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.


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 Tutorials are available from

Supplementary information: Supplementary data are available at Bioinformatics Advances online.