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14 reviewsAbstractThe advancement of highly multiplexed spatial technologies requires scalable meth‑ods that can leverage spatial information. We present MISTy, a fexible, scalable, and explainable machine learning framework for extracting relationships from any spatial omics data, from dozens to thousands of measured markers. MISTy builds multiple views focusing on diferent spatial or functional contexts to dissect diferent efects. We evaluated MISTy on in silico and breast cancer datasets measured by imaging mass cytometry and spatial transcriptomics. We estimated structural and functional interac‑tions coming from diferent spatial contexts in breast cancer and demonstrated how to relate MISTy’s results to clinical features.Keywords: Spatial omics, Multiplexed data, Machine learning, Intercellular signalingBackground
