Hi, What is the best way to use roast for platforms where there are multiple probes per gene? Is it best to choose one probe per gene or for instance, should all probes in all the genes in a given pathway be entered? Thanks, Beth -- output of sessionInfo(): R version 3.1.0 (2014-04-10) Platform: x86_64-w64-mingw32/x64 (64-bit) locale: [1] LC_COLLATE=English_United States.1252 LC_CTYPE=English_United States.1252 [3] LC_MONETARY=English_United States.1252 LC_NUMERIC=C [5] LC_TIME=English_United States.1252 attached base packages: [1] parallel stats graphics grDevices utils datasets methods base other attached packages: [1] biomaRt_2.20.0 IRanges_1.22.6 BiocGenerics_0.10.0 BiocInstaller_1.14.2 [5] edgeR_3.6.1 limma_3.20.1 loaded via a namespace (and not attached): [1] AnnotationDbi_1.26.0 Biobase_2.24.0 DBI_0.2-7 GenomeInfoDb_1.0.2 [5] RCurl_1.95-4.1 RSQLite_0.11.4 stats4_3.1.0 tools_3.1.0 [9] XML_3.98-1.1 > -- Sent via the guest posting facility at bioconductor.org.

Dear Beth, You can include all probes for all genes in a given pathway. That's what I do. It would not be wrong to choose one good probe for each gene (if chosen independently of differential expression), but including multiple probes when they are available gives more power. Best wishes Gordon > Date: Thu, 8 May 2014 10:50:16 -0700 (PDT) > From: "Beth [guest]" <guest at="" bioconductor.org=""> > To: bioconductor at r-project.org, wilmotb at ohsu.edu > Subject: [BioC] limma -roast: probe-level vs gene-level? > > Hi, > What is the best way to use roast for platforms where there are multiple > probes per gene? Is it best to choose one probe per gene or for > instance, should all probes in all the genes in a given pathway be > entered? > Thanks, > Beth > > -- output of sessionInfo(): > > R version 3.1.0 (2014-04-10) > Platform: x86_64-w64-mingw32/x64 (64-bit) > > locale: > [1] LC_COLLATE=English_United States.1252 LC_CTYPE=English_United States.1252 > [3] LC_MONETARY=English_United States.1252 LC_NUMERIC=C > [5] LC_TIME=English_United States.1252 > > attached base packages: > [1] parallel stats graphics grDevices utils datasets methods base > > other attached packages: > [1] biomaRt_2.20.0 IRanges_1.22.6 BiocGenerics_0.10.0 BiocInstaller_1.14.2 > [5] edgeR_3.6.1 limma_3.20.1 > > loaded via a namespace (and not attached): > [1] AnnotationDbi_1.26.0 Biobase_2.24.0 DBI_0.2-7 GenomeInfoDb_1.0.2 > [5] RCurl_1.95-4.1 RSQLite_0.11.4 stats4_3.1.0 tools_3.1.0 > [9] XML_3.98-1.1 ______________________________________________________________________ The information in this email is confidential and intend...{{dropped:4}}