Simultaneous Representation Learning of Multi-Omics and Clinical Outcome Data via a Supervised Knowledge-Guided Bayesian Factor Model.

With the advent of high-throughput techniques, multi-omics data and various clinical outcomes have been collected for a range of diseases. Multi-omics data play a crucial role in uncovering complex biological processes, yet simultaneous representation learning of such high-dimensional, heterogeneous multi-modality data along with clinical outcomes remains limited. To address this gap, we propose a supervised knowledge-guided Bayesian factor model for integrative analysis of multi-omics and clinical outcome data. The proposed method simultaneously extracts an informative low-dimensional representation and predicts one or more clinical outcomes of interest. The two-level adaptive shrinkage in the novel hierarchical priors allows for the identification of both active modalities and features, resulting in a biologically meaningful structural identification of the high-dimensional data. Moreover, the method is robust to noisy edges in biological graphs that do not align with ground truth. Finally, the proposed method can handle different data types including both continuous and categorical data. Extensive simulation studies and real data analyses of Alzheimer's disease (AD) data demonstrate the advantages of the proposed approach over existing methods. Notably, our analysis of multi-omics and imaging phenotype data from ADNI provides meaningful insights into the underlying biological mechanisms of AD.