Yes that makes perfect sense now. I thought this might be the case, but the additional filtering (by having Entrez id for example) meant that I didn't have half the number of initial probesets, which threw me a little. Thanks and regards, Jim James W. MacDonald wrote: > Hi James, > > james perkins wrote: >> Hi James >> >> I meant when we have filterByQuantile as TRUE. In this case it seems >> to behave differently, and I can't figure out why, and I don't want >> to guess! > > OK. That's a different question. The details section of the help page > explains this: > > Note that by default the numerical-filter cutoff is interpreted as > a quantile, so leaving the default values intact would filter out > 50% of the genes remaining at this stage. If you prefer to set the > cutoff at some absolute threshold, change the value of > 'varByQuantile' to 'FALSE', and modify 'var.cutoff' accordingly. > > And looking at the code should help further: > > > if (var.filter) { > esetIqr <- apply(exprs(eset), 1, var.func) > if (filterByQuantile) { > if (0 < var.cutoff && var.cutoff < 1) { > var.cutoff = quantile(esetIqr, var.cutoff) > } > else stop("Cutoff Quantile has to be between 0 and 1.") > } > selected <- esetIqr > var.cutoff > > So if you leave varByQuantile = TRUE then after you do the > annotation-based filtering (GO, Entrez Gene, AFFX probesets, > duplicates), you will take what remains and filter out 50%. > > Does that help? > > Best, > > Jim > > >> >> Regards, >> >> Jim >> >> James W. MacDonald wrote: >>> Hi James, >>> >>> james perkins wrote: >>>> Hi, >>>> >>>> I am finding the nsFilter IQR cutoff somewhat confusing. >>>> >>>> It says it is using IQR with a default cutoff of 0.5. >>>> >>>> This gives the impression that if you line up the data and take the >>>> value between the 0.25 and 0.75 quantiles, you would keep the >>>> probeset if this value was < 0.5 >>>> >>>> However this is not the case, so I would like to know how exactly >>>> does this work? >>> >>> Actually it _is_ the case - perhaps you misunderstand something. >>> >>> First, get all probesets with an IQR > 0.5 >>> > T1 <- apply(exprs(sample.ExpressionSet), 1, IQR) > 0.5 >>> >>> Now do the same using nsFilter() >>> > T2 <- nsFilter(sample.ExpressionSet, FALSE, filterByQuantile = >>> FALSE, feature.exclude="", remove.dupEntrez = FALSE) >>> >>> Are they the same? >>> > all.equal(featureNames(sample.ExpressionSet)[T1], >>> featureNames(T2$eset)) >>> [1] TRUE >>> >>> Best, >>> >>> Jim >>> >>> >>> >>>> >>>> Regards, >>>> >>>> James >>>> >>>> _______________________________________________ >>>> 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 >>> >