Trajectory inference from single-cell genomics data with a process time model.

ABSTRACT: Single-cell transcriptomics experiments provide gene expression snapshots of heterogeneous cell populations across cell states. These snapshots have been used to infer trajectories and dynamic information even without intensive, time-series data by ordering cells according to gene expression similarity. However, while single-cell snapshots sometimes offer valuable insights into dynamic processes, current methods for ordering cells are limited by descriptive notions of "pseudotime" that lack intrinsic physical meaning. Instead of pseudotime, we propose inference of "process time" via a principled modeling approach to formulating trajectories and inferring latent variables corresponding to timing of cells subject to a biophysical process. Our implementation of this approach, called Chronocel

SUBMITTER: Fang M 

PROVIDER: S-EPMC11760028 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature