DOSE: Disease Ontology Semantic and Enrichment analysis

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This package implements five methods proposed by Resnik, Schlicker, Jiang, Lin and Wang respectively for measuring semantic similarities among DO terms and gene products. Enrichment analyses including hypergeometric model and gene set enrichment analysis are also implemented for discovering disease associations of high-throughput biological data.

DOSE is released within the Bioconductor project and the source code is hosted in GitHub.

Author

Guangchuang Yu, School of Public Health, The University of Hong Kong.

Citation

Please cite the following article when using DOSE:

doi Altmetric citation

Yu G, Wang L, Yan G and He QY*. DOSE: an R/Bioconductor package for Disease Ontology Semantic and Enrichment analysis. Bioinformatics, 2015, 31(4):608-609.

Installation

Install DOSE is easy, follow the guide in the Bioconductor page:

## try http:// if https:// URLs are not supported
source("https://bioconductor.org/biocLite.R")
## biocLite("BiocUpgrade") ## you may need this
biocLite("DOSE")

Overview

Semantic similarity measurement

  • DO term semantic similarity
  • Gene semantic similarity

Enrichment Analysis

  • DO (Disease Ontology)
  • NCG (Network of Cancer Genes)
  • DisGeNet (gene-disease and SNP-disease associations)

Visualization

  • barplot
  • cnetplot
  • dotplot
  • enrichMap
  • gseaplot
  • simplot
  • upsetplot

Find out details and examples on Documentation.

Projects that depend on DOSE

Bioconductor packages

  • bioCancer: Interactive Multi-Omics Cancers Data Visualization and Analysis
  • ChIPseeker: ChIPseeker for ChIP peak Annotation, Comparison, and Visualization
  • clusterProfiler: statistical analysis and visualization of functional profiles for genes and gene clusters
  • debrowser: Interactive Differential Expresion Analysis Browser
  • eegc: Engineering Evaluation by Gene Categorization (eegc)
  • facopy: Feature-based association and gene-set enrichment for copy number alteration analysis in cancer
  • LINC: co-expression of lincRNAs and protein-coding genes
  • meshes: MeSH Enrichment and Semantic analyses
  • miRsponge: Identification and analysis of miRNA sponge interaction networks and modules
  • MoonlightR: Identify oncogenes and tumor suppressor genes from omics data
  • PathwaySplice: An R Package for Unbiased Splicing Pathway Analysis
  • ReactomePA: Reactome Pathway Analysis

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