To add to the list of references, see also: Yamamoto, G.; Nannya, Y.; Kato, M.; Sanada, M.; Levine, R. L.; Kawamata, N.; Hangaishi, A.; Kurokawa, M.; Chiba, S.; Gilliland, D. G.; Koeffler, H. P. & Ogawa, S. Highly sensitive method for genomewide detection of allelic composition in nonpaired, primary tumor specimens by use of affymetrix single-nucleotide-polymorphism genotyping microarrays. Am J Hum Genet, 2007, 81, 114-126 PMID: 17564968.
/Henrik On Fri, Jun 26, 2009 at 10:29 AM, Lavinia<lavinia.gor...@mcri.edu.au> wrote: > > Thanks for your email Henrik. > >> When you say heterogeneous, do you mean that they contain a lot of CN >> aberrations or do you mean that they have different noise levels? > > Different noise levels. > >> >> If "not too many" things go on in your reference samples, then you >> should expect to get an improvement when you calculate the reference >> channel as the average over a larger and larger pool of samples. A >> few years ago I checked this on 500K data and I found a dramatic drop >> in SNRs when increasing from 5 to 10 to 20 reference samples and then >> i flattened out. >> >> However, if you look Nannya et al (2005), their CNAG method tries to >> identify a subset of reference samples that gives best SNRs. This is >> to say that you believe there is a set of reference samples that are >> more "normal" than others. An alternative argument, which may make >> even more sense is that there will always be some systematic effects >> remaining in the estimates and if you can locate a set of reference >> samples that have similar remaining effects as you test sample, they >> will cancel out better than if other reference samples where used. >> Note that this strategy uses different pools of references for each >> test sample. I know that the Broad Institute (Gaddy Getz, Scott >> Carter et al.) are doing something similar in the TCGA project and >> they say they get better SNRs. > > thank you very much for these pointers, I'll look into them. > I'll try and do something a bit more systematic and post the results. > > cheers > > Lavinia. > > > --~--~---------~--~----~------------~-------~--~----~ When reporting problems on aroma.affymetrix, make sure 1) to run the latest version of the package, 2) to report the output of sessionInfo() and traceback(), and 3) to post a complete code example. You received this message because you are subscribed to the Google Groups "aroma.affymetrix" group. To post to this group, send email to aroma-affymetrix@googlegroups.com To unsubscribe from this group, send email to aroma-affymetrix-unsubscr...@googlegroups.com For more options, visit this group at http://groups.google.com/group/aroma-affymetrix?hl=en -~----------~----~----~----~------~----~------~--~---
