Pathway-Aware Low-Rank Factorization and Regression for Interpretable Multi-Omics Analysis

Music City Center 

Multi-omics studies now profile complementary molecular layers -genome, transcriptome, proteome, and metabolome- in the same biospecimens, generating massive matrices whose joint structure encodes biological regulation. Low-rank factor models are a proven tool for distilling such high-dimensional data into interpretable molecular modules, yet current approaches typically analyze one omics layer at a time or look for interactions between pairs of them. This omission sacrifices both statistical power and biological plausibility.‎
We propose an advanced matrix factorization framework that seamlessly integrates overlapping pathway annotations while co-decomposing multiple omics matrices. Methodological novelties include (i) an interaction-aware group sparsity penalty that encourages factors to respect partially overlapping pathways defined for each omics layer and induces sign consistency on every selected pathway, and (ii) a factor-level false discovery rate control strategy based on stability selection, delivering finite-sample guarantees on module reproducibility while balancing the contribution of each view.‎
Through extensive simulations reflecting realistic pathway overlap, our method improves estimation efficiency.‎
An open-source R implementation built on high-performance C++ (Armadillo) back-end facilitates deployment to single-omics, multi-omics, or phenotype-association studies, and the framework naturally extends to multivariate regression for overlapping feature and outcome selection. By embedding pathway knowledge into multi-omics factorization, our approach advances both interpretability and statistical power in contemporary molecular biology.‎

LOW-RANK

FACTOR ANALISIS

OVERLAPPING CLUSTERING

PATHWAY ANNOTATION

PENALIZATION

OPTIMIZATION