question about RMA normalized gene expression values
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@james-anderson-1641
Last seen 10.2 years ago
Hi, Do different arrays have identical distribution in probe set level after RMA normalization? Since RMA does quantile normalization in probe level, the distribution in probe level should be identical. Does the median polish summarization (which summarizes the expression value from probe level to probe set level) make the distribution different from each other? Thanks, -James [[alternative HTML version deleted]]
Normalization probe Normalization probe • 1.8k views
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@james-w-macdonald-5106
Last seen 4 hours ago
United States
Hi James, James Anderson wrote: > Hi, > > Do different arrays have identical distribution in probe set level after RMA normalization? Since RMA does quantile normalization in probe level, the distribution in probe level should be identical. Does the median polish summarization (which summarizes the expression value from probe level to probe set level) make the distribution different from each other? You can test this for yourself. biocLite("affydata") library(affydata) data(Dilution) hist(Dilution) a <- normalize(Dilution) hist(a) eset <- rma(Dilution) plotDensity(exprs(eset)) Best, Jim > > Thanks, > > -James > > > > > [[alternative HTML version deleted]] > > _______________________________________________ > 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 -- James W. MacDonald, M.S. Biostatistician Douglas Lab University of Michigan Department of Human Genetics 5912 Buhl 1241 E. Catherine St. Ann Arbor MI 48109-5618 734-615-7826 ********************************************************** Electronic Mail is not secure, may not be read every day, and should not be used for urgent or sensitive issues
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Hi James, Thanks for your reply. In the example you provided, the distribution is ALMOST identical, except in the region with large intensities. So I think the correct way should be that the distribution in probe set level from different arrays should be ALMOST identical, but theoretically, it could deviate from strictly identical, due to the median polish. The reason I asked this question is because I actually downloaded one dataset online which says it has been RMA normalized, but the distribution deviates from identical more than what I expect, that's why I am suspicious of the question: do different arrays have identical distribution in probe set level after RMA? Thanks again, -Jim --- On Wed, 1/20/10, James W. MacDonald <jmacdon@med.umich.edu> wrote: From: James W. MacDonald <jmacdon@med.umich.edu> Subject: Re: [BioC] question about RMA normalized gene expression values To: "James Anderson" <janderson_net@yahoo.com> Cc: bioconductor@stat.math.ethz.ch Date: Wednesday, January 20, 2010, 1:04 PM Hi James, James Anderson wrote: > Hi, > Do different arrays have identical distribution in probe set level after RMA normalization? Since RMA does quantile normalization in probe level, the distribution in probe level should be identical. Does the median polish summarization (which summarizes the expression value from probe level to probe set level) make the distribution different from each other? You can test this for yourself. biocLite("affydata") library(affydata) data(Dilution) hist(Dilution) a <- normalize(Dilution) hist(a) eset <- rma(Dilution) plotDensity(exprs(eset)) Best, Jim > > Thanks, > > -James > > > >           [[alternative HTML version deleted]] > > _______________________________________________ > Bioconductor mailing list > Bioconductor@stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor -- James W. MacDonald, M.S. Biostatistician Douglas Lab University of Michigan Department of Human Genetics 5912 Buhl 1241 E. Catherine St. Ann Arbor MI 48109-5618 734-615-7826 ********************************************************** Electronic Mail is not secure, may not be read every day, and should not be used for urgent or sensitive issues [[alternative HTML version deleted]]
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Hi, generally in RMA the individual probe distributions are identical, the probe-set distributions are usually only similar. You can verify this by sampling (w/o replacement) twice n/l vectors (i.e. probe sets) of length l from the same population of n values (i.e probes) and then using average/median/median polish whatever you like on the vectors and compare the distributions you get. Best Wishes, Bj?rn James Anderson wrote: > Hi James, > Thanks for your reply. In the example you provided, the distribution is ALMOST identical, except in the region with large intensities. So I think the correct way should be that the distribution in probe set level from different arrays should be ALMOST identical, but theoretically, it could deviate from strictly identical, due to the median polish. The reason I asked this question is because I actually downloaded one dataset online which says it has been RMA normalized, but the distribution deviates from identical more than what I expect, that's why I am suspicious of the question: do different arrays have identical distribution in probe set level after RMA? > Thanks again, > -Jim > > --- On Wed, 1/20/10, James W. MacDonald <jmacdon at="" med.umich.edu=""> wrote: > > From: James W. MacDonald <jmacdon at="" med.umich.edu=""> > Subject: Re: [BioC] question about RMA normalized gene expression values > To: "James Anderson" <janderson_net at="" yahoo.com=""> > Cc: bioconductor at stat.math.ethz.ch > Date: Wednesday, January 20, 2010, 1:04 PM > > Hi James, > > James Anderson wrote: >> Hi, >> Do different arrays have identical distribution in probe set level after RMA normalization? Since RMA does quantile normalization in probe level, the distribution in probe level should be identical. Does the median polish summarization (which summarizes the expression value from probe level to probe set level) make the distribution different from each other? > > You can test this for yourself. > > biocLite("affydata") > library(affydata) > data(Dilution) > hist(Dilution) > a <- normalize(Dilution) > hist(a) > eset <- rma(Dilution) > plotDensity(exprs(eset)) > > Best, > > Jim > > >> Thanks, >> >> -James >> >> >> >> [[alternative HTML version deleted]] >> >> _______________________________________________ >> 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 > > -- James W. MacDonald, M.S. > Biostatistician > Douglas Lab > University of Michigan > Department of Human Genetics > 5912 Buhl > 1241 E. Catherine St. > Ann Arbor MI 48109-5618 > 734-615-7826 > ********************************************************** > Electronic Mail is not secure, may not be read every day, and should not be used for urgent or sensitive issues > > > > > [[alternative HTML version deleted]] > -- -------------------------------------------------- Bj?rn Usadel, PhD Max Planck Institute of Molecular Plant Physiology AG Integrative Carbon Biology Am Muehlenberg 1 14476 Potsdam-Golm Tel.: +49 331 5678153 email usadel at mpimp-golm.mpg.de http://tinyurl.com/IntegrativeCarbonBiology
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hi James A, i've come across a situation where the probeset distributions can be quite different: say you have 10 arrays, and 3 were poor hybs, eg they were very bright (which can happen if the RNA is over-fragmented), then while the probe distributions will be identical because of the qnorm, the probeset distributions will differ quite dramatically following the median polish. If you're seeing quite distinct shapes in the distributions of RMA normalised data, i'd be doubting the quality of the original arrays. Can you get your hands on the CEL files? that'll tell you for sure. HTH mark ----------------------------------------------------- Mark Cowley, PhD Peter Wills Bioinformatics Centre Garvan Institute of Medical Research, Sydney, Australia ----------------------------------------------------- On 26/01/2010, at 5:59 AM, Bjoern Usadel wrote: > Hi, > > generally in RMA the individual probe distributions are identical, > the probe-set distributions are usually only similar. > > You can verify this by sampling (w/o replacement) twice n/l vectors > (i.e. probe sets) of length l from the same population of n values > (i.e probes) and then using average/median/median polish whatever > you like on the vectors and compare the distributions you get. > > Best Wishes, > Bj?rn > > James Anderson wrote: >> Hi James, >> Thanks for your reply. In the example you provided, the >> distribution is ALMOST identical, except in the region with large >> intensities. So I think the correct way should be that the >> distribution in probe set level from different arrays should be >> ALMOST identical, but theoretically, it could deviate from strictly >> identical, due to the median polish. The reason I asked this >> question is because I actually downloaded one dataset online which >> says it has been RMA normalized, but the distribution deviates from >> identical more than what I expect, that's why I am suspicious of >> the question: do different arrays have identical distribution in >> probe set level after RMA? Thanks again, >> -Jim >> --- On Wed, 1/20/10, James W. MacDonald <jmacdon at="" med.umich.edu=""> >> wrote: >> From: James W. MacDonald <jmacdon at="" med.umich.edu=""> >> Subject: Re: [BioC] question about RMA normalized gene expression >> values >> To: "James Anderson" <janderson_net at="" yahoo.com=""> >> Cc: bioconductor at stat.math.ethz.ch >> Date: Wednesday, January 20, 2010, 1:04 PM >> Hi James, >> James Anderson wrote: >>> Hi, Do different arrays have identical distribution in probe set >>> level after RMA normalization? Since RMA does quantile >>> normalization in probe level, the distribution in probe level >>> should be identical. Does the median polish summarization (which >>> summarizes the expression value from probe level to probe set >>> level) make the distribution different from each other? >> You can test this for yourself. >> biocLite("affydata") >> library(affydata) >> data(Dilution) >> hist(Dilution) >> a <- normalize(Dilution) >> hist(a) >> eset <- rma(Dilution) >> plotDensity(exprs(eset)) >> Best, >> Jim >>> Thanks, >>> >>> -James >>> >>> >>> >>> [[alternative HTML version deleted]] >>> >>> _______________________________________________ >>> 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 >> -- James W. MacDonald, M.S. >> Biostatistician >> Douglas Lab >> University of Michigan >> Department of Human Genetics >> 5912 Buhl >> 1241 E. Catherine St. >> Ann Arbor MI 48109-5618 >> 734-615-7826 >> ********************************************************** >> Electronic Mail is not secure, may not be read every day, and >> should not be used for urgent or sensitive issues >> [[alternative HTML version deleted]] > > -- > -------------------------------------------------- > Bj?rn Usadel, PhD > Max Planck Institute of Molecular Plant Physiology > AG Integrative Carbon Biology > Am Muehlenberg 1 > 14476 Potsdam-Golm > Tel.: +49 331 5678153 > email usadel at mpimp-golm.mpg.de > http://tinyurl.com/IntegrativeCarbonBiology > > _______________________________________________ > 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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