scMEGA: single-cell multi-omic enhancer-based gene regulatory network inference.
Journal
Bioinformatics advances
ISSN: 2635-0041
Titre abrégé: Bioinform Adv
Pays: England
ID NLM: 9918282081306676
Informations de publication
Date de publication:
2023
2023
Historique:
received:
18
10
2022
revised:
08
12
2022
accepted:
11
01
2023
entrez:
26
1
2023
pubmed:
27
1
2023
medline:
27
1
2023
Statut:
epublish
Résumé
The increasing availability of single-cell multi-omics data allows to quantitatively characterize gene regulation. We here describe scMEGA (Single-cell Multiomic Enhancer-based Gene Regulatory Network Inference) that enables an end-to-end analysis of multi-omics data for gene regulatory network inference including modalities integration, trajectory analysis, enhancer-to-promoter association, network analysis and visualization. This enables to study the complex gene regulation mechanisms for dynamic biological processes, such as cellular differentiation and disease-driven cellular remodeling. We provide a case study on gene regulatory networks controlling myofibroblast activation in human myocardial infarction. scMEGA is implemented in R, released under the MIT license and available from https://github.com/CostaLab/scMEGA. Tutorials are available from https://costalab.github.io/scMEGA. Supplementary data are available at
Identifiants
pubmed: 36698768
doi: 10.1093/bioadv/vbad003
pii: vbad003
pmc: PMC9853317
doi:
Types de publication
Journal Article
Langues
eng
Pagination
vbad003Informations de copyright
© The Author(s) 2023. Published by Oxford University Press.
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