Comparison and optimization of cellular neighbor preference methods for quantitative tissue analysis

Studying the spatial distribution of cell types in tissues is essential for understanding their function in health and disease. A widely applied measure in spatial omics analysis is the pairwise neighbor preference of cell types, indicating whether two cell types frequently occur in close proximity to each other. While various neighbor preference methods exist, there is no clear guidance for selecting one over another. Here we present a comprehensive comparison of existing neighbor preference analysis methods. We systematically evaluate the method's underlying analytical steps and introduce COZI, a combination of analysis steps not previously described. We compare results from existing methods and COZI with respect to their ability to distinguish distinct tissue architectures and to recover neighbor preference directionality using two tissue simulations and two biological datasets. Overall, we delineate method-specific strengths and limitations and demonstrate that COZI provides sensitive and directional neighbor preference analysis for quantification of spatial data.