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 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

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. 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)
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:

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:

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:

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
• 02. Gene Ontology
• 03. Import ontology files
• 04. Information content
• 05. Term similarity
• 06. Similarity between two groups of terms
• 07. Visualize DAGs
• 08. Random DAGs
• 09. Shiny app
• 10. Functional enrichment

License

MIT @ Zuguang Gu