---
title: "MeSH Enrichment and Semantic Analyses"
author: "Guangchuang Yu\\
School of Basic Medical Sciences, Southern Medical University"
date: "`r Sys.Date()`"
bibliography: meshes.bib
biblio-style: apalike
output:
prettydoc::html_pretty:
toc: true
theme: cayman
highlight: github
pdf_document:
toc: true
vignette: >
% \VignetteIndexEntry{An introduction to meshes}
% \VignetteEngine{knitr::rmarkdown}
%\VignetteDepends{MeSH.Hsa.eg.db}
%\VignetteDepends{MeSH.db}
% \usepackage[utf8]{inputenc}
%\VignetteEncoding{UTF-8}
---
```{r style, echo=FALSE, results="asis", message=FALSE}
knitr::opts_chunk$set(tidy = FALSE,
warning = FALSE,
message = FALSE)
```
```{r echo=FALSE, results="hide", message=FALSE}
CRANpkg <- function (pkg) {
cran <- "https://CRAN.R-project.org/package"
fmt <- "[%s](%s=%s)"
sprintf(fmt, pkg, cran, pkg)
}
Biocpkg <- function (pkg) {
sprintf("[%s](http://bioconductor.org/packages/%s)", pkg, pkg)
}
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:
|Abbreviation |Category |
|:------------|:---------------------------------------------------------------|
|A |Anatomy |
|B |Organisms |
|C |Diseases |
|D |Chemicals and Drugs |
|E |Analytical, Diagnostic and Therapeutic Techniques and Equipment |
|F |Psychiatry and Psychology |
|G |Phenomena and Processes |
|H |Disciplines and Occupations |
|I |Anthropology, Education, Sociology and Social Phenomena |
|J |Technology and Food and Beverages |
|K |Humanities |
|L |Information Science |
|M |Persons |
|N |Health Care |
|V |Publication Type |
|Z |Geographical Locations |
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]
## minGSSize = 200 for only speed up the compilation of the vignette
x <- enrichMeSH(de, MeSHDb = "MeSH.Hsa.eg.db", database='gendoo', category = 'C', minGSSize=200)
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}
## minGSSize = 200 for only speed up the compilation of the vignette
y <- gseMeSH(geneList, MeSHDb = "MeSH.Hsa.eg.db", database = 'gene2pubmed', category = "G", minGSSize=200)
head(y)
```
User can use visualization methods implemented in `r Biocpkg("enrichplot")` (i.e.`barplot`, `dotplot`, `cnetplot`, `emapplot` 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]
# Need helps?
If you have questions/issues, please visit
[meshes homepage](https://guangchuangyu.github.io/software/meshes/) first.
Your problems are mostly documented. If you think you found a bug, please follow
[the guide](https://guangchuangyu.github.io/2016/07/how-to-bug-author/) and
provide a reproducible example to be posted
on
[github issue tracker](https://github.com/GuangchuangYu/meshes/issues).
For questions, please post
to [Bioconductor support site](https://support.bioconductor.org/) and tag your
post with *meshes*.
For Chinese user, you can follow me on [WeChat (微信)](https://guangchuangyu.github.io/blog_images/biobabble.jpg).
# Session Information
Here is the output of `sessionInfo()` on the system on which this document was compiled:
```{r echo=FALSE}
sessionInfo()
```
# References