Hi, Just a quick question about low expression levels on Affy systems - I hope it's not too off-topic; it is about normalisation and data analysis... I've heard a lot of people advocating that it's a good idea to perform an initial filtering on either Present Marginal or Absent calls, or on gene-expression levels (so that only genes with an expression > 40, say, after scaling to a TGT of 100 using the MAS5.0 algorithm, are part of the further analysis). Firstly, am I right in thinking that this is to eliminate data that are too close to the background noise level of the system. I wanted to canvas opinion as to whether people feel we need to do this if we have replicates and are using statistical tests - rather than just fold-changes - to identify 'interesting' genes. Does the statistical testing do this job for us? Crispin -------------------------------------------------------- This email is confidential and intended solely for the use of th... {{dropped}}

if you look at an MA plot of expression values obtained from MAS 5.0 on two replicate arrays you will see that for genes with low expression you get very large fold changes. when using MAS 5.0, if you dont filter genes with observed low intensity you will get lots of false positives (some call this the fish tail effect). however, by filtering genes in such a way you run the risk of creating various false negatives. if you use, for example, dChip or RMA this fish tail problem is not nearly as bad. if you normalize with vsn it pretty much goes away completely. thus, using RMA, or dChip, and/or vsn, etc..., the P/A calls are not essential for avoiding many false positives. some of this is discussed here: http://nar.oupjournals.org/cgi/content/full/31/4/e15?ijkey=EAz2cYYbEWQ rE&keytype=ref&siteid=nar hope this helps, rafael On Fri, 30 May 2003, Crispin Miller wrote: > Hi, > Just a quick question about low expression levels on Affy systems - I hope it's not too off-topic; it is about normalisation and data analysis... > I've heard a lot of people advocating that it's a good idea to perform an initial filtering on either Present Marginal or Absent calls, or on gene-expression levels (so that only genes with an expression > 40, say, after scaling to a TGT of 100 using the MAS5.0 algorithm, are part of the further analysis). Firstly, am I right in thinking that this is to eliminate data that are too close to the background noise level of the system. > > I wanted to canvas opinion as to whether people feel we need to do this if we have replicates and are using statistical tests - rather than just fold-changes - to identify 'interesting' genes. Does the statistical testing do this job for us? > > Crispin > > -------------------------------------------------------- > > > This email is confidential and intended solely for the use of th... {{dropped}} > > _______________________________________________ > Bioconductor mailing list > Bioconductor@stat.math.ethz.ch > https://www.stat.math.ethz.ch/mailman/listinfo/bioconductor >