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

MOTIVATION: Single-cell RNA sequencing (scRNA-seq) offers new possibilities to infer gene regulatory network (GRNs) for biological processes involving a notion of time, such as cell differentiation or cell cycles. It also raises many challenges due to the destructive measurements inherent to the technology.
RESULTS: In this work, we propose a new method named GRISLI for de novo GRN inference from scRNA-seq data. GRISLI infers a velocity vector field in the space of scRNA-seq data from profiles of individual cells, and models the dynamics of cell trajectories with a linear ordinary differential equation to reconstruct the underlying GRN with a sparse regression procedure. We show on real data that GRISLI outperforms a recently proposed state-of-the-art method for GRN reconstruction from scRNA-seq data.
AVAILABILITY AND IMPLEMENTATION: The MATLAB code of GRISLI is available at: https://github.com/PCAubin/GRISLI. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.