Gene regulation inference from single-cell RNA-seq data with linear differential equations and velocity inference
Abstract
Single-cell RNA sequencing (scRNA-seq) offers new possibilities to infer gene regulation networks
(GRN) 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. 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 data, 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.