提出一步推断方法，加速单细胞动态演化建模
WFR-MFM: One-Step Inference for Dynamic Unbalanced Optimal Transport
- 用平均速度与增殖场统一建模运输与质量变化
- 推理速度比基线快数量级，精度仍高
- 适合大规模扰动响应预测场景
从有限观测重建动态演化是单细胞生物学中的基础挑战。动态非平衡最优传输为建模耦合传输与质量变化提供了合理框架。然而，现有方法依赖推断时的轨迹模拟，成为可扩展应用的关键瓶颈。本文提出一种均值流框架，通过平均速度场与质量增长场，总结任意时间区间内的传输与质量增长动态，实现无需轨迹模拟的一步生成。在此基础上，构建了基于Wasserstein-Fisher-Rao几何的均值流匹配方法（WFR-MFM）。在合成与真实单细胞RNA测序数据集上，WFR-MFM的推理速度较多种基线提升数量级，同时保持高预测精度，并可在含数千种条件的大规模合成数据上高效进行扰动响应预测。
原文摘要 · Abstract (English)
Reconstructing dynamical evolution from limited observations is a fundamental challenge in single-cell biology, where dynamic unbalanced optimal transport provides a principled framework for modeling coupled transport and mass variation. However, existing approaches rely on trajectory simulation at inference time, making inference a key bottleneck for scalable applications. In this work, we propose a mean-flow framework for unbalanced flow matching that summarizes both transport and mass-growth dynamics over arbitrary time intervals using mean velocity and mass-growth fields, enabling fast one-step generation without trajectory simulation. To solve dynamic unbalanced optimal transport under the Wasserstein-Fisher-Rao geometry, we further build on this framework to develop Wasserstein-Fisher-Rao Mean Flow Matching (WFR-MFM). Across synthetic and real single-cell RNA sequencing datasets, WFR-MFM achieves orders-of-magnitude faster inference than a range of existing baselines while maintaining high predictive accuracy, and enables efficient perturbation response prediction on large synthetic datasets with thousands of conditions.
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