Linking spatial omics to patient phenotypes at the population scale by BSNMani: Bayesian scalar-on-network regression with manifold learning.

Spatial omics enables the integration of high-dimensional molecular organization with clinical outcomes, yet incorporating spatial single-cell information into predictive models at the population scale remains challenging. Here, we adapted BSNMani, Bayesian scalar-on-network regression with manifold learning, to integrate subject-specific, spatially informed co-expression networks into clinical prediction. The benchmark comparison showed that the feature selection by BSNMani significantly outperformed Elastic Net and Lasso methods for prediction performance. On SEA-AD MERFISH transcriptomics cohort, BSNMani framework achieved an accuracy of 0.74 for Alzheimer's disease (AD) prediction and revealed four distinct gene-gene co-expression subnetworks with clear biological relevance, such as glutamatergic synapses and neurogenesis. Furthermore, BSNMani achieved a good survival prediction of another breast cancer cohort measured by Imaging Mass Cytometry (IMC) (C-index=0.74) with 2 subnetworks being identified. Furthermore, BSNMani can also use cell-type-specific spatial omics data to enhance the granularity and better pinpoint biological patterns. In summary, BSNMani is a powerful tool that uses high-dimensional spatial omics data for clinical outcome prediction at the population scale across diverse disease settings, revealing deep biological insights while maintaining easy interpretation.