DeepVelo: Deep Learning Extends RNA Velocity to Multi-Lineage Systems With Cell-Specific Kinetics

Authors

Haotian Cui
Hassaan Maan
Maria C Vladoiu
Jiao Zhang
Michael D Taylor
Bo Wang

Publication Date

1-19-2024

Journal

Genome Biology

DOI

10.1186/s13059-023-03148-9

PMID

38243313

PMCID

PMC10799431

PubMedCentral® Posted Date

1-19-2024

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes

Keywords

Gene Expression Profiling, Sequence Analysis, RNA, Deep Learning, RNA, Cell Differentiation, Single-Cell Analysis, RNA velocity, Single-cell RNA sequencing, Deep Learning, Development, Cancer

Abstract

Existing RNA velocity estimation methods strongly rely on predefined dynamics and cell-agnostic constant transcriptional kinetic rates, assumptions often violated in complex and heterogeneous single-cell RNA sequencing (scRNA-seq) data. Using a graph convolution network, DeepVelo overcomes these limitations by generalizing RNA velocity to cell populations containing time-dependent kinetics and multiple lineages. DeepVelo infers time-varying cellular rates of transcription, splicing, and degradation, recovers each cell's stage in the differentiation process, and detects functionally relevant driver genes regulating these processes. Application to various developmental and pathogenic processes demonstrates DeepVelo's capacity to study complex differentiation and lineage decision events in heterogeneous scRNA-seq data.

Comments

Associated Data