Dear list members, I've been looking at the distribution plots of pm intensities and the corresponding expression values calculated by gcrma for a certain data set. Now I'm wondering what the best interpretation of these plots would be, because the former looks quite usual while the latter seems quite "unfamiliar" to me (nearly like a bimodal distribution, a jpg file should be attached). The data measured is simply the expression for certain mouse tumor tissues. Can anybody explain why after the background correction and normalisation I get this distribution shape? log(PM) density: | * | * * | * * | * * | * * | * * |* * | *********** +------------------------------- gcrma-expression values: | * | * * | * * | * * | * * | * * * * * |* **** * | *** +---------------------------------- -- Benjamin Otto Universitaetsklinikum Eppendorf Hamburg Institut fuer Klinische Chemie Martinistrasse 52 20246 Hamburg

There was just a lengthy exchange on this subject earlier this week - check the Bioconductor Archives https://stat.ethz.ch/pipermail/bioconductor/ for "RMA biomodality" Jenny At 08:13 AM 6/9/2006, Michal Okoniewski wrote: >Benjamin, > >And what do you get when you use standard RMA instead of GCRMA? >I have run GCRMA on my data and see similar pattern - the second peak >is small, but is there. In general GCRMA seems to result in much more >small values - RMA has a bit "nicer" distribution. > >Once I've run a distribution of correlation of all probesets against >all. >For MAS and RMA I got what I expected - almost normal distribution, >slightly shifted towards positive r. For GCRMA the distribution >had not really normal shape (almost symmetric convex function with the >maximum roughly in the same place as RMA - close to 0). From that time >on, I believe that GCRMA may impose some artifacts onto data... > >Cheers, >Michal > >-----Original Message----- >From: bioconductor-bounces at stat.math.ethz.ch >[mailto:bioconductor-bounces at stat.math.ethz.ch] On Behalf Of botto >Sent: 08 June 2006 13:24 >To: bioconductor at stat.math.ethz.ch >Subject: [BioC] unnormalised vs normalised distribution > >Dear list members, > >I've been looking at the distribution plots of pm intensities and the >corresponding expression values calculated by gcrma for a certain data >set. Now I'm wondering what the best interpretation of these plots would >be, because the former looks quite usual while the latter seems quite >"unfamiliar" to me (nearly like a bimodal distribution, a jpg file >should be attached). The data measured is simply the expression for >certain mouse tumor tissues. Can anybody explain why after the >background correction and normalisation I get this distribution shape? > >log(PM) density: > >| * >| * * >| * * >| * * >| * * >| * * >|* * >| *********** >+------------------------------- > >gcrma-expression values: > >| * >| * * >| * * >| * * >| * * >| * * * * * >|* **** * >| *** >+---------------------------------- > > > > > >-- >Benjamin Otto >Universitaetsklinikum Eppendorf Hamburg >Institut fuer Klinische Chemie >Martinistrasse 52 >20246 Hamburg > >-------------------------------------------------------- > > >This email is confidential and intended solely for the use o...{{dropped}} > >_______________________________________________ >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 Jenny Drnevich, Ph.D. Functional Genomics Bioinformatics Specialist W.M. Keck Center for Comparative and Functional Genomics Roy J. Carver Biotechnology Center University of Illinois, Urbana-Champaign 330 ERML 1201 W. Gregory Dr. Urbana, IL 61801 USA ph: 217-244-7355 fax: 217-265-5066 e-mail: drnevich at uiuc.edu

Benjamin, And what do you get when you use standard RMA instead of GCRMA? I have run GCRMA on my data and see similar pattern - the second peak is small, but is there. In general GCRMA seems to result in much more small values - RMA has a bit "nicer" distribution. Once I've run a distribution of correlation of all probesets against all. For MAS and RMA I got what I expected - almost normal distribution, slightly shifted towards positive r. For GCRMA the distribution had not really normal shape (almost symmetric convex function with the maximum roughly in the same place as RMA - close to 0). From that time on, I believe that GCRMA may impose some artifacts onto data... Cheers, Michal -----Original Message----- From: bioconductor-bounces@stat.math.ethz.ch [mailto:bioconductor-bounces at stat.math.ethz.ch] On Behalf Of botto Sent: 08 June 2006 13:24 To: bioconductor at stat.math.ethz.ch Subject: [BioC] unnormalised vs normalised distribution Dear list members, I've been looking at the distribution plots of pm intensities and the corresponding expression values calculated by gcrma for a certain data set. Now I'm wondering what the best interpretation of these plots would be, because the former looks quite usual while the latter seems quite "unfamiliar" to me (nearly like a bimodal distribution, a jpg file should be attached). The data measured is simply the expression for certain mouse tumor tissues. Can anybody explain why after the background correction and normalisation I get this distribution shape? log(PM) density: | * | * * | * * | * * | * * | * * |* * | *********** +------------------------------- gcrma-expression values: | * | * * | * * | * * | * * | * * * * * |* **** * | *** +---------------------------------- -- Benjamin Otto Universitaetsklinikum Eppendorf Hamburg Institut fuer Klinische Chemie Martinistrasse 52 20246 Hamburg -------------------------------------------------------- This email is confidential and intended solely for the use o...{{dropped}}