MoReL: Multi-omics Relational Learning

TL;DR

MoReL introduces a deep Bayesian generative model utilizing fused Gromov-Wasserstein regularization to effectively infer molecular interactions from heterogeneous multi-omics data, improving alignment and prediction accuracy.

Contribution

It presents a novel multi-view graph inference method that incorporates optimal transport regularization for better integration of heterogeneous multi-omics data.

Findings

Enhanced interaction prediction accuracy over baselines

Effective integration of heterogeneous data types

Improved alignment of latent representations

Abstract

Multi-omics data analysis has the potential to discover hidden molecular interactions, revealing potential regulatory and/or signal transduction pathways for cellular processes of interest when studying life and disease systems. One of critical challenges when dealing with real-world multi-omics data is that they may manifest heterogeneous structures and data quality as often existing data may be collected from different subjects under different conditions for each type of omics data. We propose a novel deep Bayesian generative model to efficiently infer a multi-partite graph encoding molecular interactions across such heterogeneous views, using a fused Gromov-Wasserstein (FGW) regularization between latent representations of corresponding views for integrative analysis. With such an optimal transport regularization in the deep Bayesian generative model, it not only allows incorporating view-specific side information, either with graph-structured or unstructured data in different views, but also increases the model flexibility with the distribution-based regularization. This allows efficient alignment of heterogeneous latent variable distributions to derive reliable interaction predictions compared to the existing point-based graph embedding methods. Our experiments on several real-world datasets demonstrate enhanced performance of MoReL in inferring meaningful interactions compared to existing baselines.