Deep generative modeling of transcriptional dynamics for RNA velocity analysis in single cells.

ABSTRACT: RNA velocity has been rapidly adopted to guide interpretation of transcriptional dynamics in snapshot single-cell data; however, current approaches for estimating RNA velocity lack effective strategies for quantifying uncertainty and determining the overall applicability to the system of interest. Here, we present veloVI (velocity variational inference), a deep generative modeling framework for estimating RNA velocity. veloVI learns a gene-specific dynamical model of RNA metabolism and provides a transcriptome-wide quantification of velocity uncertainty. We show that veloVI compares favorably to previous approaches with respect to goodness of fit, consistency across transcriptionally similar cells and stability across preprocessing pipelines for quantifying RNA abundance. Further, we demon

SUBMITTER: Gayoso A 

PROVIDER: S-EPMC10776389 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature