---
title: "MeSH Enrichment and Semantic Analyses"
author: "\\

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

        School of Public Health, The University of Hong Kong"
date: "`r Sys.Date()`"
bibliography: meshes.bib
csl: nature.csl
output: 
  BiocStyle::html_document:
    toc: true
  BiocStyle::pdf_document:
    toc: true
vignette: >
  % \VignetteIndexEntry{An introduction to meshes}
  % \VignetteEngine{knitr::rmarkdown}
  %\VignetteDepends{MeSH.Hsa.eg.db}
  %\VignetteDepends{MeSH.db}  
  % \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(MeSH.Hsa.eg.db)
library(MeSH.db)
library(DOSE)
library(meshes)
```

# Introduction


MeSH (Medical Subject Headings) is the NLM (U.S. National Library of
Medicine) controlled vocabulary used to manually index articles for
MEDLINE/PubMed. MeSH is comprehensive life science vocabulary. MeSH has
19 categories and `MeSH.db` contains 16 of them. That is:

<table>
<thead>
<tr class="header">
<th>Abbreviation</th>
<th>Category</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>A</td>
<td>Anatomy</td>
</tr>
<tr class="even">
<td>B</td>
<td>Organisms</td>
</tr>
<tr class="odd">
<td>C</td>
<td>Diseases</td>
</tr>
<tr class="even">
<td>D</td>
<td>Chemicals and Drugs</td>
</tr>
<tr class="odd">
<td>E</td>
<td>Analytical, Diagnostic and Therapeutic Techniques and Equipment</td>
</tr>
<tr class="even">
<td>F</td>
<td>Psychiatry and Psychology</td>
</tr>
<tr class="odd">
<td>G</td>
<td>Phenomena and Processes</td>
</tr>
<tr class="even">
<td>H</td>
<td>Disciplines and Occupations</td>
</tr>
<tr class="odd">
<td>I</td>
<td>Anthropology, Education, Sociology and Social Phenomena</td>
</tr>
<tr class="even">
<td>J</td>
<td>Technology and Food and Beverages</td>
</tr>
<tr class="odd">
<td>K</td>
<td>Humanities</td>
</tr>
<tr class="even">
<td>L</td>
<td>Information Science</td>
</tr>
<tr class="odd">
<td>M</td>
<td>Persons</td>
</tr>
<tr class="even">
<td>N</td>
<td>Health Care</td>
</tr>
<tr class="odd">
<td>V</td>
<td>Publication Type</td>
</tr>
<tr class="even">
<td>Z</td>
<td>Geographical Locations</td>
</tr>
</tbody>
</table>

MeSH terms were associated with Entrez Gene ID by three methods,
`gendoo`, `gene2pubmed` and `RBBH` (Reciprocal Blast Best Hit).

|Method|Way of corresponding Entrez Gene IDs and MeSH IDs|
|------|-------------------------------------------------|
|Gendoo|Text-mining|
|gene2pubmed|Manual curation by NCBI teams|
|RBBH|sequence homology with BLASTP search (E-value<10<sup>-50</sup>)|


# Enrichment Analysis

`meshes` supports enrichment analysis (over-representation analysis and gene set
enrichment analysis) of gene list or whole expression profile using MeSH
annotation. Data source from `gendoo`, `gene2pubmed` and `RBBH` are all
supported. User can selecte interesting category to test. All 16
categories are supported. The analysis supports >70 species listed in [MeSHDb BiocView](https://bioconductor.org/packages/release/BiocViews.html#___MeSHDb).

For algorithm details, please refer to the vignettes of `r Biocpkg("DOSE")`[@yu_dose_2015] package.

```{r}
library(meshes)
data(geneList, package="DOSE")
de <- names(geneList)[1:100]
x <- enrichMeSH(de, MeSHDb = "MeSH.Hsa.eg.db", database='gendoo', category = 'C')
head(x)
```


In the over-representation analysis, we use data source from `gendoo` and `C` (Diseases) category.

In the following example, we use data source from `gene2pubmed` and test category `G` (Phenomena and Processes) using GSEA.

```{r}
y <- gseMeSH(geneList, MeSHDb = "MeSH.Hsa.eg.db", database = 'gene2pubmed', category = "G")
head(y)
```

User can use visualization methods implemented in [DOSE](https://guangchuangyu.github.io/DOSE) (i.e.`barplot`, `dotplot`, `cnetplot`, `enrichMap`, `upsetplot` and `gseaplot`) to visualize these enrichment results. With these visualization methods, it's much easier to interpret enriched results.


```{r}
dotplot(x)
gseaplot(y, y[1,1], title=y[1,2])
```

# Semantic Similarity

`meshes` implemented four IC-based methods (i.e. Resnik[@philip_semantic_1999], Jiang[@jiang_semantic_1997],
Lin[@lin_information-theoretic_1998] and Schlicker[@schlicker_new_2006]) and one graph-structure based method (i.e. Wang[@wang_new_2007]). For algorithm details, please refer to the vignette of `r Biocpkg("GOSemSim")` package[@yu2010] 


`meshSim` function is designed to measure semantic similarity between two MeSH term vectors.

```{r}
library(meshes)
## hsamd <- meshdata("MeSH.Hsa.eg.db", category='A', computeIC=T, database="gendoo")
data(hsamd)
meshSim("D000009", "D009130", semData=hsamd, measure="Resnik")
meshSim("D000009", "D009130", semData=hsamd, measure="Rel")
meshSim("D000009", "D009130", semData=hsamd, measure="Jiang")
meshSim("D000009", "D009130", semData=hsamd, measure="Wang")

meshSim(c("D001369", "D002462"), c("D017629", "D002890", "D008928"), semData=hsamd, measure="Wang")
```

`geneSim` function is designed to measure semantic similarity among two gene vectors.

```{r}
geneSim("241", "251", semData=hsamd, measure="Wang", combine="BMA")
geneSim(c("241", "251"), c("835", "5261","241", "994"), semData=hsamd, measure="Wang", combine="BMA")
```


# Related tools

## Enrichment analysis
  
- `r Biocpkg("DOSE")`[@yu_dose_2015]
- `r Biocpkg("clusterProfiler")`[@yu2012]
- `r Biocpkg("ReactomePA")`[@yu_reactomepa_2016]
  
## Semantic similairty measurement

- `r Biocpkg("GOSemSim")`[@yu2010]
- `r Biocpkg("DOSE")`[@yu_dose_2015]
  

# Session Information

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

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

# References

