-1589 Last seen 7.1 years ago 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 > > > -------------------------------------------------------------------- ---- > > _______________________________________________ > Bioconductor mailing list > Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor
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Hi Kevin, Thanks for your suggestion. They are spotted cDNA arrays. There are ~1300 non-cDNA spots on the arrays, about 1000 blanks and 300 controls. Removing these non-cDNA genes before normalizations (either within or between) didn't seem to have much effect, but I did realize that my PCA plots from before had the non-cDNAs in them. When I remove the non-cDNA spots and do the PCA plot, the dye effect is no longer seen strongly in PC2 alone, but it is strongly seen when plotting PC2 vs. PC3. PC2 and PC3 are about 50% and 30% of PC1, respectively, much less than when the non-cDNA were included, so this is helpful. However, I'm still back to wondering if I should attempt another normalization, or just account for the dye effect in the model as a blocking variable? Thanks, Jenny At 11:07 AM 1/10/2007, Kevin R. Coombes wrote: >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("r ef >>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 >> >>-------------------------------------------------------------------- ---- >>_______________________________________________ >>Bioconductor mailing list >>Bioconductor at stat.math.ethz.ch >>https://stat.ethz.ch/mailman/listinfo/bioconductor >>Search the archives: >>http://news.gmane.org/gmane.science.biology.informatics.conductor > >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 ADD REPLY 0 Entering edit mode @henrik-bengtsson-4333 Last seen 4 months ago United States 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 > > _______________________________________________ > Bioconductor mailing list > Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor > >
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Entering edit mode
Jenny Drnevich ★ 2.2k
@jenny-drnevich-382
Last seen 7.1 years ago
Thanks Henrik, Where can I find documentation on this affine normalization? There is no real help page for normalizeAffine, just a generic "Non-doumented Objects" page. Cheers, Jenny At 12:18 AM 1/14/2007, Henrik Bengtsson wrote: >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("r ef >>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 >> >>_______________________________________________ >>Bioconductor mailing list >>Bioconductor at stat.math.ethz.ch >>https://stat.ethz.ch/mailman/listinfo/bioconductor >>Search the archives: >>http://news.gmane.org/gmane.science.biology.informatics.conductor >> > >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 ADD COMMENT 0 Entering edit mode [Forgot to reply to everyone] Hi Jenny, sorry about that. I design my help indices for help.start() / PDF help. The particular one you're looking for is under ?normalizeAffine.matrix. There is also one help section "1. Calibration and Normalization" with more general comments. The main reference is: [1] Henrik Bengtsson and Ola H?ssjer, _Methodological Study of Affine Transformations of Gene Expression Data_, Methodological study of affine transformations of gene expression data with proposed robust non-parametric multi-dimensional normalization method, BMC Bioinformatics, 2006, 7:100. You'll also find a few talks on the topic at: http://www.maths.lth.se/bioinformatics/talks/ Hope this helps Henrik On 1/17/07, Jenny Drnevich <drnevich at="" uiuc.edu=""> wrote: > Thanks Henrik, > > Where can I find documentation on this affine normalization? There is no > real help page for normalizeAffine, just a generic "Non-doumented Objects" > page. > > Cheers, > Jenny > > At 12:18 AM 1/14/2007, Henrik Bengtsson wrote: > >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 > >> > >>_______________________________________________ > >>Bioconductor mailing list > >>Bioconductor at stat.math.ethz.ch > >>https://stat.ethz.ch/mailman/listinfo/bioconductor > >>Search the archives: > >>http://news.gmane.org/gmane.science.biology.informatics.conductor > >> > > > >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 > >