Hello group, I created an Expression Set as the following: Expression Set (exprSet) with 4986 genes 12 samples phenoData object with 2 variables and 12 cases varLabels cov1: Genotype cov2: Treatment then I run the Rondom Forest: rf <- randomForest(Xm, trainY, ntree = 2000, mtry = 70, importance = TRUE) var.imp.plot(rf, n.var = 30) The problem is when I chose different mtry, such as mtry = 70, mtry = 65, and mtry = 60, I got totally different gene lists with importance. Really get confusing. Any suggestions? Thank you. Xin LIU This e-mail is from ArraGen Ltd\ \ The e-mail and any files ...{{dropped}}

The problem is that the importance measure in randomForest is based on random permutations of the data, so even with the same mtry, multiple runs of randomForest will give you different importance measures, and thus rankings. This is especially true for small number of samples. People have tried using 10000-30000 or even more trees to get a more stable ranking of importance measures in RF. HTH, Andy > From: Liu, Xin > > Hello group, > > I created an Expression Set as the following: > > Expression Set (exprSet) with > 4986 genes > 12 samples > phenoData object with 2 variables and 12 cases > varLabels > cov1: Genotype > cov2: Treatment > > then I run the Rondom Forest: > rf <- randomForest(Xm, trainY, ntree = 2000, mtry = 70, > importance = TRUE) > var.imp.plot(rf, n.var = 30) > > The problem is when I chose different mtry, such as mtry = > 70, mtry = 65, and mtry = 60, I got totally different gene > lists with importance. Really get confusing. > > Any suggestions? Thank you. > > Xin LIU > > > > This e-mail is from ArraGen Ltd\ \ The e-mail and any files > ...{{dropped}} > > _______________________________________________ > Bioconductor mailing list > Bioconductor@stat.math.ethz.ch > https://www.stat.math.ethz.ch/mailman/listinfo/bioconductor > >