DOSE: Disease Ontology Semantic and Enrichment analysis
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.
Please cite the following article when using
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.
Find out more on Featured Articles.
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")
Semantic similarity measurement
- DO term semantic similarity
- Gene semantic similarity
- DO (Disease Ontology)
- NCG (Network of Cancer Genes)
- DisGeNet (gene-disease and SNP-disease associations)
Find out details and examples on Documentation.
Projects that depend on DOSE
- 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