Hi all- We are investigating SAM and EBAM analysis for our data and run into a problem that reading the archives has not helped any. We performed SAM on a data set and noticed the permutations randomly selected 100 to generate the results. We were curious what that meant and if had anything similar to iterations in SAS (more usually is better). So we tried more and discovered that it was not better necessarly to us (increased FDR values for same deltas). And since we do not exactly understand what permutations exactly are doing in this case we wanted to check with the experts on their opinion if it should be changed are left at the default of 100. Thanks for the time and energy Louisa "If we knew what we were doing, it wouldn't be called Research." - Albert Einstein Louisa Rispoli, Ph.D. Reproductive Physiology Department of Animal Science University of Tennessee, Knoxville A105 Johnson Animal Research and Teaching Unit 1750 Alcoa Highway Knoxville, TN 37920 phone:(865) 946-1874 fax:(865) 946-1010 email: lrispoli at utk.edu > library(siggenes) Loading required package: multtest Loading required package: survival Loading required package: splines > trt <- pData(Poly.rma.filter)$Treatment > trt [1] "Ctrl" "Ctrl" "Ctrl" "Ctrl" "Ctrl" "Ctrl" "Ctrl" "Ctrl" "HS" "HS" "HS" "HS" "HS" "HS" "HS" "HS" > clPoly <- ifelse(trt=="Ctrl", 0, 1) > clPoly [1] 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 > dataPoly.rma <- exprs(Poly.rma.filter) > sam.out <-sam(dataPoly.rma, clPoly, rand=123456) We're doing 12870 complete permutations and randomly select 100 of them. > sam.out SAM Analysis for the Two-Class Unpaired Case Assuming Unequal Variances Delta p0 False Called FDR 1 0.1 0.666 7190.38 8620 0.556 2 0.2 0.666 6265.49 7932 0.526 3 0.3 0.666 2303.43 3891 0.394 4 0.4 0.666 299.36 660 0.302 5 0.5 0.666 90.83 246 0.246 6 0.6 0.666 34.53 117 0.197 7 0.7 0.666 10.92 44 0.165 8 0.8 0.666 1.06 6 0.118 9 0.9 0.666 0 0 0 10 1.0 0.666 0 0 0 > sam.out1 <- sam(dataPoly.rma, clPoly, rand=123456, B=1000) We're doing 12870 complete permutations and randomly select 1000 of them. > sam.out1 SAM Analysis for the Two-Class Unpaired Case Assuming Unequal Variances Delta p0 False Called FDR 1 0.1 0.675 7371.715 8705 0.571 2 0.2 0.675 5928.818 7584 0.527 3 0.3 0.675 1275.681 2233 0.385 4 0.4 0.675 255.636 549 0.314 5 0.5 0.675 87.618 228 0.259 6 0.6 0.675 37.511 116 0.218 7 0.7 0.675 12.686 44 0.195 8 0.8 0.675 2.287 8 0.193 9 0.9 0.675 0 0 0 10 1.0 0.675 0 0 0 > sessionInfo() R version 2.7.1 (2008-06-23) 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 datasets methods base other attached packages: [1] siggenes_1.14.0 multtest_1.20.0 affycoretools_1.12.0 annaffy_1.12.1 KEGG.db_2.2.0 biomaRt_1.14.0 [7] RCurl_0.9-3 GOstats_2.6.0 Category_2.6.0 RBGL_1.16.0 annotate_1.18.0 xtable_1.5-2 [13] GO.db_2.2.0 AnnotationDbi_1.2.2 RSQLite_0.6-9 DBI_0.2-4 graph_1.18.1 limma_2.14.5 [19] bovinecdf_2.2.0 simpleaffy_2.16.0 gcrma_2.12.1 matchprobes_1.12.0 genefilter_1.20.0 survival_2.34-1 [25] affy_1.18.2 preprocessCore_1.2.0 affyio_1.8.0 Biobase_2.0.1 loaded via a namespace (and not attached): [1] cluster_1.11.11 XML_1.95-3