TempoBridge：基于最优传输耦合的源条件流匹配用于单细胞群体转变

中文摘要 AI 辅助

破坏性单细胞测量提供的是未配对的群体快照，而非同一细胞在不同条件下的观测。局部细胞状态和转变请求可能也不足以区分不同源群体的响应。我们引入了TempoBridge，一种用于时间、遗传和化学群体转变的通用源条件传输公式。源细胞初始化潜在传输并提供固定的经验群体摘要。速度场接收该摘要以及演化的细胞状态、流动时间和结构化转变描述符。小批量最优传输（OT）仅为条件流匹配训练路径提供耦合；推理既不需要目标表达，也不需要OT计算。在留出供体上，TempoBridge实现了0.129的能量距离，而scGen为0.144。在Seen 2/2下，遗传平均表达$L_2$误差为2.261，而scGPT-scratch为3.156。在留出化合物上，条件平均药物效应相关性为0.598，而CellFlow适配器为0.561。时间消融显示，在移除源上下文、用随机配对替换最优传输或用静态残差回归替换流匹配后，平均分布误差更高。总之，这些结果证明了通用源条件传输公式在留出供体、基因组合和化合物上的预测效用。

英文摘要

Destructive single-cell measurements provide unpaired population snapshots rather than observations of the same cells across conditions. Local cell states and transition requests may also be insufficient to distinguish responses across source populations. We introduce TempoBridge, a common source-conditioned transport formulation for temporal, genetic, and chemical population transitions. Source cells initialize latent transport and provide a fixed empirical population summary. The velocity field receives this summary alongside the evolving cell state, flow time, and a structured transition descriptor. Minibatch optimal transport (OT) supplies couplings only for conditional flow-matching training paths; inference requires neither target expression nor OT computation. On held-out donors, TempoBridge achieves an Energy distance of 0.129 versus 0.144 for scGen. Genetic mean-expression $L_2$ error is 2.261 versus 3.156 for scGPT-scratch under Seen 2/2. On held-out compounds, condition-averaged drug-effect correlation is 0.598 versus 0.561 for the CellFlow adapter. Temporal ablations show higher mean distributional error after removing source context, replacing optimal transport with random pairing, or replacing flow matching with static residual regression. Together, these results demonstrate the predictive utility of a common source-conditioned transport formulation across held-out donors, gene combinations, and compounds.

发表机构

• Universiti Sains Malaysia（马来西亚理科大学）
• The Chinese University of Hong Kong Shenzhen（香港中文大学（深圳））
• Shenzhen Loop Area Institute（深圳河套学院）
• Shenzhen Research Institute of Big Data（深圳大数据研究院）
• University of Electronic Science and Technology of China（电子科技大学）
• King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）

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