---
title: "HDO_vignette"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{HDO_vignette}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```



## Authors
Erqiang Hu

Department of Pathology, Albert Einstein College of Medicine, Bronx, NY 10461, USA.
Einstein Pathology Single-cell & Bioinformatics laboratory, Bronx, NY 10461, USA.
Montefiore Einstein Comprehensive Cancer Center, Albert Einstein College of Medicine, Bronx, NY 10461, USA.


## Introduction
Disease Ontology (DO) was developed to create a consistent description of gene 
products with disease perspectives, and is essential for supporting functional 
genomics in disease context. Accurate disease descriptions can discover new 
relationships between genes and disease, and new functions for previous 
uncharacteried genes and alleles.We have developed the 
[DOSE](https://bioconductor.org/packages/DOSE/) package for semantic 
similarity analysis and disease enrichment analysis, and `DOSE` import an 
Bioconductor package 'DO.db' to get the relationship(such as parent and child)
between DO terms. But `DO.db`  hasn't been updated for years, and a lot of 
semantic information is [missing](https://github.com/YuLab-SMU/DOSE/issues/57). 
So we developed the new package `HDO.db` for Human Disease Ontology annotation.


```{r setup}
library(HDO.db)
```

## Overview

```{r}
library(AnnotationDbi)
```

The annotation data comes from 
https://github.com/DiseaseOntology/HumanDiseaseOntology/tree/main/src/ontology,
and HDO.db provide these AnnDbBimap object:
```{r}
ls("package:HDO.db")
packageVersion("HDO.db")
```

You can use `help` function to get their documents: `help(DOOFFSPRING)`
```{r}
toTable(HDOmetadata)
HDOMAPCOUNTS
```


## Fetch whole DO terms

In HDO.db, `HDOTERM` represet the whole DO terms and their names. The users can 
also get their aliases and synonyms from `HDOALIAS` and `HDOSYNONYM`, 
respectively.

convert HDOTERM to table
```{r}
doterm <- toTable(HDOTERM)
head(doterm)
```


convert HDOTERM to list
```{r}
dotermlist <- as.list(HDOTERM)
head(dotermlist)
```

get alias of `DOID:0001816`
```{r}
doalias <- as.list(HDOALIAS)
doalias[['DOID:0001816']]
```

get synonym of `DOID:0001816`
```{r}
dosynonym <- as.list(HDOSYNONYM)
dosynonym[['DOID:0001816']]
```


## Fetch the relationship between DO terms
Similar to `DO.db`, we provide four Bimap objects to represent relationship
between DO terms: HDOANCESTOR,HDOPARENTS,HDOOFFSPRING, and HDOCHILDREN.

### HDOANCESTOR
HDOANCESTOR describes the association between DO terms and their ancestral 
terms based on a directed acyclic graph (DAG) defined by the Disease Ontology.
We can use `toTable` function in `AnnotationDbi` package to get a two-column 
data.frame: the first column means the DO term ids, and the second column means 
their ancestor terms.
```{r}
anc_table <- toTable(HDOANCESTOR)
head(anc_table)
```

get ancestor of "DOID:0001816"
```{r}
anc_list <- AnnotationDbi::as.list(HDOANCESTOR)
anc_list[["DOID:0001816"]]
```


### HDOPARENTS
HDOPARENTS describes the association between DO terms and their direct parent 
terms based on DAG. We can use `toTable` function in `AnnotationDbi` package to 
get a two-column data.frame: the first column means the DO term ids, and the 
second column means their parent terms.
```{r}
parent_table <- toTable(HDOPARENTS)
head(parent_table)
```


get parent term of "DOID:0001816"
```{r}
parent_list <- AnnotationDbi::as.list(HDOPARENTS)
parent_list[["DOID:0001816"]]
```

###  HDOOFFSPRING
HDOPARENTS describes the association between DO terms and their offspring  
terms based on DAG. it's the exact opposite of `HDOANCESTOR`, 
whose usage is similar to it.

get offspring of "DOID:0001816"
```{r}
off_list <- AnnotationDbi::as.list(HDO.db::HDOOFFSPRING)
off_list[["DOID:0001816"]]
```

### HDOCHILDREN
HDOCHILDREN describes the association between DO terms and their direct 
children terms based on DAG. it's the exact opposite of `HDOPARENTS`, 
whose usage is similar to it.

get children of "DOID:4"
```{r}
child_list <- AnnotationDbi::as.list(HDO.db::HDOCHILDREN)
child_list[["DOID:4"]]
```

### HDOGENE
HDOGENE describes the association between DO ids and gene ids based on alliancegenome. 
get genes of "DOID:0001816"
```{r}
HDO_list <- AnnotationDbi::as.list(HDO.db::HDOGENE)
HDO_list[["DOID:0001816"]]
```
### HDOGENENCG
HDOGENE describes the association between gene ids and ncg ids based on NCG. 
get disease of gene id 60.
```{r}
NCG_list <- AnnotationDbi::as.list(HDO.db::HDOGENENCG)
NCG_list[["60"]]
```


The HDO.db support the `select()`, `keys()`, `keytypes()`,
and `columns` interface.
```{r}
columns(HDO.db)
## use doid keys
dokeys <- head(keys(HDO.db))
res <- select(x = HDO.db, keys = dokeys, keytype = "doid", 
    columns = c("offspring", "term", "parent", "gene"))
head(res)

## use term keys
dokeys <- head(keys(HDO.db, keytype = "term"))
res <- select(x = HDO.db, keys = dokeys, keytype = "term", 
    columns = c("offspring", "doid", "parent", "gene"))   
head(res)

## use term gene
dokeys <- head(keys(HDO.db, keytype = "gene"))
res <- select(x = HDO.db, keys = dokeys, keytype = "gene", 
    columns = c("doid", "ncg"))   
head(res)
```


## Semantic similarity analysis
Please go to 
<https://yulab-smu.top/biomedical-knowledge-mining-book/> for the vignette.


## Disease enrichment analysis
Please go to 
<https://yulab-smu.top/biomedical-knowledge-mining-book/dose-enrichment.html> 
for the vignette.

```{r}
sessionInfo()
```
