在大规模空间组学数据集中一致且可扩展的空间模式检测
Consistent and scalable detection and comparison of spatial patterns
AI总结：
本文提出了一种统一的空间模式检测方法，通过二次型统一主要方法并推导一致性条件，揭示了Moran's I等方法的不一致性，并提出了可扩展的修正方案，适用于大规模空间组学数据集。
摘要：
检测空间模式对于科学发现至关重要，但当前方法缺乏统计一致性，并在应用于大规模空间组学数据集时面临计算障碍。我们通过单一二次型统一了主要方法，并推导出通用的一致性条件。我们揭示了包括Moran's I在内的几种广泛使用的方法不一致，并提出了可扩展的修正方法。所得到的检验方法能够在数百万个空间位置和单细胞谱系追踪数据集中实现稳健的空间模式检测。
英文摘要：
Spatial omics maps molecular features within tissues to study their organization across biological contexts, including disease and development. Interpreting these maps requires distinguishing patterns from noise and comparing them across samples. Yet pattern detection remains unresolved, and comparisons based on regional summaries lose spatial detail. Here we show that spatial frequency provides a common basis for detecting and comparing these patterns. Our framework, SONIC, unifies spatial detection tests through a common quadratic form, establishes conditions for reliable detection and explains how kernel choice determines sensitivity to different spatial patterns. It also separates changes in spatial organization from changes in overall abundance without aligning tissues. Fast, memory-efficient algorithms make detection and comparison practical across large datasets. Applying SONIC, we identify plaque-associated responses in Alzheimer's disease and differences in the spatial expression of blood-clotting genes between glioma subtypes. We further construct a gene-centric atlas that traces changes in spatial expression through mouse and human embryogenesis and supports retrieval of related patterns across species. These results extend spatial analysis from identifying individual patterns to following their changes across anatomically different tissues. Together, SONIC establishes a shared language for integrating molecular maps across studies and technologies.
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