BSNMani_ST: A Bayesian Model for Linking Spatial Transcriptomics Features to Patient Phenotypes at the Population Scale

Music City Center 

Spatial transcriptomics (ST) provides valuable insights into molecular and spatial features of tissues, but associating ST data with patient phenotypes at the population scale is challenging. We introduce BSNMani_ST, a Bayesian scalar-on-network regression model with manifold learning, designed to predict clinical outcomes by linking ST features to population phenotypes in a scalable and interpretable manner. We applied BSNMani_ST to spatial transcriptomics data from the Seattle Alzheimer's Disease Brain Cell Atlas, as well as a single-cell imaging mass spectrometry dataset of breast cancers. BSNMani_ST identified biologically relevant gene co-expression subnetworks. These subnetworks are enriched for neurogenesis, neuronal communication, and signaling pathways in the Brain Cell Atlas data and immune-related antigens, cytokeratin, and hormone receptor antigens in the breast cancer data. We also performed simulations using synthetic datasets with latent subnetworks, BSNMani_ST outperformed other competing methods. These results underscore its robustness in capturing population-level patterns while incorporating clinical context.