Hello list and Evarist, I am using the phenoTest package in order to perform gene set enrichment analysis but I am getting some weird(I think) errors. What I want to do is find the statistically significant enriched GO categories and KEGG pathways of the upregulated, downregulated and differentially expressed genes. I was able to find the enriched GO and KEGG for the upregulated and downregulated genes using these calls of gsea: gseaGOResults <- gsea.go(newDiffExpr, ontologies = 'BP', p.adjust.method='BH'); gseaKEGGResults <- gsea.kegg(newDiffExpr, p.adjust.method='BH'); But when I do the same call with the parameter absVals set to TRUE(to find the enriched GO and KEGG based for the differentially expressed genes), for example I do this like this for the KEGG pathways: gseaKEGGResults <- gsea.kegg(newDiffExpr, p.adjust.method='BH', absVals = TRUE); I get the following error: Error in gam(y.nosel ~ s(x.nosel, k = k, bs = "cr")) : Not enough (non-NA) data to do anything meaningful I also get another error but not always. Unfortunately, I could not find exactly under which circumstances each error occurred. The second error is this: Error in smooth.construct.cr.smooth.spec(object, dk$data, dk$knots) : x.nosel has insufficient unique values to support 10 knots: reduce k. You can find here <https: www.dropbox.com="" s="" 7ojwm0gmjlkj2s5="" gsea_test.zip=""> my data and a small script of what I am running. In the end of the email I have written also the output of sessionInfo() and traceback() of the first error. Am I doing something wrong or have I overlooked something? Thank you in advance, Kostas Kerkentzes Postgraduate student, Computer Science Department, University of Crete This is the output of sessionInfo(): R version 3.0.2 (2013-09-25) Platform: x86_64-w64-mingw32/x64 (64-bit) locale: [1] LC_COLLATE=Greek_Greece.1253 LC_CTYPE=Greek_Greece.1253 LC_MONETARY=Greek_Greece.1253 [4] LC_NUMERIC=C LC_TIME=Greek_Greece.1253 attached base packages: [1] grid splines parallel stats graphics grDevices utils datasets methods [10] base other attached packages: [1] KEGG.db_2.10.1 hgu95av2.db_2.10.1 org.Hs.eg.db_2.10.1 phenoTest_1.10.0 [5] RSQLite_0.11.4 DBI_0.2-7 gridExtra_0.9.1 ggplot2_0.9.3.1 [9] BMA_3.16.2.3 robustbase_0.90-2 leaps_2.9 survival_2.37-7 [13] Heatplus_2.8.0 annotate_1.40.1 AnnotationDbi_1.24.0 Biobase_2.22.0 [17] BiocGenerics_0.8.0 loaded via a namespace (and not attached): [1] affy_1.40.0 affyio_1.30.0 BiocInstaller_1.12.0 biomaRt_2.18.0 [5] BioNet_1.23.2 Biostrings_2.30.1 bit_1.1-11 bitops_1.0-6 [9] Category_2.28.0 caTools_1.16 cellHTS2_2.26.0 cluster_1.15.1 [13] codetools_0.2-8 colorspace_1.2-4 DEoptimR_1.0-1 dichromat_2.0-0 [17] digest_0.6.4 ellipse_0.3-8 ff_2.2-12 foreach_1.4.1 [21] Formula_1.1-1 gdata_2.13.2 genefilter_1.44.0 GenomicRanges_1.14.4 [25] gplots_2.12.1 graph_1.40.1 GSEABase_1.24.0 gtable_0.1.2 [29] gtools_3.3.1 hgu133a.db_2.10.1 Hmisc_3.14-3 hopach_2.22.0 [33] HTSanalyzeR_2.14.0 igraph_0.7.0 IRanges_1.20.7 iterators_1.0.6 [37] KernSmooth_2.23-10 labeling_0.2 lattice_0.20-27 latticeExtra_0.6-26 [41] limma_3.18.13 MASS_7.3-30 Matrix_1.1-2-2 mgcv_1.7-28 [45] munsell_0.4.2 mvtnorm_0.9-9997 nlme_3.1-115 oligoClasses_1.24.0 [49] pcaPP_1.9-49 plyr_1.8.1 prada_1.38.0 preprocessCore_1.24.0 [53] proto_0.3-10 RankProd_2.34.0 RBGL_1.38.0 RColorBrewer_1.0-5 [57] Rcpp_0.11.0 RCurl_1.95-4.1 reshape2_1.2.2 rrcov_1.3-4 [61] scales_0.2.3 SNPchip_2.8.0 stats4_3.0.2 stringr_0.6.2 [65] tools_3.0.2 vsn_3.30.0 XML_3.98-1.1 xtable_1.7-3 [69] XVector_0.2.0 zlibbioc_1.8.0 This is the output of traceback(): 13: stop("Not enough (non-NA) data to do anything meaningful") 12: gam(y.nosel ~ s(x.nosel, k = k, bs = "cr")) 11: getNesGam(escore, gsets.len, es.sim) 10: getSummary(es, es.sim, fchr, p.adjust.method = p.adjust.method, pval.comp.method, pval.smooth.tail, signatures, test, fewGsets) 9: cbind(n = unlist(lapply(x$s, length)), getSummary(es, es.sim, fchr, p.adjust.method = p.adjust.method, pval.comp.method, pval.smooth.tail, signatures, test, fewGsets)) 8: gseaSignificance(x, p.adjust.method, pval.comp.method, pval.smooth.tail) 7: gseaSignificance(x, p.adjust.method, pval.comp.method, pval.smooth.tail) 6: FUN(X[[1L]], ...) 5: lapply(x, function(x) gseaSignificance(x, p.adjust.method, pval.comp.method, pval.smooth.tail)) 4: gseaSignificance(sim, p.adjust.method = p.adjust.method, pval.comp.method = pval.comp.method, pval.smooth.tail = pval.smooth.tail) 3: gseaSignificance(sim, p.adjust.method = p.adjust.method, pval.comp.method = pval.comp.method, pval.smooth.tail = pval.smooth.tail) 2: gsea(x = x, gsets = kegg, logScale = logScale, absVals = absVals, averageRepeats = averageRepeats, B = B, mc.cores = mc.cores, test = test, p.adjust.method = p.adjust.method, pval.comp.method = pval.comp.method, pval.smooth.tail = pval.smooth.tail, minGenes = minGenes, maxGenes = maxGenes, center = center) 1: gsea.kegg(newDiffExpr, p.adjust.method = "BH", absVals = TRUE) [[alternative HTML version deleted]]