Gene regulation inference from single-cell RNA-seq data with linear differential equations and velocity inference

Area of Science:

• Computational biology
• Genomics
• Systems biology

Background:

• Single-cell RNA sequencing (scRNA-seq) enables the study of gene regulatory networks (GRNs) in dynamic biological processes like cell differentiation.
• scRNA-seq presents challenges for GRN inference due to inherent measurement limitations.

Purpose of the Study:

• To introduce GRISLI, a novel computational method for de novo GRN inference from scRNA-seq data.
• To reconstruct GRNs by modeling cell trajectory dynamics.

Main Methods:

• GRISLI infers a velocity vector field from scRNA-seq data.
• Cell trajectory dynamics are modeled using linear ordinary differential equations.
• GRNs are reconstructed via a sparse regression procedure.

Main Results:

• GRISLI successfully reconstructs GRNs from scRNA-seq data.
• The proposed method demonstrates superior performance compared to a state-of-the-art GRN inference technique on real-world data.

Conclusions:

• GRISLI offers a robust approach for GRN inference from scRNA-seq data.
• The method effectively captures the dynamics of biological processes at the single-cell level.