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

ABSTRACT: 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 gl

SUBMITTER: Liu T 

PROVIDER: S-EPMC12633620 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature