用组织结构和信号通路信息指导轨迹推断，让空间组学数据的动态分析更真实可靠。
ContextFlow: Context-Aware Flow Matching For Trajectory Inference From Spatial Omics Data
- 将局部组织结构与配体-受体互作整合为可解释的约束矩阵
- 在三个数据集上优于现有方法，轨迹既准确又符合生物学逻辑
- 适合研究发育、疾病进展等时空动态过程的生物学家
从纵向空间分辨组学数据中推断轨迹是理解发育、再生修复、疾病进展及治疗反应过程中组织结构与功能变化动态的基础。本文提出ContextFlow，一种融合先验知识的上下文感知流匹配框架，通过引入局部组织结构和配体-受体通信模式构建转移合理性矩阵，对最优传输目标进行正则化。该方法生成的轨迹不仅统计上一致，且具备生物学意义，可推广用于建模多时间点空间组学数据中的时空动态。在三个数据集上的评估显示，ContextFlow在多种定量与定性指标（包括推断精度和生物一致性）上持续优于当前最优流匹配方法。代码已开源：https://github.com/santanurathod/ContextFlow
原文摘要 · Abstract (English)
Inferring trajectories from longitudinal spatially-resolved omics data is fundamental to understanding the dynamics of structural and functional tissue changes in development, regeneration and repair, disease progression, and response to treatment. We propose ContextFlow, a novel context-aware flow matching framework that incorporates prior knowledge to guide the inference of structural tissue dynamics from spatially resolved omics data. Specifically, ContextFlow integrates local tissue organization and ligand-receptor communication patterns into a transition plausibility matrix that regularizes the optimal transport objective. By embedding these contextual constraints, ContextFlow generates trajectories that are not only statistically consistent but also biologically meaningful, making it a generalizable framework for modeling spatiotemporal dynamics from longitudinal, spatially resolved omics data. Evaluated on three datasets, ContextFlow consistently outperforms state-of-the-art flow matching methods across multiple quantitative and qualitative metrics of inference accuracy and biological coherence. Our code is available at: \href{https://github.com/santanurathod/ContextFlow}{ContextFlow}
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。