scMEGA: single-cell multi-omic enhancer-based gene regulatory network inference

Zhijian Li, James S. Nagai, Christoph Kuppe, Rafael Kramann, Ivan G. Costa*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

9 Citations (Scopus)
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Abstract

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.

Original languageEnglish
Article numbervbad003
JournalBioinformatics Advances
Volume3
Issue number1
Early online date12 Jan 2023
DOIs
Publication statusPublished - 2023

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