Key result
scMEGA is an R package that enables end-to-end analysis of single-cell multi-omics data for gene regulatory network inference, demonstrated in a case study of human myocardial infarction.
Why the study?
The growing availability of single-cell multi-omics data creates an opportunity to quantitatively characterize complex gene regulation mechanisms in dynamic biological processes.
scMEGA provides a novel computational pipeline for inferring gene regulatory networks from single-cell multi-omics data, which can be applied to study disease-driven cellular remodeling such as post-MI myofibroblast activation.
Should not yet alter post-MI fibrosis care; leaves open experimental validation of inferred regulatory networks.
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: online.
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Li et al. (2023) studied Myocardial infarction. scMEGA was evaluated. scMEGA is an R package that enables end-to-end analysis of single-cell multi-omics data for gene regulatory network inference, demonstrated in a case study of human myocardial infarction.
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