Contributor:
Li, Zhijian;
Nagai, James S;
Kuppe, Christoph;
Kramann, Rafael;
Costa, Ivan G
Published:
Oxford University Press (OUP), 2023
Published in:
Bioinformatics Advances, 3 (2023) 1
Language:
English
DOI:
10.1093/bioadv/vbad003
ISSN:
2635-0041
Origination:
Footnote:
Description:
Abstract Summary 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. Availability and implementation 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 information Supplementary data are available at Bioinformatics Advances online.