SPAC: A Python Package for Spatial Single-Cell Analysis of Multiplexed Imaging

Multiplexed immunofluorescence microscopy captures detailed, spatially resolved measurements of multiple biomarkers simultaneously.These measurements reveal tissue composition and cellular interactions in situ at the single-cell level.The growing scale and dimensional complexity of these datasets demand reproducible, comprehensive, and user-friendly computational tools.To address this need, we developed SPAC (SPAtial single-Cell analysis), a Python-based package and a corresponding Shiny application within an integrated, modular SPAC ecosystem designed specifically for biologists without extensive coding expertise.Following image segmentation and extraction of spatially resolved single-cell data, SPAC streamlines downstream phenotyping and spatial analysis, facilitating the characterization of cellular heterogeneity and spatial organization within tissues.Through scalable performance, specialized spatial statistics, highly customizable visualizations, and seamless workflows from dataset to insights, SPAC significantly lowers the barriers to sophisticated spatial analyses.