Question: RMA and quantile normalisation
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gravatar for Arne.Muller@aventis.com
15.6 years ago by
Arne.Muller@aventis.com620 wrote:
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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ADD COMMENTlink modified 15.6 years ago by Francois Collin50 • written 15.6 years ago by Arne.Muller@aventis.com620
Answer: RMA and quantile normalisation
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gravatar for Arne.Muller@aventis.com
15.6 years ago by
Arne.Muller@aventis.com620 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
ADD COMMENTlink written 15.6 years ago by Arne.Muller@aventis.com620
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 >
ADD REPLYlink written 15.6 years ago by mai98ftu@studserv.uni-leipzig.de140
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 >
ADD REPLYlink written 15.6 years ago by Rafael A. Irizarry2.3k
Answer: RMA and quantile normalisation
0
gravatar for Francois Collin
15.6 years ago by
Francois Collin50 wrote:
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 >
ADD COMMENTlink written 15.6 years ago by Francois Collin50
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