Inf values using SAM (package=siggenes)
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@holger-schwender-344
Last seen 10.2 years ago
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 --
Annotation Survival Cancer genefilter affy limma maDB affyio Annotation Survival Cancer • 1.3k views
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