SPRM: spatial process and relationship modeling for multiplexed images.

Motivation: There has been tremendous recent growth both in technologies for measurement of many different markers in the same tissue and in resulting datasets (especially from projects such as HuBMAP and the Human Cell Atlas). Analysis of images in these datasets is often restricted to measuring the amount of each marker in each cell. While this is important, it ignores other information that is contained in tissue images. SPRM was therefore created for use in the HuBMAP image analysis pipelines and can be used for any spatial proteomics dataset. Results: It calculates a number of measures of image quality, including metrics for the quality of cell segmentation, and extracts many different types of cell features that give much richer characterization than just marker intensities per cell. Different feature types are used to cluster cells into potential cell types to view the tissue through these different lenses, and these are compared to expert annotations if provided in order to define cell subtypes. The package also constructs a cell adjacency matrix to characterize cell spatial distributions. Example analyses are provided in Supplementary Information. Availability and implementation: SPRM is available as python open source at https://github.com/hubmapconsortium/sprm and as a PyPI package.