SDGCCA：用于多组学整合的有监督深度广义典型相关分析
SDGCCA: Supervised Deep Generalized Canonical Correlation Analysis for Multi-omics Integration
摘要：
多组学数据的整合为揭示与某些表型相关的生物学机制提供了机会。本文提出了一种新的多组学整合方法--监督式深度广义典型相关分析（SDGCCA），用于非线性多组学流形之间的相关结构建模，旨在改善表型分类，揭示与表型相关的生物标志物。SDGCCA解决了其他基于典型相关分析（CCA）的模型（例如，深CCA，深广义CCA），通过考虑复杂/非线性交叉数据相关性和区分表型组。虽然有一些方法用于非线性CCA预测的判别目的的表型，他们只考虑两个视图。另一方面，SDGCCA是一种非线性多视角CCA投影方法。当我们将SDGCCA应用于阿尔茨海默病（AD）患者的预测和早期和晚期癌症的区分时，它优于其他基于CCA的方法和其他监督方法。此外，我们证明了SDGCCA可以用于特征选择以识别重要的多组学生物标志物。在AD数据的应用中，SDGCCA识别了多组学数据中的基因簇，这些基因簇是众所周知的与AD相关的。
英文摘要：
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.
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