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bioRxiv - (-) - [2025-05-08; online 2025-05-08]
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.
PubMed 40654897
DOI 10.1101/2025.05.02.651831
Crossref 10.1101/2025.05.02.651831
pmc: PMC12247973
pii: 2025.05.02.651831