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 language | English |
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Article number | vbad003 |
Journal | Bioinformatics Advances |
Volume | 3 |
Issue number | 1 |
Early online date | 12 Jan 2023 |
DOIs | |
Publication status | Published - 2023 |
Bibliographical note
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