Learning stochastic dynamics and cell-fate landscapes from single-cell snapshots via optimal transport.

Authors

Juntan Liu, Peijie Zhou, Qing Nie, Chunhe Li

Published in

Science advances. Volume 12. Issue 37. Pages eaeb4205. Sep 11, 2026. Epub Sep 09, 2026.

Abstract

The temporal dynamics and stochasticity of gene expression are critical to cell fate decisions, yet integrating snapshot omics data across multiple time points remains a major challenge. Here, we introduce DiffusionOT, a dynamic machine learning framework that infers cellular trajectories from multi-time point single-cell transcriptomics by incorporating stochastic effects. DiffusionOT transforms stochastic differential equations into ordinary differential equations, using optimal transport and neural networks to solve a high-dimensional landscape model. Through an unsupervised learning of the stochastic force in the data, DiffusionOT allows robust inference of the underlying stochastic dynamics of cell-state transitions. The framework includes a stochastic trajectory analysis module for lineage tracing and a gene perturbation module for in silico knockout and overexpression experiments. Benchmarks on simulated and four real-world datasets, including a spatial Stereo-seq dataset, demonstrate DiffusionOT's accuracy and efficiency in inferring state-transition velocities, cellular trajectories, population growth, gene regulatory networks, and cell-fate landscape.

PMID:
42715336
Bibliographic data and abstract were imported from PubMed on 10 Sep 2026.

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