Population-Scale Integration of Spatial Omics Networks for Clinical Prediction and Biological Discovery by SPIN

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 introduce SPIN (Spatial Predictive Integration Network), which integrates subject-specific, spatially informed co-expression or co-abundance networks for population-scale clinical prediction. Current implementation of SPIN includes three key modules: network construction, molecular-to-clinical prediction by Bayesian scalar-on-network regression with manifold learning (BSNMani), and visualization & interpretation module. In the SEA-AD MERFISH transcriptomics cohort, SPIN framework achieved an accuracy of 0.81 for dementia-status prediction and revealed gene-gene co-expression subnetworks with clear biological relevance, such as glutamatergic synapses- and neurogenesis-related subnetworks. SPIN also achieved robust survival prediction in a breast cancer single proteomics cohort (C-index=0.78), identifying two survival-associated spatial proteomic subnetworks. SPIN can further use cell-type-specific spatial omics data to enhance the granularity. In summary, SPIN is a valuable tool that uses high-dimensional spatial omics data for clinical outcome prediction at the population scale across diverse disease settings, revealing biological insights while maintaining interpretation. SPIN package is available at: https://github.com/lanagarmire/SPIN