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 of the paper “Mazandu et al., Gene Ontology semantic similarity tools: survey on features and challenges for biological knowledge discovery. Briefings in Bioinformatics 2017”.
Citation
Zuguang Gu. simona: a comprehensive R package for semantic similarity analysis on bio-ontologies. bioRxiv, 2023. https://doi.org/10.1101/2023.12.03.569758
Install
simona is available on Bioconductor. It can be installed by:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("simona")Or the devel version:
devtools::install_github("jokergoo/simona")Usage
Creat an ontology object:
library(simona)
parents = c("a", "a", "b", "b", "c", "d")
children = c("b", "c", "c", "d", "e", "f")
dag = create_ontology_DAG(parents, children)
dagAn 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:
dag = create_ontology_DAG_from_GO_db("BP", org_db = "org.Hs.eg.db")
dagAn 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, definitionImport from an .obo file:
dag = import_obo("https://purl.obolibrary.org/obo/po.obo")
dagAn 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, definitionThe 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:
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")