---
title: "Disease Ontology Semantic and Enrichment analysis"
author: "\\

	Guangchuang Yu (<guangchuangyu@gmail.com>)\\

        School of Public Health, The University of Hong Kong"
date: "`r Sys.Date()`"
bibliography: DOSE.bib
csl: nature.csl
output: 
  BiocStyle::html_document:
    toc: true
  BiocStyle::pdf_document:
    toc: true
vignette: >
  %\VignetteIndexEntry{00 DOSE introduction}
  %\VignettePackage{DOSE}
  % \VignetteEngine{knitr::rmarkdown}
  % \usepackage[utf8]{inputenc}
---

```{r style, echo=FALSE, results="asis", message=FALSE}
BiocStyle::markdown()
knitr::opts_chunk$set(tidy = FALSE,
                      warning = FALSE,
                      message = FALSE)
```

```{r echo=FALSE, results='hide', message=FALSE}
library(DOSE)
```


Disease Ontology (DO)[@schriml_disease_2011] aims to provide an open source ontology for the integration of biomedical data that is associated with human disease. 
We developed `r Biocpkg("DOSE")`[@yu_dose_2015] package to promote the investigation of diseases. `r Biocpkg("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.


# Citation

If you use `r Biocpkg("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>.


# Overview

`r Biocpkg("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)[@schriml_disease_2011], Network of Cancer Gene (NCG)[@omer_ncg] and DisGeNET[@janet_disgenet]. In addition, several visualization methods were developed to help interpreting semantic and enrichment results.


# Vignette Entry

+ [Semantic similarity analysis](semanticAnalysis.html)
+ [Disease enrichment analysis](enrichmentAnalysis.html)
+ [Disease GSEA analysis](GSEA.html)

More documents can be found in <https://guangchuangyu.github.io/DOSE>.

# Session Information

Here is the output of `sessionInfo()` on the system on which this document was compiled:

```{r echo=FALSE}
sessionInfo()
```

# References

