04_meshes_Semantic_Similarity.Rmd

# MeSH semantic similarity analysis {#meshes-semantic-similarity}


```{r meshes-geneSim, include=FALSE}
geneSim <- meshes::geneSim
```


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 a 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>)|

## Supported organisms {#meshes-supported-organisms}

The `r Biocpkg("meshes")` package [@yu_meshes_2018] relies on `MeSHDb` to prepare semantic data for measuring simiarlity. `MeSHDb` can be downloaded from `r Biocpkg("AnnotationHub")` (see also `r Biocpkg("AHMeSHDbs")`) and about 200 species are available and are supported by the `r Biocpkg("meshes")` package. 

First, we need to load/fetch species-specific MeSH annotation database:

```{r meshdb, warning=FALSE, cache=TRUE, eval=FALSE}
#############################
## BioC 2.14 to BioC 3.13  ##
#############################
##
## library(MeSH.Hsa.eg.db)
## db <- MeSH.Hsa.eg.db
##
##---------------------------

# From BioC 3.14 (Nov. 2021, with R-4.2.0)
library(AnnotationHub)
library(MeSHDbi)
ah <- AnnotationHub(localHub=TRUE)
hsa <- query(ah, c("MeSHDb", "Homo sapiens"))
file_hsa <- hsa[[1]]
db <- MeSHDbi::MeSHDb(file_hsa)
```


The semantic data can be prepared by the `meshdata()` function:

```{r meshdata, cache=TRUE, eval=FALSE}
library(meshes)
hsamd <- meshdata(db, category='A', computeIC=T, database="gendoo")

## you may want to save the result for future usage
# 
# save(hsamd, file = "hsamd.rda")
#
```

```{r echo=FALSE}
## saveRDS(hsamd, file="cache/hsamd.rds")
hsamd <- readRDS("cache/hsamd.rds")
```


## MeSH semantic similarity measurement {#meshes-semantic-simiarlity}

The `r Biocpkg("meshes")` package [@yu_meshes_2018] 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]), to measure MeSH term semantic similarity. For algorithm details, please refer to [Chapter 1](#semantic-similarity-overview).


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

```{r meshSim, cache=TRUE}
library(meshes)
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")
```



## Gene semantic similarity measurement {#gene-meshes-semantic-similarity}


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


```{r meshes-geneSim2, cache=TRUE}
geneSim("241", "251", semData=hsamd, measure="Wang", combine="BMA")
geneSim(c("241", "251"), c("835", "5261","241", "994"), semData=hsamd, measure="Wang", combine="BMA")
```