scMomentum

scMomentum infers gene regulatory networks (GRNs) and reconstructs developmental energy landscapes from single-cell RNA sequencing (RNA-seq) data to characterize cell-type-specific regulatory mechanisms.

Key Features:

• Model-Based, Data-Driven Formulation: Employs a model-based, data-driven approach to predict gene regulatory networks and developmental energy landscapes directly from single-cell transcriptomic data.
• Computational Efficiency and Scalability: Optimized for computational efficiency and scalability to accommodate large-scale single-cell genomics datasets.
• Network Structure and Biological Interpretation: Captures structured relationships between genes to enable interpretation of cell-type-specific regulatory mechanisms and energy dynamics.

Scientific Applications:

• Cell Clustering and Annotation: Links cell clusters to underlying gene regulatory networks to inform cell-type-specific functions and states.
• Trajectory Inference: Reconstructs developmental energy landscapes to support inference of cellular differentiation trajectories.
• Regulatory Mechanism Inference without Temporal or Perturbation Experiments: Enables inference of regulatory mechanisms from single-cell RNA-seq data without requiring temporal sampling or experimental perturbations.

Methodology:

Applies a model-based, data-driven framework that leverages single-cell RNA-seq transcriptomic data to model gene interactions, predict gene regulatory networks, and reconstruct energy landscapes by integrating transcriptomic information to reflect network structure and cellular energy dynamics.