PROFET Predicts Continuous Gene Expression Dynamics from scRNA-seq Data to Elucidate Heterogeneity of Cancer Treatment Responses.

ABSTRACT: Single-cell RNA sequencing captures static snapshots of gene expression but lacks the ability to track continuous gene expression dynamics over time. To overcome this limitation, we developed PROFET (Particle-based Reconstruction Of generative Force-matched Expression Trajectories), a computational framework that reconstructs continuous, nonlinear single-cell gene expression trajectories from sparsely sampled scRNA-seq data. PROFET first generates particle flows between time-stamped samples using a novel Lipschitz-regularized gradient flow approach and then learns a global vector field for trajectory reconstruction using neural force-matching. The framework was developed using synthetic data simulating cell state transitions and subsequently validated on both mouse and human in vitro

SUBMITTER: Cheng YC 

PROVIDER: S-EPMC12236938 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature