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