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

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

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 >