超越连续性：从单细胞快照无模拟重建离散分支动力学
Beyond Continuity: Simulation-free Reconstruction of Discrete Branching Dynamics from Single-cell Snapshots
AI总结：
针对单细胞快照数据中随机性和非保守质量动态（如细胞增殖和凋亡）的挑战，提出无模拟框架Unbalanced Schrödinger Bridge (USB)，通过离散分支薛定谔桥问题建模单细胞分辨率的跳跃式生灭动态，实现高效轨迹重建与离散模拟。
摘要：
从破坏性快照推断细胞轨迹因随机性和非保守质量动态（如细胞增殖和凋亡）的挑战而复杂化。现有的不平衡最优传输（OT）方法将质量视为连续流体，在群体水平进行推断。然而，这种宏观视角往往无法捕捉单细胞分辨率下生灭事件的离散跳跃性质，而这对于理解谱系分支和命运决定至关重要。我们提出无模拟框架Unbalanced Schrödinger Bridge (USB)，用于学习底层动态，有效整合随机和非平衡效应，并在单细胞分辨率下建模离散、跳跃式的生灭动态。理论上，USB为分支薛定谔桥（BSB）问题提供了可处理的解，给出了严格的微观解释，其中单个细胞同时经历布朗运动和离散生灭跳跃。技术上，该方法通过引入无模拟训练目标实现高效求解器，有效扩展到高维组学数据。实验上，我们在模拟和真实数据集上证明，USB不仅达到优于或可比于确定性基线的轨迹重建性能，而且独特地实现了单细胞分辨率下生灭动态的真实离散模拟。
英文摘要：
Inferring cellular trajectories from destructive snapshots is complicated by the challenges of stochasticity and non-conservative mass dynamics such as cell proliferation and apoptosis. Existing unbalanced Optimal Transport (OT) methods treat mass as a continuous fluid, performing inference at the population level. However, this macroscopic view often fails to capture the discrete, jump-like nature of birth-death events at single-cell resolution, which is essential for understanding lineage branching and fate decisions. We present Unbalanced Schrödinger Bridge (USB), a simulation-free framework for learning underlying dynamics that effectively integrates both stochastic and unbalanced effects which also models the discrete, jump-like birth-death dynamics at single-cell resolution. Theoretically, USB provides a tractable solution to the Branching Schrödinger Bridge (BSB) problem, offering a rigorous microscopic interpretation where individual cells undergo both Brownian motion and discrete birth-death jumps. Technically, the method implements an efficient solver by introducing a simulation-free training objective that effectively scales to high-dimensional omics data. Empirically, we demonstrate on both simulated and real-world datasets that USB not only achieves trajectory reconstruction performance better than or comparable to deterministic baselines but also uniquely enables realistic discrete simulation of birth-death dynamics at single-cell resolution.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
