SpatialQPFs: An R package for deciphering cell-cell spatial relationship

Abstract

ABSTRACTUnderstanding the spatial dynamics within tissue microenvironments is crucial for deciphering cellular interactions and molecular signaling in living systems. These spatial characteristics govern cell distribution, extracellular matrix components, and signaling molecules, influencing local biochemical and biophysical conditions. Decoding these features offers insights into physiological processes, disease progression, and clinical outcomes. By elucidating spatial relationships between cell types, researchers uncover tissue architecture, cell communication networks, and microenvironment dynamics, aiding in the identification of biomarkers and therapeutic targets. Digital pathology imaging, including Hematoxylin and Eosin (H&E) staining, provides high-resolution histological information that offer intricate insights into cell-cell spatial relationships with greater details. However, current methods for capturing cell-cell spatial interactions are constrained by either methodological scopes or implementations restricted to script-level access. This limitation undermines generalizability and standardization, crucial for ensuring reproducibility. To address these limitations, we introduceSpatialQPFs, an extendable R package designed for extraction of interpretable spatial features from digital pathology images. By leveraging segmented cell information, our package provides researchers with a comprehensive toolkit for applying a range of spatial statistical methods within a stochastic process framework which includes analysis of point pattern data, areal data, and geostatistical data. This allows for a thorough analysis of cell spatial relationships, enhancing the depth and accuracy of spatial insights derived from the tissue, thereby empowering researchers to conduct comprehensive spatial analyses efficiently and reproducibly.