Dear Julian, the only values that are infinitive are the values of cutlow and cutup, where cutup is the smallest value of the test statistic for a gene to be called differentially expressed if its test value is larger than zero, and cutup is the largest test score less than zero for a gene to be called differentially expressed. If none of the genes is called differentially expressed by SAM than cutlow and cutup are set to -Inf and Inf, respectively. This has both practical and theoretical reasons. Best, Holger -------- Original-Nachricht -------- > Datum: Fri, 28 Dec 2007 11:43:33 +0800 (SGT) > Von: Julian Lee <julian at="" omniarray.com=""> > An: bioconductor at stat.math.ethz.ch > Betreff: [BioC] Inf values using SAM (package=siggenes) > Dear All, > > I'm having some problems trying to find differentially expressed genes > using SAM. I have the SAM for excel version but that unfortunately is limited > to only 500 genes. > > >from the vignette, i'm supplying the sam function, two important > arguments, data and cl. > > > data > ExpressionSet (storageMode: lockedEnvironment) > assayData: 16304 features, 65 samples > element names: exprs > phenoData > rowNames: D02_2nd, D02_3rd, ..., D31_BL (65 total) > varLabels and varMetadata: > Patient_ID: Patient's ID > Patient_Initials: Patient's Initials > ...: ... > 25_at_cycle1: 25% reduction from baseline at cycle1 > (17 total) > featureData > rowNames: 1007_s_at, 1053_at, ..., AFFX-r2-Ec-bioD-5_at (16304 total) > varLabels and varMetadata: none > experimentData: use 'experimentData(object)' > Annotation [1] "hgu133plus2" > > >cl > [1] 1 1 0 1 1 0 1 1 0 1 0 1 1 1 1 1 0 1 1 0 1 1 0 1 1 0 1 1 0 1 1 1 0 1 1 > 0 1 1 > [39] 1 1 0 1 1 0 1 1 0 1 1 1 0 1 0 1 1 0 1 1 0 1 0 1 0 1 0 > > data was pre-processed using the rma function, followed by genefiltering > > >data<-rma(U133PLUS2 CELFILES) > >library(genefilter) > >f1<-pOverA(0.25,log2(100)) > >f2<-function(x) (IQR(x)>0.5) > >ff<-filterfun(f1,f2) > >data<-data[genefilter(data,ff),] > > ##56,000 genes reduced to 16304 genes > > >library(siggenes) > > >sam.out<-sam(exprs(data),cl,rand=1234) > >sam.out > SAM Analysis for the Two-Class Unpaired Case Assuming Unequal Variances > > Delta p0 False Called FDR > 1 0.1 1 0 0 0 > 2 0.2 1 0 0 0 > 3 0.3 1 0 0 0 > 4 0.4 1 0 0 0 > 5 0.5 1 0 0 0 > 6 0.6 1 0 0 0 > 7 0.7 1 0 0 0 > 8 0.8 1 0 0 0 > 9 0.9 1 0 0 0 > 10 1.0 1 0 0 0 > > >summary(sam.out) > SAM Analysis for the Two-Class Unpaired Case Assuming Unequal Variances > > s0 = 0.0735 (The 0 % quantile of the s values.) > > Number of permutations: 100 > > MEAN number of falsely called variables is computed. > > Delta p0 False Called FDR cutlow cutup j2 j1 > 1 0.1 1 0 0 0 -Inf Inf 0 16305 > 2 0.2 1 0 0 0 -Inf Inf 0 16305 > 3 0.3 1 0 0 0 -Inf Inf 0 16305 > 4 0.4 1 0 0 0 -Inf Inf 0 16305 > 5 0.5 1 0 0 0 -Inf Inf 0 16305 > 6 0.6 1 0 0 0 -Inf Inf 0 16305 > 7 0.7 1 0 0 0 -Inf Inf 0 16305 > 8 0.8 1 0 0 0 -Inf Inf 0 16305 > 9 0.9 1 0 0 0 -Inf Inf 0 16305 > 10 1.0 1 0 0 0 -Inf Inf 0 16305 > > I'm not too sure why i'm getting Infinity values. This is my first time > using SAM on bioconductor. > > Thank you > > regards > > Julian Lee > National Cancer Center Singapore > > >sessionInfo() > R version 2.5.1 (2007-06-27) > i386-pc-mingw32 > > locale: > LC_COLLATE=English_United States.1252;LC_CTYPE=English_United > States.1252;LC_MONETARY=English_United States.1252;LC_NUMERIC=C;LC_TIME=English_United > States.1252 > > attached base packages: > [1] "splines" "tools" "stats" "graphics" "grDevices" "utils" > [7] "datasets" "methods" "base" > > other attached packages: > genefilter maDB limma affy affyio siggenes > multtest > "1.14.1" "1.8.0" "2.10.5" "1.14.2" "1.4.1" "1.10.1" > "1.16.1" > survival Biobase > "2.32" "1.14.1" > > _______________________________________________ > 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 --