  Multiplexed imaging data are revolutionizing our understanding of the
composition and organization of tissues and tumors. A critical aspect of such
tissue profiling is quantifying the spatial relationship relationships among
cells at different scales from the interaction of neighboring cells to
recurrent communities of cells of multiple types. This often involves
statistical analysis of 10^7 or more cells in which up to 100 biomolecules
(commonly proteins) have been measured. While software tools currently cater to
the analysis of spatial transcriptomics data, there remains a need for toolkits
explicitly tailored to the complexities of multiplexed imaging data including
the need to seamlessly integrate image visualization with data analysis and
exploration. We introduce SCIMAP, a Python package specifically crafted to
address these challenges. With SCIMAP, users can efficiently preprocess,
analyze, and visualize large datasets, facilitating the exploration of spatial
relationships and their statistical significance. SCIMAP's modular design
enables the integration of new algorithms, enhancing its capabilities for
spatial analysis.
