Adaptive spatial blocking for scalable clustering inference with applications to high-throughput spatial proteomics

2026-06-10

AI Summary

This study addresses the high computational cost of large-scale spatial point pattern clustering inference, which hinders its application to high-throughput spatial proteomics data. The authors propose an efficient testing framework based on adaptive spatial tiling: by extracting non-overlapping local tiles that satisfy constraints on both point count and geometric shape, and combining Ripley’s K-function with asymptotic normal approximation and evidence aggregation across multiple tiles, the method enables scalable clustering inference and rapid p-value computation. While preserving statistical power, the approach achieves substantial gains in computational efficiency. It successfully detects spatial clustering of plasma cells and their co-localization with macrophages in both simulated data and real human gut spatial proteomics datasets, demonstrating strong scalability and practical utility.

Abstract

Ripley's K-function is a widely used spatial summary statistic for assessing clustering in point patterns. However, existing K-based methods can be computationally prohibitive for large-scale data, particularly in high-throughput spatial proteomics, because they rely on spatial information from all points in the image. To address this challenge, we propose a computationally efficient block-based testing framework that extracts disjoint local blocks from an image and aggregates clustering evidence across them. The proposed adaptive spatial blocking algorithm constructs blocks satisfying point-count and shape constraints, enabling scalable spatial clustering inference and fast p-value computation through an asymptotic normal approximation. Numerical studies demonstrate that the proposed method provides a favorable balance between statistical power and computational efficiency. In an application to healthy human intestine spatial proteomics data, our method detects strong spatial aggregation of plasma cells and colocalization between plasma cells and macrophages, while scaling favorably to large images.

Problem

• spatial clustering
• Ripley's K-function
• high-throughput spatial proteomics
• scalable inference
• computational efficiency

Innovation

• adaptive spatial blocking
• scalable clustering inference
• Ripley's K-function
• spatial proteomics
• asymptotic normal approximation