Spatial analysis for highly multiplexed imaging data to identify tissue microenvironments

Highly multiplexed in situ imaging cytometry assays have made it possible to study the spatial organization of numerous cell types simultaneously. We have addressed the challenge of quantifying complex multi-cellular relationships by proposing a statistical method which clusters local indicators of spatial association. Our approach successfully identifies distinct tissue architectures in datasets generated from three state-of-the-art high-parameter assays demonstrating its value in summarizing the information-rich data generated from these technologies.

Automated summary

Highly multiplexed in situ imaging cytometry assays enable simultaneous study of spatial organization of multiple cell types. We propose a statistical method that clusters local indicators of spatial association to quantify complex multi-cellular relationships. Our approach successfully identifies distinct tissue architectures in datasets from state-of-the-art high-parameter assays, demonstrating its value in summarizing information-rich data generated from these technologies.