Disease Ontology (DO)1 aims to provide an open source ontology for the integration of biomedical data that is associated with human disease. We developed DOSE2 package to promote the investigation of diseases. DOSE provides five methods including Resnik, Lin, Jiang, Rel and Wang for measuring semantic similarities among DO terms and gene products; Hypergeometric model and Gene Set Enrichment Analysis (GSEA) were also implemented for extracting disease association insight from genome wide expression profiles.
If you use DOSE in published research, please cite G. Yu (2015).
G Yu, LG Wang, GR Yan, QY He. DOSE: an R/Bioconductor package for Disease Ontology Semantic and Enrichment analysis. Bioinformatics 2015, 31(4):608-609. http://dx.doi.org/10.1093/bioinformatics/btu684.
DOSE provides five methods for measureing semantic similarity among DO terms and genes. It implemented over-representation analysis to associate disease with gene list (e.g. differential expressed genes) and gene set enrichment analysis to associate disease with genome wide expression profiles. The enrichment analyses support Disease Ontology (DO)1, Network of Cancer Gene (NCG)3 and DisGeNET4. In addition, several visualization methods were developed to help interpreting semantic and enrichment results.
More documents can be found in https://guangchuangyu.github.io/DOSE.
Here is the output of sessionInfo() on the system on which this document was compiled:
## R version 3.4.0 (2017-04-21)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server 2012 R2 x64 (build 9600)
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## Matrix products: default
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## locale:
## [1] LC_COLLATE=C
## [2] LC_CTYPE=English_United States.1252
## [3] LC_MONETARY=English_United States.1252
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United States.1252
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## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
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## other attached packages:
## [1] DOSE_3.2.0 BiocStyle_2.4.0
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## loaded via a namespace (and not attached):
## [1] Rcpp_0.12.10 compiler_3.4.0 plyr_1.8.4
## [4] tools_3.4.0 digest_0.6.12 RSQLite_1.1-2
## [7] evaluate_0.10 memoise_1.1.0 tibble_1.3.0
## [10] gtable_0.2.0 igraph_1.0.1 fastmatch_1.1-0
## [13] DBI_0.6-1 yaml_2.1.14 parallel_3.4.0
## [16] gridExtra_2.2.1 fgsea_1.2.1 stringr_1.2.0
## [19] knitr_1.15.1 S4Vectors_0.14.0 IRanges_2.10.0
## [22] stats4_3.4.0 rprojroot_1.2 grid_3.4.0
## [25] qvalue_2.8.0 Biobase_2.36.0 data.table_1.10.4
## [28] AnnotationDbi_1.38.0 BiocParallel_1.10.0 GOSemSim_2.2.0
## [31] rmarkdown_1.4 reshape2_1.4.2 GO.db_3.4.1
## [34] DO.db_2.9 ggplot2_2.2.1 magrittr_1.5
## [37] splines_3.4.0 backports_1.0.5 scales_0.4.1
## [40] htmltools_0.3.5 BiocGenerics_0.22.0 colorspace_1.3-2
## [43] stringi_1.1.5 lazyeval_0.2.0 munsell_0.4.31. Schriml, L. M. et al. Disease ontology: A backbone for disease semantic integration. Nucleic Acids Research 40, D940–D946 (2011).
2. Yu, G., Wang, L.-G., Yan, G.-R. & He, Q.-Y. DOSE: An r/bioconductor package for disease ontology semantic and enrichment analysis. Bioinformatics 31, 608–609 (2015).
3. A., O., D., G. M., M., T. P. & C., F. D. NCG 5.0: Updates of a manually curated repository of cancer genes and associated properties from cancer mutational screenings. Nucleic Acids Research 44, D992–D999 (2016).
4. Janet, P. et al. DisGeNET: A discovery platform for the dynamical exploration of human diseases and their genes. Database 2015, bav028 (2015).