A Variational Information Bottleneck Approach to Multi-Omics Data Integration

TL;DR

This paper introduces a deep variational information bottleneck method for integrating incomplete multi-omics data, effectively capturing complex interactions and handling missing views to improve predictive performance.

Contribution

It presents a novel IB-based framework that models joint representations as products of marginals, enabling flexible learning from incomplete multi-view omics data.

Findings

Consistently outperforms state-of-the-art benchmarks

Effectively handles various view-missing patterns

Enhances data integration in biomedical research

Abstract

Integration of data from multiple omics techniques is becoming increasingly important in biomedical research. Due to non-uniformity and technical limitations in omics platforms, such integrative analyses on multiple omics, which we refer to as views, involve learning from incomplete observations with various view-missing patterns. This is challenging because i) complex interactions within and across observed views need to be properly addressed for optimal predictive power and ii) observations with various view-missing patterns need to be flexibly integrated. To address such challenges, we propose a deep variational information bottleneck (IB) approach for incomplete multi-view observations. Our method applies the IB framework on marginal and joint representations of the observed views to focus on intra-view and inter-view interactions that are relevant for the target. Most importantly, by modeling the joint representations as a product of marginal representations, we can efficiently learn from observed views with various view-missing patterns. Experiments on real-world datasets show that our method consistently achieves gain from data integration and outperforms state-of-the-art benchmarks.