Stable biomarker discovery in multi-omics data via canonical correlation analysis

Abstract

Multi-omics analysis offers a promising avenue to a better understanding of complex biological phenomena. In particular, untangling the pathophysiology of multifactorial health conditions such as the inflammatory bowel disease (IBD) could benefit from simultaneous consideration of several omics levels. However, taking full advantage of multi-omics data requires the adoption of suitable new tools. Multi-view learning, a machine learning technique that natively joins together heterogeneous data, is a natural source for such methods. Here we present a new approach to variable selection in unsupervised multi-view learning by applying stability selection to canonical correlation analysis (CCA). We apply our method, StabilityCCA, to simulated and real multi-omics data, and demonstrate its ability to find relevant variables and improve the stability of variable selection. In a case study on an IBD microbiome data set, we link together metagenomics and metabolomics, revealing a connection between their joint structure and the disease, and identifying potential biomarkers. Our results showcase the usefulness of multi-view learning in multi-omics analysis and demonstrate StabilityCCA as a powerful tool for biomarker discovery.