Abstract
Abstract
Quantifying interactions in biological tissues requires determining whether an interaction exists, the distance over which it operates, and its strength. Existing spatial-statistical approaches can characterize spatial organization but often require interaction range or interaction models to be specified a priori, making them less suited to settings where these quantities are themselves unknown. Here, we present a practical framework that recovers the presence, range, and association strength of spatial bias toward a tissue structure directly from segmented histology. The framework combines four complementary statistics, the resource selection function, nearest-structure distance distributions, the cross J-function, and the radial distribution function, with specimen-specific reference states that preserve tissue stromal architecture while spanning a continuum of known spatial biases. Across controlled
in silico
tissues, we show that tissue stromal architecture imposes geometric limits on the range of recoverable interactions and that event fraction independently constrains statistical sensitivity. These spatial associations establish fundamental bounds on inference and demonstrate that spatial statistics must be interpreted relative to tissue-specific reference states rather than absolute scales. We apply the framework to proliferating tumor cells relative to CAF-rich stroma during tyrosine kinase inhibitor therapy in an ALK+ NSCLC model and identify a strong stroma-associated proliferation bias extending approximately 30-60 µm. The framework is broadly applicable to any histological setting in which events and reference structures can be defined, providing a practical approach for quantifying localized interactions directly from tissue stromal architecture.