# Introduction

Spatial relationship analysis aims to identify the interactions and spatial distribution patterns between cell types in spatial transcriptomics data. By deeply mining spatial transcriptomics data, we can reveal spatial relationships such as proximity, co-occurrence, and enrichment between cell types, helping to understand the spatial heterogeneity of tissue structure and function.

## **Algorithm recommendation**

&#x20;If you want quick results, it is recommended to try the Squidpy method first. Squidpy provides a variety of spatial relationship analysis methods, including neighborhood enrichment, co-occurrence analysis, and Ripley's statistics, suitable for comprehensive spatial relationship exploration. CRAWDAD provides distance-scale-based spatial relationship analysis, which can identify interaction relationships and spatial covariation trends between cell types, especially suitable for detecting significant cell proximity or avoidance phenomena.

## Reference

* dos Santos Peixoto, R., Miller, B. F., Brusko, M. A., Aihara, G., Atta, L., Anant, M., ... & Fan, J. (2025). Characterizing cell-type spatial relationships across length scales in spatially resolved omics data. *Nature Communications*, *16*(1), 350.
* Palla, G., Spitzer, H., Klein, M., Fischer, D., Schaar, A. C., Kuemmerle, L. B., ... & Theis, F. J. (2022). Squidpy: a scalable framework for spatial omics analysis. *Nature methods*, *19*(2), 171-178.
