Statistical inference with a manifold-constrained RNA velocity model uncovers cell cycle speed modulations.

ABSTRACT: Across biological systems, cells undergo coordinated changes in gene expression, resulting in transcriptome dynamics that unfold within a low-dimensional manifold. While low-dimensional dynamics can be extracted using RNA velocity, these algorithms can be fragile and rely on heuristics lacking statistical control. Moreover, the estimated vector field is not dynamically consistent with the traversed gene expression manifold. To address these challenges, we introduce a Bayesian model of RNA velocity that couples velocity field and manifold estimation in a reformulated, unified framework, identifying the parameters of an explicit dynamical system. Focusing on the cell cycle, we implement VeloCycle to study gene regulation dynamics on one-dimensional periodic manifolds and validate its ability

SUBMITTER: Lederer AR 

PROVIDER: S-EPMC11621032 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature