Nov 7, 2018

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

BioRxiv : the Preprint Server for Biology
Pierre-Cyril Aubin-Frankowski, Jean-Philippe Vert

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.

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Mentioned in this Paper

Sequence Determinations, RNA
Reconstructive Surgical Procedures
GRN
Sequence Determinations
Anatomical Space Structure
Cell Differentiation Process
Genetic Vectors
RNA, Small Cytoplasmic
Cell Cycle
Frontotemporal Dementia

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