PCR Validation threshold in dChip normalized data
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Benjamin Otto ▴ 830
@benjamin-otto-1519
Last seen 8.2 years ago
Hi, Given a dataset for Affymetrix arrays normalized with mas5 or rma we usually made the experience that signals below 80 (6,3 in log2 format) are hard to validate with PCR. Can somebody tell me, how I can judge on dChip normalized data in a analog way? Where can I draw a threshold to tell, which signal has good chances to withstand a verification with with PCR or even on protein level? And which signals usually indicate a much to low expression level? Best regards, Benjamin ====================================== Benjamin Otto University Hospital Hamburg-Eppendorf Institute For Clinical Chemistry Martinistr. 52 D-20246 Hamburg Tel.: +49 40 42803 1908 Fax.: +49 40 42803 4971 ====================================== -- Pflichtangaben gem?? Gesetz ?ber elektronische Handelsregister und Genossenschaftsregister sowie das Unternehmensregister (EHUG): Universit?tsklinikum Hamburg-Eppendorf K?rperschaft des ?ffentlichen Rechts Gerichtsstand: Hamburg Vorstandsmitglieder: Prof. Dr. J?rg F. Debatin (Vorsitzender) Dr. Alexander Kirstein Ricarda Klein Prof. Dr. Dr. Uwe Koch-Gromus
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@sean-davis-490
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On Thu, Sep 4, 2008 at 8:01 AM, Benjamin Otto <b.otto at="" uke.uni-="" hamburg.de=""> wrote: > Hi, > > Given a dataset for Affymetrix arrays normalized with mas5 or rma we usually > made the experience that signals below 80 (6,3 in log2 format) are hard to > validate with PCR. > > Can somebody tell me, how I can judge on dChip normalized data in a analog > way? Where can I draw a threshold to tell, which signal has good chances to > withstand a verification with with PCR or even on protein level? And which > signals usually indicate a much to low expression level? I don't think this is an answerable question, exactly. See Rafael Irizarry's work on gene expression barcoding. http://www.ncbi.nlm.nih.gov/pubmed/17906632 In short, each probeset has a different threshold for expression, potentially. Also, keep in mind that PCR, while held out as a "gold standard" is not without its own biases. Finally, for proteins, all bets are off, as there are a number of highly relevant mechanisms for regulation of protein expression that occur after transcription. Not really an answer, but it is reality, I think. Sean
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Hi Sean, First, thanks for the quick reply! Second, I wouldn't expect a perfect cutoff between possible validation and junk. That would be more a question about judging the data signal range with some tolerance where I can expect the gene to be expressed at all and where the bets are, that I don't see much more than a lot of noise. Third, probably I was just a little bit too slow to attach my PS comment. What irritated and animated me to post the question was my observation of really negative values in a GEO dataset (GSE3446) which should only be normalized with dCHip without additional transformation. I never, and I must say I rarely used dChip for normalization ... still I never observed negative values in pure dChip normalization. And this dataset has a range from -11000 up to 17000. So that is where I just lost, or still don't have, the slightest feeling for what the dataset range tells me. It is not z-score normalized. The sd's are unequal 1. Probably something similar. But then, would you say: "No judgement possible anymore about "present/absent"-states because the normal states are already curated by some mean value?" Best regards, Benjamin -----Urspr?ngliche Nachricht----- Von: seandavi at gmail.com [mailto:seandavi at gmail.com] Im Auftrag von Sean Davis Gesendet: Thursday, September 04, 2008 2:34 PM An: Benjamin Otto Cc: bioconductor at stat.math.ethz.ch Betreff: Re: [BioC] PCR Validation threshold in dChip normalized data On Thu, Sep 4, 2008 at 8:01 AM, Benjamin Otto <b.otto at="" uke.uni-="" hamburg.de=""> wrote: > Hi, > > Given a dataset for Affymetrix arrays normalized with mas5 or rma we usually > made the experience that signals below 80 (6,3 in log2 format) are hard to > validate with PCR. > > Can somebody tell me, how I can judge on dChip normalized data in a analog > way? Where can I draw a threshold to tell, which signal has good chances to > withstand a verification with with PCR or even on protein level? And which > signals usually indicate a much to low expression level? I don't think this is an answerable question, exactly. See Rafael Irizarry's work on gene expression barcoding. http://www.ncbi.nlm.nih.gov/pubmed/17906632 In short, each probeset has a different threshold for expression, potentially. Also, keep in mind that PCR, while held out as a "gold standard" is not without its own biases. Finally, for proteins, all bets are off, as there are a number of highly relevant mechanisms for regulation of protein expression that occur after transcription. Not really an answer, but it is reality, I think. Sean -- Pflichtangaben gem?? Gesetz ?ber elektronische Handelsregister und Genossenschaftsregister sowie das Unternehmensregister (EHUG): Universit?tsklinikum Hamburg-Eppendorf K?rperschaft des ?ffentlichen Rechts Gerichtsstand: Hamburg Vorstandsmitglieder: Prof. Dr. J?rg F. Debatin (Vorsitzender) Dr. Alexander Kirstein Ricarda Klein Prof. Dr. Dr. Uwe Koch-Gromus
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Hi Benjamin, I agree that a single expression threshold is unlikely to work for all probesets, however if you would still like to find a single cutoff, then i'd suggest that you plot histograms or density plots of the data from each array to identify an approximate threshold at which 2 populations of probes separate into a clearly low, background set, and a higher set of expressed signals. This method works very well for gcRMA (the 2 populations are very distinct), moderately well for RMA (the 2 populations are somewhat distinct) and I have no idea for dChip. Caveat - your data probably needs to be on log scale, so you'll need to deal with the negative values in some appropriate way... add an offset? cheers, Mark ----------------------------------------------------- Mark Cowley, BSc (Bioinformatics)(Hons) Peter Wills Bioinformatics Centre Garvan Institute of Medical Research, Sydney, Australia ----------------------------------------------------- On 04/09/2008, at 10:45 PM, Benjamin Otto wrote: > Hi Sean, > > First, thanks for the quick reply! > > Second, I wouldn't expect a perfect cutoff between possible > validation and junk. That would be more a question about judging the > data signal range with some tolerance where I can expect the gene to > be expressed at all and where the bets are, that I don't see much > more than a lot of noise. > > Third, probably I was just a little bit too slow to attach my PS > comment. What irritated and animated me to post the question was my > observation of really negative values in a GEO dataset (GSE3446) > which should only be normalized with dCHip without additional > transformation. I never, and I must say I rarely used dChip for > normalization ... still I never observed negative values in pure > dChip normalization. And this dataset has a range from -11000 up to > 17000. So that is where I just lost, or still don't have, the > slightest feeling for what the dataset range tells me. > It is not z-score normalized. The sd's are unequal 1. Probably > something similar. But then, would you say: > > "No judgement possible anymore about "present/absent"-states because > the normal states are already curated by some mean value?" > > Best regards, > > Benjamin > > > > -----Urspr?ngliche Nachricht----- > Von: seandavi at gmail.com [mailto:seandavi at gmail.com] Im Auftrag von > Sean Davis > Gesendet: Thursday, September 04, 2008 2:34 PM > An: Benjamin Otto > Cc: bioconductor at stat.math.ethz.ch > Betreff: Re: [BioC] PCR Validation threshold in dChip normalized data > > On Thu, Sep 4, 2008 at 8:01 AM, Benjamin Otto <b.otto at="" uke.uni-="" hamburg.de=""> > wrote: >> Hi, >> >> Given a dataset for Affymetrix arrays normalized with mas5 or rma >> we usually >> made the experience that signals below 80 (6,3 in log2 format) are >> hard to >> validate with PCR. >> >> Can somebody tell me, how I can judge on dChip normalized data in a >> analog >> way? Where can I draw a threshold to tell, which signal has good >> chances to >> withstand a verification with with PCR or even on protein level? >> And which >> signals usually indicate a much to low expression level? > > I don't think this is an answerable question, exactly. See Rafael > Irizarry's work on gene expression barcoding. > > http://www.ncbi.nlm.nih.gov/pubmed/17906632 > > In short, each probeset has a different threshold for expression, > potentially. Also, keep in mind that PCR, while held out as a "gold > standard" is not without its own biases. Finally, for proteins, all > bets are off, as there are a number of highly relevant mechanisms for > regulation of protein expression that occur after transcription. > > Not really an answer, but it is reality, I think. > > Sean > > > > -- > Pflichtangaben gem?? Gesetz ?ber elektronische Handelsregister und > Genossenschaftsregister sowie das Unternehmensregister (EHUG): > > Universit?tsklinikum Hamburg-Eppendorf > K?rperschaft des ?ffentlichen Rechts > Gerichtsstand: Hamburg > > Vorstandsmitglieder: > Prof. Dr. J?rg F. Debatin (Vorsitzender) > Dr. Alexander Kirstein > Ricarda Klein > Prof. Dr. Dr. Uwe Koch-Gromus > _______________________________________________ > 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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Benjamin Otto ▴ 830
@benjamin-otto-1519
Last seen 8.2 years ago
A small PS to my posting: The dataset GSE3466 in GEO is dChip normalized according to the provided informations (!Sample_data_processing=dCHIP (PM-MM model) ) Now the table contains negative values up to -11053. I can't to have ever observed negative values after dChip Normalization. Any suggestions concerning this point? Thanks very much, Benjamin -----Urspr?ngliche Nachricht----- Von: bioconductor-bounces at stat.math.ethz.ch [mailto:bioconductor-bounces at stat.math.ethz.ch] Im Auftrag von Benjamin Otto Gesendet: Thursday, September 04, 2008 2:01 PM An: bioconductor at stat.math.ethz.ch Betreff: [BioC] PCR Validation threshold in dChip normalized data Hi, Given a dataset for Affymetrix arrays normalized with mas5 or rma we usually made the experience that signals below 80 (6,3 in log2 format) are hard to validate with PCR. Can somebody tell me, how I can judge on dChip normalized data in a analog way? Where can I draw a threshold to tell, which signal has good chances to withstand a verification with with PCR or even on protein level? And which signals usually indicate a much to low expression level? Best regards, Benjamin ====================================== Benjamin Otto University Hospital Hamburg-Eppendorf Institute For Clinical Chemistry Martinistr. 52 D-20246 Hamburg Tel.: +49 40 42803 1908 Fax.: +49 40 42803 4971 ====================================== -- Pflichtangaben gemd_ Gesetz |ber elektronische Handelsregister und Genossenschaftsregister sowie das Unternehmensregister (EHUG): Universitdtsklinikum Hamburg-Eppendorf Kvrperschaft des vffentlichen Rechts Gerichtsstand: Hamburg Vorstandsmitglieder: Prof. Dr. Jvrg F. Debatin (Vorsitzender) Dr. Alexander Kirstein Ricarda Klein Prof. Dr. Dr. Uwe Koch-Gromus -- Pflichtangaben gem?? Gesetz ?ber elektronische Handelsregister und Genossenschaftsregister sowie das Unternehmensregister (EHUG): Universit?tsklinikum Hamburg-Eppendorf K?rperschaft des ?ffentlichen Rechts Gerichtsstand: Hamburg Vorstandsmitglieder: Prof. Dr. J?rg F. Debatin (Vorsitzender) Dr. Alexander Kirstein Ricarda Klein Prof. Dr. Dr. Uwe Koch-Gromus
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