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
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
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