Dataset Information
ScMEGA: single-cell multi-omic enhancer-based gene regulatory network inference.
Ontology highlight
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.
SUBMITTER: Li Z
PROVIDER: S-EPMC9853317 | biostudies-literature | 2023
REPOSITORIES: biostudies-literature
Publications
scMEGA: single-cell multi-omic enhancer-based gene regulatory network inference.
Bioinformatics advances 20230112 1
<h4>Summary</h4>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 mec ...[more]
