标题： TopoLa：通过拓扑编码的潜在双曲几何增强单细胞和空间组学细胞表示的通用框架 显示英文标题

标题： TopoLa: A Universal Framework to Enhance Cell Representations for Single-cell and Spatial Omics through Topology-encoded Latent Hyperbolic Geometry

摘要： 最近细胞研究的进展表明，scRNA-seq能够表征细胞异质性，而空间转录组学揭示了基因表达的空间分布。细胞表示是这两个领域中的基本问题。在这里，我们提出了拓扑编码的潜在双曲几何（TopoLa），这是一种计算框架，通过捕捉细粒度的细胞间拓扑关系来增强细胞表示。该框架引入了一种新的度量标准，拓扑距离（TLd），它量化了潜在双曲空间中细胞之间的几何距离，更有效地捕捉了网络的拓扑结构。通过在其邻近细胞上执行卷积，可以显著增强细胞表示。在七个生物任务中的性能评估，包括scRNA-seq数据聚类和空间转录组学领域识别，显示TopoLa显著提高了几种最先进的模型的性能。这些结果强调了TopoLa的泛化能力和鲁棒性，确立了它作为推动生物学发现和计算方法的重要工具。 显示英文摘要

摘要： Recent advances in cellular research demonstrate that scRNA-seq characterizes cellular heterogeneity, while spatial transcriptomics reveals the spatial distribution of gene expression. Cell representation is the fundamental issue in the two fields. Here, we propose Topology-encoded Latent Hyperbolic Geometry (TopoLa), a computational framework enhancing cell representations by capturing fine-grained intercellular topological relationships. The framework introduces a new metric, TopoLa distance (TLd), which quantifies the geometric distance between cells within latent hyperbolic space, capturing the network's topological structure more effectively. With this framework, the cell representation can be enhanced considerably by performing convolution on its neighboring cells. Performance evaluation across seven biological tasks, including scRNA-seq data clustering and spatial transcriptomics domain identification, shows that TopoLa significantly improves the performance of several state-of-the-art models. These results underscore the generalizability and robustness of TopoLa, establishing it as a valuable tool for advancing both biological discovery and computational methodologies.