---
title: "DOSE: Disease Ontology Semantic and Enrichment Analysis"
author:
  - name: Guangchuang Yu
    email: guangchuangyu@gmail.com
    affiliation: Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University
date: today
format:
  html:
    toc: true
vignette: >
  %\VignetteIndexEntry{DOSE}
  %\VignettePackage{DOSE}
  %\VignetteEngine{quarto::html}
  %\VignetteEncoding{UTF-8}
---

# Overview

DOSE provides disease ontology semantic similarity and enrichment analysis for
biomedical knowledge mining. It supports ontology-aware interpretation of genes,
gene clusters, and ranked gene lists by connecting user-provided genes to
disease, phenotype, and cancer gene-set annotations.

The package currently focuses on two analysis layers:

- semantic similarity among ontology terms, genes, and gene clusters
- over-representation analysis and gene set enrichment analysis for disease- or
  phenotype-associated gene sets

# Data

DOSE uses curated ontology and gene annotation resources prepared by package
data-building scripts.

Ontology-backed resources are stored in SQLite databases and support semantic
similarity:

- Human Disease Ontology (HDO)
- Human Phenotype Ontology (HPO)
- Mammalian Phenotype Ontology (MPO)

Gene-set resources that are only used for enrichment analysis are represented as
gene-set collections, including cancer gene sets from the Network of Cancer
Genes (NCG).

# Methods

DOSE implements classic semantic similarity methods for ontology terms and gene
products, including Resnik, Lin, Jiang, Schlicker, and Wang methods where
supported by the ontology data.

For enrichment analysis, DOSE provides both over-representation analysis and
gene set enrichment analysis interfaces. These methods allow researchers to
interpret gene vectors or ranked gene lists in disease and phenotype contexts.

# Documentation

This vignette is intentionally brief. Full documentation, worked examples, and
case studies are maintained in the online book:

<https://yulab-smu.top/biomedical-knowledge-mining-book/>

# Citation

If you use DOSE in published research, please cite:

Yu G, Wang L-G, Yan G-R, He Q-Y. DOSE: an R/Bioconductor package for Disease
Ontology Semantic and Enrichment analysis. *Bioinformatics* 2015, 31(4):608-609.
<https://doi.org/10.1093/bioinformatics/btu684>

# Support

Please post questions and issues to the
[Bioconductor support site](https://support.bioconductor.org/) and tag the post
with `DOSE`.