Gene regulation inference from single-cell RNA-seq data with linear differential equations and velocity inference
- PMID: 33026066
- DOI: 10.1093/bioinformatics/btaa576
Gene regulation inference from single-cell RNA-seq data with linear differential equations and velocity inference
Abstract
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.
© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
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