Context is important! Identifying context aware spatial relationships with Kontextual.

Author(s): Farhan Ameen,Shila Ghazanfar,Ellis Patrick

Affiliation(s): University of Sydney



State-of-the-art technologies such as PhenoCycler, IMC, MERFISH, Xenium, and others can deeply phenotype cells in their native environment, providing a high throughput means to effectively quantify spatial relationships between diverse cell populations in their native tissue environments. However, the experimental design choice of which regions of a tissue will be imaged can greatly impact the interpretation of spatial quantifications. That is, spatial relationships identified in one region of interest may not be interpreted consistently across other regions. To address this challenge we introduce Kontextual, a method in our Statial Bioconductor package, which considers alternative frames of reference for contextualising spatial relationships. These contexts may represent landmarks, spatial domains, or groups of functionally similar cells which are consistent across regions. By modelling spatial relationships between cells relative to these contexts, Kontextual produces robust spatial quantifications that are not confounded by region selected. We demonstrate in spatial proteomics and spatial transcriptomics datasets that modelling spatial relationships this way is biologically meaningful. We also demonstrate how this approach can be used in a classification setting to improve prediction of patient prognosis or treatment response.