标题： 一种空间组学的解析邻域增强评分 显示英文标题

标题： An Analytical Neighborhood Enrichment Score for Spatial Omics

摘要： 邻域增强检验用于量化具有分类标签的空间点之间的空间富集和耗竭，这是空间组学中常见的一种数据类型。 传统上，该检验依赖于基于排列的蒙特卡洛方法，这对于大型数据集来说计算成本较高。 在本研究中，我们提出了一种该检验的修改版本，可以进行解析计算。 该解析版本在八个空间组学数据集上与传统基于蒙特卡洛的方法之间显示出最小皮尔逊相关性为0.95，但速度有了显著提升。 在大型Xenium数据集上的额外实验表明了该方法高效分析大规模数据的能力，使其成为分析空间组学数据的有用工具。 显示英文摘要

摘要： The neighborhood enrichment test is used to quantify spatial enrichment and depletion between spatial points with categorical labels, which is a common data type in spatial omics. Traditionally, this test relies on a permutation-based Monte Carlo approach, which tends to be computationally expensive for large datasets. In this study, we present a modified version of the test that can be computed analytically. This analytical version showed a minimum Pearson correlation of 0.95 with the conventional Monte Carlo-based method across eight spatial omics datasets, but with substantial speed-ups. Additional experiments on a large Xenium dataset demonstrated the method's ability to efficiently analyze large-scale data, making it a valuable tool for analyzing spatial omics data.