An Analytical Neighborhood Enrichment Score for Spatial Omics

Abstract

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.