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

Zhijian Li; James S Nagai; Christoph Kuppe; Rafael Kramann; Ivan G Costa

doi:10.1093/bioadv/vbad003

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

Bioinform Adv. 2023 Jan 12;3(1):vbad003. doi: 10.1093/bioadv/vbad003. eCollection 2023.

Authors

Zhijian Li¹, James S Nagai¹, Christoph Kuppe^{2

3}, Rafael Kramann^{2

3

4}, Ivan G Costa¹

Affiliations

¹ Institute for Computational Genomics, Joint Research Center for Computational Biomedicine, RWTH Aachen University Medical School, Aachen 52062, Germany.
² Institute of Experimental Medicine and Systems Biology, RWTH Aachen University, Aachen 52062, Germany.
³ Division of Nephrology and Clinical Immunology, RWTH Aachen University, Aachen 52062, Germany.
⁴ Department of Internal Medicine, Nephrology and Transplantation, Erasmus Medical Center, Rotterdam 3042, The Netherlands.

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.