Multi-scale and multi-context interpretable mapping of cell states across heterogeneous spatial samples.

There is a growing demand for methods that can effectively align and compare spatial data in the absence of obvious visual correspondence. To address this challenge, we developed an interpretable cell mapping strategy based on solving a Linear Assignment Problem (LAP) where the total cost is computed by considering cells and their niches. We demonstrate that our approach outperforms other methods at capturing the spatial context of cells in synthetic and real data sets. The flexibility of our implementation enhances the interpretability of mapping and allows for accurate cell mapping across samples, technologies, resolutions, developmental and regenerative time. We show spatiotemporal decoupling of cells during development and patient level sub-populations in In Situ Mass Cytometry (IMC) cancer data sets. Our interpretable mapping approach facilitates systemic comparison and analysis of heterogeneous spatial data. We provide a flexible framework for researchers to tailor their analysis to the specific biological and research context.