SpaCEy links spatial tissue patterns to clinical outcomes using explainable graph neural networks

Researchers developed SpaCEy, an explainable graph neural network that analyzes spatial proteomics data to identify tissue patterns linked to clinical outcomes like survival and disease progression, without relying on predefined cell types. Applied to lung and breast cancer datasets, SpaCEy effectively stratifies patients and reveals relevant molecular markers and spatial relationships.