Inferring single-cell transcriptomic dynamics with structured latent gene expression dynamics.

ABSTRACT: Gene expression dynamics provide directional information for trajectory inference from single-cell RNA sequencing data. Traditional approaches compute RNA velocity using strict modeling assumptions about transcription and splicing of RNA. This can fail in scenarios where multiple lineages have distinct gene dynamics or where rates of transcription and splicing are time dependent. We present "LatentVelo," an approach to compute a low-dimensional representation of gene dynamics with deep learning. LatentVelo embeds cells into a latent space with a variational autoencoder and models differentiation dynamics on this "dynamics-based" latent space with neural ordinary differential equations. LatentVelo infers a latent regulatory state that controls the dynamics of an individual cell to model mul

SUBMITTER: Farrell S 

PROVIDER: S-EPMC10545944 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature