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

arXiv: https://arxiv.org/abs/2204.09045 · HTML: https://mlsanity.com/paper/2204.09045/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 39 references that are in the map.
- First posted on arXiv 17 Apr 2022.

## Builds on (most cited first, 5 of 5)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Deep Generalized Canonical Correlation Analysis](https://mlsanity.com/paper/1702.02519/) (2017)
- [A whitening approach to probabilistic canonical correlation analysis for omics data integration](https://mlsanity.com/paper/1802.03490/) (2018)
- [Deep Multi-View Learning via Task-Optimal CCA](https://mlsanity.com/paper/1907.07739/) (2019)

## Built on by (most cited first, 3 of 3)

- [From Classical Machine Learning to Emerging Foundation Models: Review on Multimodal Data Integration for Cancer Research](https://mlsanity.com/paper/2507.09028/) (2025)
- [Deep Metric Loss for Multimodal Learning](https://mlsanity.com/paper/2308.10486/) (2023)
- [Brain-Cognition Fingerprinting via Graph-GCCA with Contrastive Learning](https://mlsanity.com/paper/2409.13887/) (2024)

## Abstract (arXiv)

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, aiming for improving classification of phenotypes and revealing biomarkers related to phenotypes. SDGCCA addresses the limitations of other canonical correlation analysis (CCA)-based models (e.g., deep CCA, deep generalized CCA) by considering complex/nonlinear cross-data correlations and discriminating phenotype groups. Although there are a few methods for nonlinear CCA projections for discriminant purposes of phenotypes, they only consider two views. On the other hand, SDGCCA is the nonlinear multiview CCA projection method for discrimination. When we applied SDGCCA to prediction of patients of Alzheimer's disease (AD) and discrimination of early- and late-stage cancers, it outperformed other CCA-based methods and other supervised methods. In addition, we demonstrate that SDGCCA can be used for feature selection to identify important multi-omics biomarkers. In the application on AD data, SDGCCA identified clusters of genes in multi-omics data, which are well known to be associated with AD.