SDGCCA: Supervised Deep Generalized Canonical Correlation Analysis for Multi-Omics Integration

Abstract

Integration of multi-omics data provides opportunities for revealing biological mechanisms related to certain phenotypes. We propose a novel method of multi-omics integration called supervised deep generalized canonical correlation analysis (SDGCCA) for modeling correlation structures between nonlinear multi-omics manifolds that aims at improving the classification of phenotypes and revealing the biomarkers related to phenotypes. SDGCCA addresses the limitations of other canonical correlation analysis (CCA)-based models (such as deep CCA, deep generalized CCA) by considering complex/nonlinear cross-data correlations between multiple ( $\ge$ 2) modalities. Although there are a few methods to learn nonlinear CCA projections for classifying phenotypes, they only consider two views. Methods extended to multiple views either do not perform classification or do not provide feature ranking. In contrast, SDGCCA is a nonlinear multi-view CCA projection method that performs classification and ranks features. When we applied SDGCCA in predicting patients with Alzheimer's disease (AD) and discrimination of early- and late-stage cancers, it outperformed other CCA-based and other supervised methods. In addition, we demonstrate that SDGCCA can be applied for feature selection to identify important multi-omics biomarkers. On applying AD data, SDGCCA identified clusters of genes in multi-omics data, well known to be associated with AD.