C-S.B.29: Phenotype-driven parallel embedding for microbiome multi-omic data integration
Authors
DAP Consortium
Elhanan Borenstein
Keywords
Microbiome, Multi-omic integration
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The human microbiome is a key determinant of health and disease, but most reported associations remain descriptive and lack a system-level view. Multi-omic profiling can provide such a view, yet integration is challenging because omics differ in scale, structure, and biological meaning. Existing embedding-based approaches often either have limited predictive power or collapse all omics into a single latent space, losing omic-specific information.
We introduce PAPRICA (Phenotype-Aware Parallel Representation for Integrative omiC Analysis), a parallel encoder-decoder framework that embeds each omic in its own latent space while jointly modeling cross-omic and phenotype-associated relationships. PAPRICA is trained to reconstruct each omic, align samples across omic-specific latent spaces, and capture variation related to continuous phenotypes such as fecal calprotectin in IBD.
Across three multi-omic microbiome datasets, PAPRICA outperformed alternative integration models in both cross-omic prediction and phenotype prediction. These results suggest that PAPRICA effectively preserves omic-specific signals while capturing shared and phenotype-relevant structure, providing a flexible framework for microbiome multi-omic integration.
Co-authors: Dap Consortium, Elhanan Borenstein
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