空间蛋白质组学可以揭示肿瘤免疫微环境中免疫细胞的空间组织。将空间聚类的测量（例如 Ripley's K 或 Besag's L）与患者结果联系起来可能会提供重要的临床见解。然而，这些措施需要预先指定一个半径来量化聚类，但对于可能因上下文而异的最佳半径尚未达成共识。我们提出了一种空间综合测试（SPOT），它在一系列候选半径上进行这种分析。在每个半径处，SPOT 都会评估空间摘要和结果之间的关联，并针对混杂因素进行调整。然后，SPOT 使用柯西组合检验聚合半径上的结果，生成表征整体关联程度的综合 p 值。通过模拟，我们验证了 I 类错误率得到控制，并表明 SPOT 比替代方案更强大。我们还将 SPOT 应用于卵巢癌和肺癌研究。https://github.com/sarahsamorodnitsky/SPOT 提供了 R 包和教程。© 作者 2024。由牛津大学出版社出版。

Spatial proteomics can reveal the spatial organization of immune cells in the tumor immune microenvironment. Relating measures of spatial clustering, such as Ripley's K or Besag's L, to patient outcomes may offer important clinical insights. However, these measures require pre-specifying a radius in which to quantify clustering, yet no consensus exists on the optimal radius which may be context-specific.We propose a SPatial Omnibus Test (SPOT) which conducts this analysis across a range of candidate radii. At each radius, SPOT evaluates the association between the spatial summary and outcome, adjusting for confounders. SPOT then aggregates results across radii using the Cauchy combination test, yielding an omnibus p-value characterizing the overall degree of association. Using simulations, we verify that the type I error rate is controlled and show SPOT can be more powerful than alternatives. We also apply SPOT to an ovarian and lung cancer study.An R package and tutorial is provided at https://github.com/sarahsamorodnitsky/SPOT.© The Author(s) 2024. Published by Oxford University Press.