Hi Everybody, I am trying to analyze Affymetrix data using 'affy' and 'limma' packages. The problem I am facing is that results from decideTests() does not seem to have p-values corrected. Code snippet: ...... AF.fit2 <- eBayes(AF.fit2) ##Without p-value correction AF.results.global <- decideTests(AF.fit2, method = 'global',adjust.method = 'none') write.fit(AF.fit2,AF.results.global,file='AF_RESULTS_GLOB_wo_pval_adju st.tsv', sep = '\t') ##With p-val correction AF.results.global.Corr <- decideTests(AF.fit2, method = 'global',adjust.method = 'fdr',p.value= 0.05) write.fit(AF.fit2,AF.results.global.Corr,file='AF_RESULTS_GLOB.tsv', sep = '\t') ...... Now when I check both the results, with and without p-val correction, they both have exactly same p-values. Which means that it does not makes any difference if I choose 'adjust.method' as 'none' or 'fdr'. Please tell me how can I correct p-values. I am sure I am missing something. Thanks AK >sessionInfo() R version 2.15.3 (2013-03-01) Platform: x86_64-w64-mingw32/x64 (64-bit) locale: [1] LC_COLLATE=English_United States.1252 LC_CTYPE=English_United States.1252 LC_MONETARY=English_United States.1252 [4] LC_NUMERIC=C LC_TIME=English_United States.1252 attached base packages: [1] stats graphics grDevices utils datasets methods base other attached packages: [1] virtualArray_1.2.1 preprocessCore_1.20.0 plyr_1.8 lumiMouseAll.db_1.18.0 org.Mm.eg.db_2.8.0 affycoretools_1.30.0 [7] KEGG.db_2.8.0 GO.db_2.8.0 RSQLite_0.11.4 DBI_0.2-7 genefilter_1.40.0 annotate_1.36.0 [13] AnnotationDbi_1.20.7 sva_3.4.0 mgcv_1.7-23 corpcor_1.6.6 lumi_2.10.0 nleqslv_2.0 [19] affy_1.36.1 limma_3.14.4 Biobase_2.18.0 BiocGenerics_0.4.0 loaded via a namespace (and not attached): [1] affyio_1.26.0 affyPLM_1.34.0 annaffy_1.30.0 AnnotationForge_1.0.3 BiocInstaller_1.8.3 biomaRt_2.14.0 [7] Biostrings_2.26.3 Category_2.24.0 colorspace_1.2-2 gcrma_2.30.0 gdata_2.12.0.2 GEOquery_2.24.1 [13] GOstats_2.24.0 gplots_2.11.0 graph_1.36.2 grid_2.15.3 GSEABase_1.20.2 gtools_2.7.1 [19] IRanges_1.16.6 KernSmooth_2.23-10 lattice_0.20-15 MASS_7.3-23 Matrix_1.0-12 methylumi_2.4.0 [25] nlme_3.1-108 parallel_2.15.3 RBGL_1.34.0 RCurl_1.95-4.1 reshape2_1.2.2 splines_2.15.3 [31] stats4_2.15.3 stringr_0.6.2 survival_2.37-4 tools_2.15.3 XML_3.96-1.1 xtable_1.7-1 [37] zlibbioc_1.4.0 [[alternative HTML version deleted]]

Hi Atul, On 7/23/2013 2:48 PM, Atul Kakrana wrote: > Hi Everybody, > > I am trying to analyze Affymetrix data using 'affy' and 'limma' > packages. The problem I am facing is that results from decideTests() > does not seem to have p-values corrected. > > Code snippet: > ...... > AF.fit2<- eBayes(AF.fit2) > > ##Without p-value correction > AF.results.global<- decideTests(AF.fit2, method = > 'global',adjust.method = 'none') > write.fit(AF.fit2,AF.results.global,file='AF_RESULTS_GLOB_wo_pval_ad just.tsv', > sep = '\t') > > ##With p-val correction > AF.results.global.Corr<- decideTests(AF.fit2, method = > 'global',adjust.method = 'fdr',p.value= 0.05) > write.fit(AF.fit2,AF.results.global.Corr,file='AF_RESULTS_GLOB.tsv', sep > = '\t') > > ...... > > Now when I check both the results, with and without p-val correction, > they both have exactly same p-values. Which means that it does not makes > any difference if I choose 'adjust.method' as 'none' or 'fdr'. Please > tell me how can I correct p-values. I am sure I am missing something. See the arguments for write.fit, in particular the 'adjust' argument. Best, Jim > > Thanks > > AK > >> sessionInfo() > R version 2.15.3 (2013-03-01) > Platform: x86_64-w64-mingw32/x64 (64-bit) > > locale: > [1] LC_COLLATE=English_United States.1252 LC_CTYPE=English_United States.1252 LC_MONETARY=English_United States.1252 > [4] LC_NUMERIC=C LC_TIME=English_United States.1252 > > attached base packages: > [1] stats graphics grDevices utils datasets methods base > > other attached packages: > [1] virtualArray_1.2.1 preprocessCore_1.20.0 plyr_1.8 lumiMouseAll.db_1.18.0 org.Mm.eg.db_2.8.0 affycoretools_1.30.0 > [7] KEGG.db_2.8.0 GO.db_2.8.0 RSQLite_0.11.4 DBI_0.2-7 genefilter_1.40.0 annotate_1.36.0 > [13] AnnotationDbi_1.20.7 sva_3.4.0 mgcv_1.7-23 corpcor_1.6.6 lumi_2.10.0 nleqslv_2.0 > [19] affy_1.36.1 limma_3.14.4 Biobase_2.18.0 BiocGenerics_0.4.0 > > loaded via a namespace (and not attached): > [1] affyio_1.26.0 affyPLM_1.34.0 annaffy_1.30.0 AnnotationForge_1.0.3 BiocInstaller_1.8.3 biomaRt_2.14.0 > [7] Biostrings_2.26.3 Category_2.24.0 colorspace_1.2-2 gcrma_2.30.0 gdata_2.12.0.2 GEOquery_2.24.1 > [13] GOstats_2.24.0 gplots_2.11.0 graph_1.36.2 grid_2.15.3 GSEABase_1.20.2 gtools_2.7.1 > [19] IRanges_1.16.6 KernSmooth_2.23-10 lattice_0.20-15 MASS_7.3-23 Matrix_1.0-12 methylumi_2.4.0 > [25] nlme_3.1-108 parallel_2.15.3 RBGL_1.34.0 RCurl_1.95-4.1 reshape2_1.2.2 splines_2.15.3 > [31] stats4_2.15.3 stringr_0.6.2 survival_2.37-4 tools_2.15.3 XML_3.96-1.1 xtable_1.7-1 > [37] zlibbioc_1.4.0 > > > > [[alternative HTML version deleted]] > > _______________________________________________ > Bioconductor mailing list > Bioconductor at r-project.org > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor -- James W. MacDonald, M.S. Biostatistician University of Washington Environmental and Occupational Health Sciences 4225 Roosevelt Way NE, # 100 Seattle WA 98105-6099