INLAomics for Scalable and Interpretable Spatial Multiomic Data Integration

Integrating spatial transcriptomics with antibody-based proteomics enables the investigation of biological regulation within intact tissue architecture. However, current approaches for spatial multi-omics integration often depend on dimensionality reduction or autoencoders, which disregard spatial context, limit interpretability, and face challenges with scalability. To address these limitations, we developed INLAomics, a multivariate hierarchical Bayesian framework that models protein abundance in tissue sections by leveraging histological features and latent spatial factors inferred from spatial transcriptomics data. INLAomics supports two key applications: (1) identifying spatial gene co-expression programs to build interpretable gene-protein networks, and (2) predicting spatial protein expression in tissues lacking proteomics measurements. Applied across diverse datasets, INLAomics reveals previously unrecognized gene-protein associations and achieves substantial improvements in protein prediction accuracy over models that treat each modality independently. The framework is both computationally efficient and biologically interpretable, offering a scalable solution for integrative analysis of spatial multi-omics data.