GRISLI

GRISLI infers gene regulatory networks (GRNs) from single-cell RNA sequencing (scRNA-seq) data to capture temporal dynamics in processes such as cell differentiation and the cell cycle.

Key Features:

• Velocity vector field inference: Infers a velocity vector field in the space of scRNA-seq data to capture dynamic profiles of individual cells.
• Linear ordinary differential equations (ODEs): Models gene expression dynamics using linear ODEs to relate transcriptional changes to regulatory interactions.
• Sparse regression procedure: Employs sparse regression to reconstruct GRNs, focusing on key regulatory interactions while reducing spurious connections.
• Comparative performance: Demonstrates performance advantages over recently developed state-of-the-art methods for GRN inference from scRNA-seq data.

Scientific Applications:

• Cell differentiation: Infers regulatory programs driving lineage progression from single-cell transcriptomes.
• Cell cycle: Captures regulatory dynamics associated with cell cycle progression at single-cell resolution.
• Time-dependent biological processes: Analyzes other temporal processes where gene regulation changes over time in single cells.
• Developmental biology: Elucidates GRNs underlying developmental transitions and timing.
• Cancer research: Investigates dysregulated regulatory interactions and dynamic programs in cancer cell populations.
• Regenerative medicine: Studies regulatory mechanisms relevant to cell fate decisions and tissue regeneration.

Methodology:

Infers a velocity vector field from scRNA-seq data, models dynamics with linear ODEs, and applies sparse regression to reconstruct gene regulatory networks.