Dear all Here I post again the question about normalization I'm sorry that this question might be obvious for statistician. The general question: How to validate the normalization outcome? Density plots? I have tried "loees with aquantile" and "vsn" and outcome of the decideTests is more or less the same - a lot of probes with differential expression. Here below the code I used in limma: RG <- read.maimages(...) ...assigning spotTypes ...removing controlspots from the RG RGb <- backgroundCorrect(RG,method="minimum") MA <- normalizeWithinArrays(RGb, method="loess") MA <- normalizeBetweenArrays(MA, method="Aquantile") ...design fit <- lmFit(MA, design) ...contrast.matrix fit <- contrasts.fit(fit, contrast.matrix) fit <- eBayes(fit) res <- decideTests(fit, method = "separate", adjust.method="BH", + p.value=0.001) write.fit(fit, results = res, file = "...", digits=2, adjust="BH", sep="\t") In that condition I've got 1800 up and 1800 down probes (out from 8100) Decreasing p.value to 0.0001 gave me 800 up and 800 down. I would like to mention here, that quite a big part of obtained data is physiologically relevant in my experiment, and the nature of the experiment suggests big differential expression. Thanks in advance for any comments on this? Best wishes Vladimir

Hi, Paul Take a look on my previous posts: [BioC] Channel splitting problem [BioC] What to do with multiple probes? There are I pose some questions, unfortunately w/o firm answers yet, and describe my design briefly regards Vladimir Paul Lang wrote: > what is your experimental design / chip system? > > I am using single-colour cDNA arrays and stablilizing with vsn, and > would be interested to see how it works on other setups. > > best > > Paul Lang > > > -----Original Message----- > From: bioconductor-bounces at stat.math.ethz.ch on behalf of > krasikov at science.uva.nl > Sent: Thu 12/1/2005 5:58 AM > To: bioconductor at stat.math.ethz.ch > Subject: [BioC] How to validate normalization? > > > > > -----Original Message----- > From: bioconductor-bounces at stat.math.ethz.ch on behalf of > krasikov at science.uva.nl > Sent: Thu 12/1/2005 5:58 AM > To: bioconductor at stat.math.ethz.ch > Subject: [BioC] How to validate normalization? > > Dear all > > Here I post again the question about normalization > I'm sorry that this question might be obvious for statistician. > > The general question: > How to validate the normalization outcome? > Density plots? > I have tried "loees with aquantile" and "vsn" and outcome of the > decideTests is more or less the same - a lot of probes with differential > expression. > > Here below the code I used in limma: > > RG <- read.maimages(...) > ...assigning spotTypes > ...removing controlspots from the RG > RGb <- backgroundCorrect(RG,method="minimum") > MA <- normalizeWithinArrays(RGb, method="loess") > MA <- normalizeBetweenArrays(MA, method="Aquantile") > ...design > fit <- lmFit(MA, design) > ...contrast.matrix > fit <- contrasts.fit(fit, contrast.matrix) > fit <- eBayes(fit) > res <- decideTests(fit, method = "separate", adjust.method="BH", > + p.value=0.001) > write.fit(fit, results = res, file = "...", digits=2, adjust="BH", sep="\t") > > In that condition I've got 1800 up and 1800 down probes (out from 8100) > Decreasing p.value to 0.0001 gave me 800 up and 800 down. > > I would like to mention here, that quite a big part of obtained data > is physiologically relevant in my experiment, > and the nature of the experiment suggests big differential expression. > > Thanks in advance for any comments on this? > > Best wishes > Vladimir > > _______________________________________________ > Bioconductor mailing list > Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor > >