I have performed the Gene Set Enrichment Analysis using the GO Stats package in R and have the Over represented set of genes and their annotated GO terms. I would like to visualize them as a tree structure to study the relationships. The number of enriched terms are: dim(summary(hgOvermatch, pvalue=0.05)) [1] 214 7 I know there are other packages to create the GO Tree visualization but one needs to perform the analysis within the tool to get the graphs. I also tried using AMIGO to visualize the tree structure but since there are more than 50 terms this is a problem. I would like to know how to interpret these terms further and also to view them as a tree structure. Thanks -- output of sessionInfo(): R version 2.15.2 (2012-10-26) Platform: i686-redhat-linux-gnu (32-bit) locale: [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 LC_PAPER=C LC_NAME=C [9] LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C attached base packages: [1] splines grid stats graphics grDevices utils datasets methods base other attached packages: [1] EMA_1.3.2 biomaRt_2.10.0 siggenes_1.32.0 RankProd_2.30.0 GSA_1.03 [6] rgl_0.93.928 gcrma_2.30.0 multtest_2.12.0 FactoMineR_1.23 leaps_2.9 [11] scatterplot3d_0.3-33 ellipse_0.3-7 car_2.0-11 survival_2.36-14 nnet_7.3-5 [16] MASS_7.3-22 heatmap.plus_1.3 Rgraphviz_2.2.1 GOSim_1.2.7.7 igraph_0.6.5-1 [21] org.Hs.eg.db_2.8.0 corpcor_1.6.5 Matrix_1.0-9 RBGL_1.34.0 flexmix_2.3-10 [26] lattice_0.20-13 cluster_1.14.3 topGO_2.10.0 SparseM_0.96 annotate_1.36.0 [31] RamiGO_1.4.0 gsubfn_0.6-5 proto_0.3-10 BiocInstaller_1.8.3 xtable_1.6-0 [36] ath1121501.db_2.7.1 org.At.tair.db_2.8.0 GO.db_2.8.0 limma_3.14.4 csSAM_1.2.1 [41] GOstats_2.24.0 RSQLite_0.10.0 DBI_0.2-5 graph_1.36.2 Category_2.22.0 [46] AnnotationDbi_1.20.5 affy_1.36.1 Biobase_2.16.0 BiocGenerics_0.4.0 R.utils_1.23.2 [51] R.oo_1.13.0 R.methodsS3_1.4.2 loaded via a namespace (and not attached): [1] affyio_1.22.0 AnnotationForge_1.0.3 Biostrings_2.22.0 genefilter_1.40.0 GSEABase_1.18.0 [6] IRanges_1.16.6 modeltools_0.2-19 parallel_2.15.2 png_0.1-4 preprocessCore_1.18.0 [11] RCurl_1.7-0 RCytoscape_1.8.2 stats4_2.15.2 tcltk_2.15.2 tools_2.15.2 [16] XML_3.9-4 XMLRPC_0.3-0 zlibbioc_1.4.0 -- Sent via the guest posting facility at bioconductor.org.

Hi Sandy, On 4/23/2013 10:37 AM, Sandy [guest] wrote: > I have performed the Gene Set Enrichment Analysis using the GO Stats package in R and have the Over represented set of genes and their annotated GO terms. > > I would like to visualize them as a tree structure to study the relationships. > > The number of enriched terms are: > > dim(summary(hgOvermatch, pvalue=0.05)) > [1] 214 7 > > > I know there are other packages to create the GO Tree visualization but one needs to perform the analysis within the tool to get the graphs. I also tried using AMIGO to visualize the tree structure but since there are more than 50 terms this is a problem. > > I would like to know how to interpret these terms further and also to view them as a tree structure. Have you looked at the vignette that comes with GOstats that specifically covers this topic? Try loading the GOstats package and then doing openVignette() at the R prompt. And then look for GOstats - Visualizing Data Using GOstats Best, Jim > > Thanks > > -- output of sessionInfo(): > > R version 2.15.2 (2012-10-26) > Platform: i686-redhat-linux-gnu (32-bit) > > locale: > [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 > [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 LC_PAPER=C LC_NAME=C > [9] LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C > > attached base packages: > [1] splines grid stats graphics grDevices utils datasets methods base > > other attached packages: > [1] EMA_1.3.2 biomaRt_2.10.0 siggenes_1.32.0 RankProd_2.30.0 GSA_1.03 > [6] rgl_0.93.928 gcrma_2.30.0 multtest_2.12.0 FactoMineR_1.23 leaps_2.9 > [11] scatterplot3d_0.3-33 ellipse_0.3-7 car_2.0-11 survival_2.36-14 nnet_7.3-5 > [16] MASS_7.3-22 heatmap.plus_1.3 Rgraphviz_2.2.1 GOSim_1.2.7.7 igraph_0.6.5-1 > [21] org.Hs.eg.db_2.8.0 corpcor_1.6.5 Matrix_1.0-9 RBGL_1.34.0 flexmix_2.3-10 > [26] lattice_0.20-13 cluster_1.14.3 topGO_2.10.0 SparseM_0.96 annotate_1.36.0 > [31] RamiGO_1.4.0 gsubfn_0.6-5 proto_0.3-10 BiocInstaller_1.8.3 xtable_1.6-0 > [36] ath1121501.db_2.7.1 org.At.tair.db_2.8.0 GO.db_2.8.0 limma_3.14.4 csSAM_1.2.1 > [41] GOstats_2.24.0 RSQLite_0.10.0 DBI_0.2-5 graph_1.36.2 Category_2.22.0 > [46] AnnotationDbi_1.20.5 affy_1.36.1 Biobase_2.16.0 BiocGenerics_0.4.0 R.utils_1.23.2 > [51] R.oo_1.13.0 R.methodsS3_1.4.2 > > loaded via a namespace (and not attached): > [1] affyio_1.22.0 AnnotationForge_1.0.3 Biostrings_2.22.0 genefilter_1.40.0 GSEABase_1.18.0 > [6] IRanges_1.16.6 modeltools_0.2-19 parallel_2.15.2 png_0.1-4 preprocessCore_1.18.0 > [11] RCurl_1.7-0 RCytoscape_1.8.2 stats4_2.15.2 tcltk_2.15.2 tools_2.15.2 > [16] XML_3.9-4 XMLRPC_0.3-0 zlibbioc_1.4.0 > > -- > Sent via the guest posting facility at bioconductor.org. > > _______________________________________________ > 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