Deciphering Functional Regulation in Multi-Omics via Knowledge-Informed Low-Rank Factorization

Thomas M. Menino Convention & Exhibition Center 

Modern multi-omics technologies profile multiple molecular layers (e.g., genomic, transcriptomic, metabolomic) to elucidate biological mechanisms. Low-rank factorization effectively reveals coordinated signatures across these layers representing shared physiological or pathological signals. However, most existing models are purely data driven, prioritizing predictive power over biological priors such as annotated pathways or gene sets. We propose a novel framework leveraging this knowledge to guide the low-rank factorization of multi-omics data. Unlike standard methods that seek generic latent structures, our algorithm constrains factors to represent distinct up- and down-regulated functional sets. This approach allows for a mechanistic interpretation where each latent factor corresponds to a specific regulatory state across omics layers. Extensive simulations confirm improved factor specialization and recovery of signed pathways and supporting features. Benchmarking on multi-omic cancer cell line profiles (DepMap), we show that our knowledge-centric approach improves stability and yields superior biological insight into regulatory heterogeneity compared to leading competitors.

Multi-omics Integration

Matrix Factorization

Structured Penalization

Directional False Discovery Control

Pathway Analysis

Statistical Software 

Section on Statistics in Genomics and Genetics