scMEGA

scMEGA infers enhancer-based gene regulatory networks from single-cell multi-omics data by linking enhancers to promoters and analyzing cell trajectories to characterize gene regulation in dynamic biological processes.

Key Features:

• End-to-End Analysis: Integrates omic integration, trajectory analysis, enhancer-to-promoter association, network inference, and visualization within a single workflow.
• Modalities Integration: Combines multiple single-cell omics modalities to incorporate diverse molecular layers in regulatory inference.
• Trajectory Analysis: Orders cells along trajectories to study dynamic processes such as cellular differentiation and remodeling.
• Enhancer-to-Promoter Association: Links enhancers with their target promoters to identify cis-regulatory interactions underlying gene expression.
• Network Analysis and Visualization: Infers gene regulatory networks and enables analysis and visualization of complex regulatory interactions.

Scientific Applications:

• Dynamic biological processes: Characterizing gene regulatory programs during cellular differentiation and disease-driven cellular remodeling.
• Myofibroblast activation in human myocardial infarction: Inferring gene regulatory networks controlling myofibroblast activation in human myocardial infarction.

Methodology:

Integrates single-cell multi-omics data, links enhancers to target promoters, performs trajectory analysis, and infers and visualizes gene regulatory networks.