[BioC] Visualization and Interpretation of Go Terms

[James W. MacDonald]

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.
>
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-- 
James W. MacDonald, M.S.
Biostatistician
University of Washington
Environmental and Occupational Health Sciences
4225 Roosevelt Way NE, # 100
Seattle WA 98105-6099