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:

```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 ontologyDAG 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: isa, 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 ontologyDAG 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: isa, partof

With the following columns in the metadata data frame: id, short_id, name, namespace, definition ```

The following IC methods are provided:

```

alltermICmethods() [1] "ICoffspring" "ICheight" "ICannotation" "ICuniversal" [5] "ICZhang2006" "ICSeco2004" "ICZhou2008" "ICSeddiqui2010" [9] "ICSanchez2011" "ICMeng2012" "ICWang_2007" ```

The following semantic similarity methods are provided:

```

alltermsimmethods() [1] "SimLin1998" "SimResnik1999" "SimFaITH2010"
[4] "SimRelevance2006" "SimSimIC2010" "SimXGraSM2013"
[7] "SimEISI2015" "SimAIC2014" "SimZhang2006"
[10] "Simuniversal" "SimWang2007" "SimGOGO2018"
[13] "SimRada1989" "SimResnikedge2005" "SimLeocock1998"
[16] "SimWP1994" "SimSlimani2006" "SimShenoy2012"
[19] "SimPekar2002" "SimStojanovic2001" "SimWangedge2012"
[22] "SimZhong2002" "SimAlMubaid2006" "SimLi2003"
[25] "SimRSS2013" "SimHRSS2013" "SimShen2010"
[28] "SimSSDD2013" "SimJiang1997" "SimKappa"
[31] "SimJaccard" "SimDice" "SimOverlap"
[34] "Sim_Ancestor" ```

The following group similarity methods are provided:

```

allgroupsimmethods() [1] "GroupSimpairwiseavg" "GroupSimpairwisemax"
[3] "GroupSimpairwiseBMA" "GroupSimpairwiseBMM"
[5] "GroupSimpairwiseABM" "GroupSimpairwiseHDF"
[7] "GroupSimpairwiseMHDF" "GroupSimpairwiseVHDF"
[9] "GroupSimpairwiseFroehlich2007" "GroupSimpairwiseJoeng2014"
[11] "GroupSimSimALN" "GroupSimSimGIC"
[13] "GroupSimSimDIC" "GroupSimSimUIC"
[15] "GroupSimSimUI" "GroupSimSimDB"
[17] "GroupSimSimUB" "GroupSimSimNTO"
[19] "GroupSimSimCOU" "GroupSimSimCOT"
[21] "GroupSimSimLP" "GroupSimYe2005"
[23] "GroupSimSimCHO" "GroupSimSimALD"
[25] "GroupSimJaccard" "GroupSimDice"
[27] "GroupSimOverlap" "GroupSimKappa" ```

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
• 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