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0This research provides a comprehensive evaluation of computational methods used to analyze neighbor preference (NEP) in spatial biology. By deconstructing various tools like histoCAT, Squidpy, and Giotto, the authors identify a common three-step framework consisting of neighborhood definition, quantification, and scoring. The study reveals that many existing methods struggle to distinguish between subtle tissue architectures or fail to accurately capture directional cellular interactions. To address these limitations, the researchers introduce COZI, a novel approach using conditional z-scores to provide more sensitive and directional spatial analysis. Validated through both simulated datasets and biological samples of breast cancer and myocardial infarction, the work serves as a foundational guide for quantifying how cell types organize and interact within complex environments.
References:
Schiller C, Ibarra-Arellano M A, Bestak K, et al. Comparison and optimization of cellular neighbor preference methods for quantitative tissue analysis[J]. Nature Communications, 2026, 17(1): 3514.
