# simona: Semantic Similarity in Bio-Ontologies
## Introduction
This package implements infrastructures for ontology analysis by offering
efficient data structures, fast ontology traversal methods, and elegant visualizations.
It provides a robust toolbox supporting over 70 methods for semantic similarity analysis.
Most methods implemented in **simona** are from
the [supplementary file](https://academic.oup.com/bib/article/18/5/886/2562801#supplementary-data)
of the paper ["Mazandu et al., Gene Ontology semantic similarity tools: survey
on features and challenges for biological knowledge discovery. Briefings in
Bioinformatics 2017"](https://doi.org/10.1093/bib/bbw067).
## Citation
simona: a Comprehensive R package for Semantic Similarity Analysis on Bio-Ontologies
Z Gu - bioRxiv, 2023. https://doi.org/10.1101/2023.12.03.569758
## Install
**simona** is available on [Bioconductor](https://bioconductor.org/packages/release/bioc/html/simona.html).
It can be installed by:
```r
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("simona")
```
Or the devel version:
```r
devtools::install_github("jokergoo/simona")
```
## Usage
Creat an ontology object:
```r
library(simona)
parents = c("a", "a", "b", "b", "c", "d")
children = c("b", "c", "c", "d", "e", "f")
dag = create_ontology_DAG(parents, children)
dag
```
```
An ontology_DAG object:
Source: Ontology
6 terms / 6 relations
Root: a
Terms: a, b, c, d, ...
Max depth: 3
Aspect ratio: 0.67:1 (based on the longest distance from root)
0.68:1 (based on the shortest distance from root)
```
From GO:
```r
dag = create_ontology_DAG_from_GO_db("BP", org_db = "org.Hs.eg.db")
dag
```
```
An ontology_DAG object:
Source: GO BP / GO.db package
28140 terms / 56449 relations
Root: GO:0008150
Terms: GO:0000001, GO:0000002, GO:0000003, GO:0000011, ...
Max depth: 18
Aspect ratio: 342.43:1 (based on the longest distance from root)
780.22:1 (based on the shortest distance from root)
Relations: is_a, part_of
Annotations are available.
With the following columns in the metadata data frame:
id, name, definition
```
Import from an `.obo` file:
```r
dag = import_obo("https://raw.githubusercontent.com/Planteome/plant-ontology/master/po.obo")
dag
```
```
An ontology_DAG object:
Source: po, releases/2023-07-13
1656 terms / 2512 relations
Root: _all_
Terms: PO:0000001, PO:0000002, PO:0000003, PO:0000004, ...
Max depth: 13
Aspect ratio: 25.08:1 (based on the longest distance from root)
39.6:1 (based on the shortest distance from root)
Relations: is_a, part_of
With the following columns in the metadata data frame:
id, short_id, name, namespace, definition
```
The following IC methods are provided:
```
> all_term_IC_methods()
[1] "IC_offspring" "IC_height" "IC_annotation" "IC_universal"
[5] "IC_Zhang_2006" "IC_Seco_2004" "IC_Zhou_2008" "IC_Seddiqui_2010"
[9] "IC_Sanchez_2011" "IC_Meng_2012" "IC_Wang_2007"
```
The following semantic similarity methods are provided:
```
> all_term_sim_methods()
[1] "Sim_Lin_1998" "Sim_Resnik_1999" "Sim_FaITH_2010"
[4] "Sim_Relevance_2006" "Sim_SimIC_2010" "Sim_XGraSM_2013"
[7] "Sim_EISI_2015" "Sim_AIC_2014" "Sim_Zhang_2006"
[10] "Sim_universal" "Sim_Wang_2007" "Sim_GOGO_2018"
[13] "Sim_Rada_1989" "Sim_Resnik_edge_2005" "Sim_Leocock_1998"
[16] "Sim_WP_1994" "Sim_Slimani_2006" "Sim_Shenoy_2012"
[19] "Sim_Pekar_2002" "Sim_Stojanovic_2001" "Sim_Wang_edge_2012"
[22] "Sim_Zhong_2002" "Sim_AlMubaid_2006" "Sim_Li_2003"
[25] "Sim_RSS_2013" "Sim_HRSS_2013" "Sim_Shen_2010"
[28] "Sim_SSDD_2013" "Sim_Jiang_1997" "Sim_Kappa"
[31] "Sim_Jaccard" "Sim_Dice" "Sim_Overlap"
[34] "Sim_Ancestor"
```
The following group similarity methods are provided:
```
> all_group_sim_methods()
[1] "GroupSim_pairwise_avg" "GroupSim_pairwise_max"
[3] "GroupSim_pairwise_BMA" "GroupSim_pairwise_BMM"
[5] "GroupSim_pairwise_ABM" "GroupSim_pairwise_HDF"
[7] "GroupSim_pairwise_MHDF" "GroupSim_pairwise_VHDF"
[9] "GroupSim_pairwise_Froehlich_2007" "GroupSim_pairwise_Joeng_2014"
[11] "GroupSim_SimALN" "GroupSim_SimGIC"
[13] "GroupSim_SimDIC" "GroupSim_SimUIC"
[15] "GroupSim_SimUI" "GroupSim_SimDB"
[17] "GroupSim_SimUB" "GroupSim_SimNTO"
[19] "GroupSim_SimCOU" "GroupSim_SimCOT"
[21] "GroupSim_SimLP" "GroupSim_Ye_2005"
[23] "GroupSim_SimCHO" "GroupSim_SimALD"
[25] "GroupSim_Jaccard" "GroupSim_Dice"
[27] "GroupSim_Overlap" "GroupSim_Kappa"
```
There is also a visualization on the complete DAG:
```r
sig_go_ids = readRDS(system.file("extdata", "sig_go_ids.rds", package = "simona"))
dag_circular_viz(dag, highlight = sig_go_ids, reorder_level = 3,
legend_labels_from = "name")
```
## Vignettes
- [01. ontology_DAG: a class for ontology data](https://jokergoo.github.io/simona/articles/v01_dag.html)
- [02. Gene Ontology](https://jokergoo.github.io/simona/articles/v02_GO.html)
- [03. Import ontology files](https://jokergoo.github.io/simona/articles/v03_import.html)
- [04. Information content](https://jokergoo.github.io/simona/articles/v04_information_content.html)
- [05. Term similarity](https://jokergoo.github.io/simona/articles/v05_term_similarity.html)
- [06. Similarity between two groups of terms](https://jokergoo.github.io/simona/articles/v06_group_similarity.html)
- [07. Visualize DAGs](https://jokergoo.github.io/simona/articles/v07_dag_visualization.html)
- [08. Random DAGs](https://jokergoo.github.io/simona/articles/v08_random.html)
- [09. Shiny app](https://jokergoo.github.io/simona/articles/v09_shiny.html)
- [10. Functional enrichment](https://jokergoo.github.io/simona/articles/v10_enrichment.html)
## License
MIT @ Zuguang Gu
