Evaluating homophily in networks via HONTO (HOmophily Network TOol): a case study of chromosomal interactions in human PPI networks.


Journal

Bioinformatics (Oxford, England)
ISSN: 1367-4811
Titre abrégé: Bioinformatics
Pays: England
ID NLM: 9808944

Informations de publication

Date de publication:
01 01 2023
Historique:
received: 10 08 2022
revised: 04 11 2022
accepted: 24 11 2022
pubmed: 29 11 2022
medline: 4 1 2023
entrez: 28 11 2022
Statut: ppublish

Résumé

It has been observed in different kinds of networks, such as social or biological ones, a typical behavior inspired by the general principle 'similarity breeds connections'. These networks are defined as homophilic as nodes belonging to the same class preferentially interact with each other. In this work, we present HONTO (HOmophily Network TOol), a user-friendly open-source Python3 package designed to evaluate and analyze homophily in complex networks. The tool takes in input from the network along with a partition of its nodes into classes and yields a matrix whose entries are the homophily/heterophily z-score values. To complement the analysis, the tool also provides z-score values of nodes that do not interact with any other node of the same class. Homophily/heterophily z-scores values are presented as a heatmap allowing a visual at-a-glance interpretation of results. Tool's source code is available at https://github.com/cumbof/honto under the MIT license, installable as a package from PyPI (pip install honto) and conda-forge (conda install -c conda-forge honto), and has a wrapper for the Galaxy platform available on the official Galaxy ToolShed (Blankenberg et al., 2014) at https://toolshed.g2.bx.psu.edu/view/fabio/honto.

Identifiants

pubmed: 36440918
pii: 6849517
doi: 10.1093/bioinformatics/btac763
pmc: PMC9805585
pii:
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : NCI NIH HHS
ID : U24 CA231877
Pays : United States
Organisme : NHGRI NIH HHS
ID : U24 HG006620
Pays : United States

Informations de copyright

© The Author(s) 2022. Published by Oxford University Press.

Références

Genome Biol. 2014 Feb 20;15(2):403
pubmed: 25001293
Nucleic Acids Res. 2021 Jan 8;49(D1):D605-D612
pubmed: 33237311
Nucleic Acids Res. 2022 Apr 21;:
pubmed: 35446428
Sci Rep. 2022 Jun 13;12(1):9757
pubmed: 35697749
PLoS One. 2013;8(3):e57707
pubmed: 23483922

Auteurs

Nicola Apollonio (N)

Institute for Applied Mathematics "Mauro Picone", National Research Council of Italy, Rome 00185, Italy.

Daniel Blankenberg (D)

Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA.

Fabio Cumbo (F)

Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA.

Paolo Giulio Franciosa (PG)

Department of Statistical Science, University of Rome "La Sapienza", Rome 00185, Italy.

Daniele Santoni (D)

Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council of Italy, Rome 00185, Italy.

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Classifications MeSH