用分层结构加速单细胞迁移建模，更准更快。
Multiscale Supervised Unbalanced Optimal Transport Flow Matching
- 基于数据分层结构设计无模拟的高效算法
- 计算开销降低，轨迹推断更准确且生物合理
- 适合有谱系先验的大规模单细胞数据分析
不平衡最优传输（UOT）为单细胞动态变化和出生-死亡过程提供了理论框架，但其高计算成本限制了在大规模数据集上的应用。尽管单细胞数据常包含层次化注释和已知的转移先验，现有UOT近似方法很少利用这种多尺度结构或先验知识。我们提出多尺度监督不平衡最优传输流匹配（MUST-FM），一种无需模拟的框架，通过利用数据的层次结构实现UOT的可扩展性。MUST-FM还支持可选的监督形式，可融入如细胞谱系等转移先验，以指导位移场与质量变化的学习。实验表明，MUST-FM在降低计算开销的同时，实现了鲁棒且具有生物学意义的轨迹推断，使全景单细胞图谱的动态建模成为可能。
原文摘要 · Abstract (English)
Unbalanced optimal transport (UOT) provides a principled framework for modeling single-cell transitions and birth-death dynamics, but its high computational cost limits scalability to large-scale datasets. Although single-cell data often contain hierarchical annotations and known transition priors, existing UOT approximations rarely exploit this multiscale structure or prior knowledge. We introduce Multiscale Supervised Unbalanced Optimal Transport Flow Matching (MUST-FM), a simulation-free framework that scales UOT by leveraging hierarchical data structure. MUST-FM further supports an optional supervised formulation that incorporates transition priors, such as cell lineages, to guide the learning of displacement fields and mass variations. Experiments show that MUST-FM reduces computational overhead while achieving robust and biologically meaningful trajectory inference, enabling dynamic modeling of atlas-scale single-cell datasets.
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