Dynamical Systems Model of RNA Velocity Improves Inference of Single-cell Trajectory, Pseudo-time and Gene Regulation.
J Mol Biol
; 434(15): 167606, 2022 08 15.
Article
em En
| MEDLINE
| ID: mdl-35489382
ABSTRACT
Recent development in inferring RNA velocity from single-cell RNA-seq opens up exciting new vista into developmental lineage and cellular dynamics. However, the estimated velocity only gives a snapshot of how the transcriptome instantaneously changes in individual cells, and it does not provide quantitative predictions and insights about the whole system. In this work, we develop RNA-ODE, a principled computational framework that extends RNA velocity to quantify systems level dynamics and improve single-cell data analysis. We model the gene expression dynamics by an ordinary differential equation (ODE) based formalism. Given a snapshot of gene expression at one time, RNA-ODE is able to predict and extrapolate the expression trajectory of each cell by solving the dynamic equations. Systematic experiments on simulations and on new data from developing brain demonstrate that RNA-ODE substantially improves many aspects of standard single-cell analysis. By leveraging temporal dynamics, RNA-ODE more accurately estimates cell state lineage and pseudo-time compared to previous state-of-the-art methods. It also infers gene regulatory networks and identifies influential genes whose expression changes can decide cell fate. We expect RNA-ODE to be a Swiss army knife that aids many facets of single-cell RNA-seq analysis.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
RNA
/
Regulação da Expressão Gênica
/
Redes Reguladoras de Genes
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Análise de Célula Única
Tipo de estudo:
Prognostic_studies
Idioma:
En
Revista:
J Mol Biol
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
Estados Unidos