multiScaleAC: Cell-Cell interaction with Moran's I as a function of kernel bandwidth

作者：Alex C. Soupir, Mitchell Hayes, Brandon J. Manley, Xuefeng Wang, Julia Wrobel, Lauren C. Peres, Brooke L. Fridley · 发表于：bioRxiv (Cold Spring Harbor Laboratory) · 年份：2026 · DOI：10.64898/2026.07.14.738306 · 研究领域：Single-cell and spatial transcriptomics、Cancer Immunotherapy and Biomarkers、Ferroptosis and cancer prognosis

Over the last decade, spatial transcriptomic technology has transformed our understanding of tissue architecture including cell-cell interactions within the tumor immune microenvironment. A specific use-case of increasing interest is leveraging the spatial statistical relationship of genes whose protein products are known to be involved in ligand-receptor interactions. One methodological limitation of this approach has been the requirement to choose one radius around a cell as a parameter that can come with selection biases. Rather, interactions between cells vary in strength across a range of spatial scales that single-radius choice may miss. To fill this gap we developed `multiScaleAC` to extended Moran's I, a correlation measure that accounts for locations of values, by employing a Gaussian kernel applied to locations and varying the bandwidth parameter h. The resulting Moranis I\left(h\right) then can be compared between samples using functional data analysis. In the current study, we used simulations to show that our framework has well controlled Type I error due to the use of permutations for assessing significant interactions. We also demonstrate that `multiScaleAC` has high statistical power to identify a significant interaction when a true interaction is simulated (1.00 at bandwidths greater than 5) and increasing power as bandwidth increases when negative interaction is simulated. We found `multiScaleAC` largely captures similar significant ligand-receptor profiles ...