RT-SRFM: A Transport-Growth-Fate Latent Dynamical Framework for Developmental Single-Cell Time Series

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ID: 332100
2026
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Abstract
Abstract Single-cell time-series data provide a powerful opportunity to model developmental progression, but existing methods often focus separately on state transition, population growth, or fate inference. Here, we present RT-SRFM, a staged latent dynamical framework for developmental single-cell transcriptomic time series. The framework integrates optimal-transport-guided velocity learning, decomposed population growth modeling, shape refinement, and supervised biological readouts within a shared latent space. It is trained through an eight-stage curriculum to improve local transport accuracy, mass calibration, fate consistency, and long-horizon rollout stability. We evaluate the model on zebrafish embryogenesis and an independent sea urchin dataset. Across both systems, it achieves competitive transport performance while improving population mass calibration and fate/segment prediction compared with representative optimal transport, neural dynamics, and fate-mapping baselines. Ablation experiments further show that shape refinement, mass calibration, growth decomposition, fate warmup, and multi-step rollout repair contribute to model stability and predictive performance. These results suggest that the proposed framework provides a unified and interpretable approach for modeling developmental dynamics from single-cell time-series snapshots.
Reference Key
openalex_W7221478735 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Yi Tian, Siqi Zhang, Jun Meng, Chengzhi Lv, Sun Changkai
Journal Bioinformatics advances
Year 2026
DOI
10.1093/bioadv/vbag293
URL
https://doi.org/10.1093/bioadv/vbag293
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