RMA and quantile normalisation
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@arnemulleraventiscom-466
Last seen 10.2 years ago
Hi All, somehow from the back of my mind I think that this was already discussed here aoms time ago, but I cannot find the postings, os please excuse if this is a duplication ... . When processing some chips with RMA background correction, crooss chip quantile normalisation and "pmonly" probe set summary, the distribution of the intensities per chip is not normal. I haven't tried -- Arne Muller, Ph.D. Toxicogenomics, Aventis Pharma arne dot muller domain=aventis com
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@arnemulleraventiscom-466
Last seen 10.2 years ago
Sorry, my last posting was incomplete (slipped over the keyboard ...). I meant that I haven't explored other methods yet, but since the RMA values are log2, I thought that I'd get something close to a normal distribution. Comapred to a normal distribution I get many low intensity probe sets. The values are generated like this: eset.rma <- expresso(cel, bgcorrect.method="rma", normalize.method="quantiles", pmcorrect.method="pmonly", summary.method="medianpolish") then: hist(exprs(eset.rma[,10])) kind regards, Arne -- Arne Muller, Ph.D. Toxicogenomics, Aventis Pharma arne dot muller domain=aventis com
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Arne, I'd be very surprised if you'd observe a normal distribution. You should not rely on this assumption! I haven't seen any data yet that looked like normal after RMA. However, I'm not quite sure what a more appropriate distribution would be. Johannes Quoting Arne.Muller@aventis.com: > Sorry, my last posting was incomplete (slipped over the keyboard ...). > > I meant that I haven't explored other methods yet, but since the RMA > values > are log2, I thought that I'd get something close to a normal > distribution. > Comapred to a normal distribution I get many low intensity probe sets. > > The values are generated like this: > > eset.rma <- expresso(cel, bgcorrect.method="rma", > normalize.method="quantiles", pmcorrect.method="pmonly", > summary.method="medianpolish") > > then: > hist(exprs(eset.rma[,10])) > > kind regards, > > Arne > > -- > Arne Muller, Ph.D. > Toxicogenomics, Aventis Pharma > arne dot muller domain=aventis com > > _______________________________________________ > Bioconductor mailing list > Bioconductor@stat.math.ethz.ch > https://www.stat.math.ethz.ch/mailman/listinfo/bioconductor >
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from the data i have looked at they are in general not normal. i would not expect them to be. there are various reasons to take the log. but to make the within chip distibution normal is not one of them. what is close to normal is the distribution of RMA expression measures of a particular gene across many chips. in your example: hist(exprs(eset.rma[10,]) but you'll need 30 or more arrays to see it. On Wed, 3 Mar 2004 Arne.Muller@aventis.com wrote: > Sorry, my last posting was incomplete (slipped over the keyboard ...). > > I meant that I haven't explored other methods yet, but since the RMA values > are log2, I thought that I'd get something close to a normal distribution. > Comapred to a normal distribution I get many low intensity probe sets. > > The values are generated like this: > > eset.rma <- expresso(cel, bgcorrect.method="rma", > normalize.method="quantiles", pmcorrect.method="pmonly", > summary.method="medianpolish") > > then: > hist(exprs(eset.rma[,10])) > > kind regards, > > Arne > > -- > Arne Muller, Ph.D. > Toxicogenomics, Aventis Pharma > arne dot muller domain=aventis com > > _______________________________________________ > Bioconductor mailing list > Bioconductor@stat.math.ethz.ch > https://www.stat.math.ethz.ch/mailman/listinfo/bioconductor >
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@francois-collin-657
Last seen 10.2 years ago
Anne, There is no reason why the distribution of expression values across a collection of genes should be normal. A given gene may have a normal distribution across samples depending on the selection of samples. If there is structure in the sample set (treatment groups, etc), then normality of errors around the main effects may be a reasonable assumption. If your analysis assumes normality for the distribution of expression values for a given sample across all genes, you may want to compare your results with those obtained from an analysis that doesn't make this assumption. Models are fitted to background corrected, normalized probe intensity data for each probe set separately. At that level, the distribution of residuals is not inconsistent with a contaminated normal error distribution for which a robust estimation procedure as used in RMA makes sense. -francois ----- Original Message ----- From: <arne.muller@aventis.com> To: <bioconductor@stat.math.ethz.ch> Sent: Wednesday, March 03, 2004 4:16 AM Subject: [BioC] RMA and quantile normalisation > Sorry, my last posting was incomplete (slipped over the keyboard ...). > > I meant that I haven't explored other methods yet, but since the RMA values > are log2, I thought that I'd get something close to a normal distribution. > Comapred to a normal distribution I get many low intensity probe sets. > > The values are generated like this: > > eset.rma <- expresso(cel, bgcorrect.method="rma", > normalize.method="quantiles", pmcorrect.method="pmonly", > summary.method="medianpolish") > > then: > hist(exprs(eset.rma[,10])) > > kind regards, > > Arne > > -- > Arne Muller, Ph.D. > Toxicogenomics, Aventis Pharma > arne dot muller domain=aventis com > > _______________________________________________ > Bioconductor mailing list > Bioconductor@stat.math.ethz.ch > https://www.stat.math.ethz.ch/mailman/listinfo/bioconductor >
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