Question: Filtering before differential expression analysis of microarrays - New paper out
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gravatar for Steve Lianoglou
10.8 years ago by
Denali
Steve Lianoglou12k wrote:
Thanks, Jim! Multiplicity as in multiple testing makes sense, I wasn't sure if he was referring to something about probes appearing in multiple places or something within arrays, or across arrays, or something (which I was trying to parse into how that might be relevant here). Cheers, -steve On Jan 14, 2009, at 12:50 PM, James W. MacDonald wrote: > Hi Steve, > > The question wasn't really asked of me, but Gordon is likely in bed > right now ;-D > > Steve Lianoglou wrote: >> Hi Gordon, >> As someone who has been dealing more and more with raw data, I >> always appreciate detailed answers from the masters, such as the >> one you just wrote. Even after reading several of the published >> articles regarding these normalization practices, I always find >> these less formal emails quite helpful. >> That said, one point you mention isn't exactly clear to me, and I'm >> wondering if you could elaborate just a bit here: >>> Filtering non-expressed probes tends not be emphasised on this >>> list because users of this list are often sophisticated enough to >>> use variance stabilizing normalization methods such as rma, vsn, >>> normexp or vst. This means that low-expression filtering is done >>> more for multiplicity issues than for variance stabilization, and >>> therefore often doesn't make a huge difference. When using >>> earlier normalization methods such as MAS for Affy or local >>> background correction for two-color arrays, expression-filtering >>> is absolutely essential, because the normalized expression values >>> are so unstable at low intensity levels. >> When you say "... low-expression filtering is done more for >> multiplicity issues than for variance stabilization", what exactly >> do you mean by "multiplicity issues"? > > By multiplicity issues Gordon was referring to the multiple > comparisons problem. A p-value is an estimate of the probability of > a type 1 error, in which we say there is a difference when in fact > there isn't (a false positive). If we reject the null hypothesis at > an alpha level of 0.05, we are in essence taking a 5% chance of > being wrong. > > For one test this isn't a problem, but as you make more and more > tests simultaneously, you expect to see more and more false > positives (e.g, if you do 20 tests at an alpha of 0.05, and there > are really no differences for any of the tests, you still expect > about one of them to appear significant even though none are). > > There are lots of ways to adjust for multiple comparisons, but one > of the best things you can do is not make so many comparisons in the > first place, by filtering out data based on one or more criteria. > > Best, > > Jim >> Thanks, >> -steve >> -- >> Steve Lianoglou >> Graduate Student: Physiology, Biophysics and Systems Biology >> Weill Medical College of Cornell University >> http://cbio.mskcc.org/~lianos >> _______________________________________________ >> 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 > Hildebrandt Lab > 8220D MSRB III > 1150 W. Medical Center Drive > Ann Arbor MI 48109-5646 > 734-936-8662 -- Steve Lianoglou Graduate Student: Physiology, Biophysics and Systems Biology Weill Medical College of Cornell University http://cbio.mskcc.org/~lianos
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