On the consistent and scalable detection of spatial patterns
Applications
2026-02-04 v1 Quantitative Methods
Methodology
Abstract
Detecting spatial patterns is fundamental to scientific discovery, yet current methods lack statistical consensus and face computational barriers when applied to large-scale spatial omics datasets. We unify major approaches through a single quadratic form and derive general consistency conditions. We reveal that several widely used methods, including Moran's I, are inconsistent, and propose scalable corrections. The resulting test enables robust pattern detection across millions of spatial locations and single-cell lineage-tracing datasets.
Cite
@article{arxiv.2602.02825,
title = {On the consistent and scalable detection of spatial patterns},
author = {Jiayu Su and Jun Hou Fung and Haoyu Wang and Dian Yang and David A. Knowles and Raul Rabadan},
journal= {arXiv preprint arXiv:2602.02825},
year = {2026}
}