07-25, 12:05–12:13 (US/Eastern), Tomatis Auditorium
State-of-the-art technologies such as PhenoCycler, IMC, CosMx, 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 the context of their native tissue environments. However, the experimental design choice of which spatial domains or region of interests (ROI) will be imaged can greatly impact the interpretation of spatial quantifications. That is, spatial relationships identified in one ROI may not be applicable in other ROIs. To address this challenge, we introduce Kontextual, a method which considers alternative contexts for the evaluation of spatial relationships. These contexts may represent landmarks, spatial domains, or groups of functionally similar cells which are consistent across ROIs. By modelling spatial relationships between cells relative to these contexts, Kontextual produces robust spatial quantifications that are not confounded by the ROI selected. We applied Kontextual to a MIBI-TOF and Xenium breast cancer dataset and identified biologically relevant relationships which were consistent across ROIs. Furthermore, we performed comparison with other spatial and non-spatial features and observed that Kontextual was better suited for prediction of patient prognosis. These results suggest Kontextual is a useful approach to overcome the challenges in analysis of high throughput spatial omics across patient cohorts.
