Expression levels of individual affymetrix probes
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@johnstone-alice-2290
Last seen 9.6 years ago
Hi, Is it possible to apply the transformations used on the probesets to individual probe expression? Such as RMA and fitting to a lm to obtain your list of probesets of interest. Then potentially looking at individual probe expression levels across samples for all 11 probes of that probeset? (but normalized across samples to enable greater accuracy of the eyeball-o-meter) The reason behind this pondering is to investigate further transcript variant effects, and also to better match qpcr investigations to the location of the probe which shows the greatest change... Cheers, Alice P Think before you print This e-mail transmission and any attachments that accompany it may contain information that is privileged, confidential or otherwise exempt from disclosure under applicable law and is intended solely for the use of the individual(s) to whom it was intended to be addressed. If you have received this e-mail by mistake, or you are not the intended recipient, any disclosure, dissemination, distribution, copying or other use or retention of this communication or its substance is prohibited. If you have received this communication in error, please immediately reply to the author via e-mail that you received this message by mistake and also permanently delete the original and all copies of this e-mail and any attachments from your computer. Thank you.
qPCR probe qPCR probe • 732 views
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@james-w-macdonald-5106
Last seen 1 hour ago
United States
Hi Alice, Johnstone, Alice wrote: > Hi, > Is it possible to apply the transformations used on the probesets to > individual probe expression? Such as RMA and fitting to a lm to obtain > your list of probesets of interest. Then potentially looking at > individual probe expression levels across samples for all 11 probes of > that probeset? (but normalized across samples to enable greater > accuracy of the eyeball-o-meter) > The reason behind this pondering is to investigate further transcript > variant effects, and also to better match qpcr investigations to the > location of the probe which shows the greatest change... If I understand you correctly, you want to go back to the background-corrected, normalized pm data for certain probesets to see the contribution of individual probes. This shouldn't be difficult. dat <- ReadAffy() dat <- bg.correct.rma(dat) ## background corrected dat <- normalize.AffyBatch.quantiles(dat) Now you can look at individual probesets using pm(dat, 'a.probeset.id') Best, Jim > Cheers, > Alice > > > P Think before you print > This e-mail transmission and any attachments that accompany it may contain information that is privileged, confidential or otherwise exempt from disclosure under applicable law and is intended solely for the use of the individual(s) to whom it was intended to be addressed. > If you have received this e-mail by mistake, or you are not the intended recipient, any disclosure, dissemination, distribution, copying or other use or retention of this communication or its substance is prohibited. If you have received this communication in error, please immediately reply to the author via e-mail that you received this message by mistake and also permanently delete the original and all copies of this e-mail and any attachments from your computer. Thank you. > > _______________________________________________ > 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 Affymetrix and cDNA Microarray Core University of Michigan Cancer Center 1500 E. Medical Center Drive 7410 CCGC Ann Arbor MI 48109 734-647-5623
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The summarization step of RMA (which is a core part of the algorithm) does not make sense unless you actually have several probes to summarize. Kasper On Oct 29, 2007, at 6:44 AM, James W. MacDonald wrote: > Hi Alice, > > Johnstone, Alice wrote: >> Hi, >> Is it possible to apply the transformations used on the probesets to >> individual probe expression? Such as RMA and fitting to a lm to >> obtain >> your list of probesets of interest. Then potentially looking at >> individual probe expression levels across samples for all 11 >> probes of >> that probeset? (but normalized across samples to enable greater >> accuracy of the eyeball-o-meter) >> The reason behind this pondering is to investigate further transcript >> variant effects, and also to better match qpcr investigations to the >> location of the probe which shows the greatest change... > > If I understand you correctly, you want to go back to the > background-corrected, normalized pm data for certain probesets to see > the contribution of individual probes. > > This shouldn't be difficult. > > dat <- ReadAffy() > dat <- bg.correct.rma(dat) ## background corrected > dat <- normalize.AffyBatch.quantiles(dat) > > Now you can look at individual probesets using > > pm(dat, 'a.probeset.id') > > Best, > > Jim > > >> Cheers, >> Alice >> >> >> P Think before you print >> This e-mail transmission and any attachments that accompany it may >> contain information that is privileged, confidential or otherwise >> exempt from disclosure under applicable law and is intended solely >> for the use of the individual(s) to whom it was intended to be >> addressed. >> If you have received this e-mail by mistake, or you are not the >> intended recipient, any disclosure, dissemination, distribution, >> copying or other use or retention of this communication or its >> substance is prohibited. If you have received this communication >> in error, please immediately reply to the author via e-mail that >> you received this message by mistake and also permanently delete >> the original and all copies of this e-mail and any attachments >> from your computer. Thank you. >> >> _______________________________________________ >> 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 > Affymetrix and cDNA Microarray Core > University of Michigan Cancer Center > 1500 E. Medical Center Drive > 7410 CCGC > Ann Arbor MI 48109 > 734-647-5623 > > _______________________________________________ > 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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