Novel Statistical Integration Methods for Multi-View Data

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In the era of big data, both the volume and variety of patient data collected from routine clinical visits and medical research, are growing at an unprecedented pace, e.g., from medical records, genomic and neuroimaging data sources. Jointly analyzing such data obtained from various sources (also known as multi-view data) to integrate rich information has undoubtedly brought new promises for precision health, but nonetheless it also poses important and emerging challenges. First, the data from each source are typically high dimensional, with more features than the number of subjects. Second, the data from different sources could be of different types (e.g., continuous, categorical, counts and time-to-event) and of different formats, (e.g., multivariate, matrix-variate and tensor-variate). Lastly, missing data are inevitable during the large-scale data collection process. While many techniques have been developed to model each of these individual data types or formats separately, there are currently few methods that can jointly analyze high-dimensional mixed multi-view data while also properly handling often incomplete multi-view. The proposed research will address these issues and focus on the following specific aims:  Aim 1 will develop joint modeling strategies to extract low-dimensional features in the multi-view data, while distinguishing shared and unique features specific to different data sources, and simultaneous evaluate their prognostic abilities to predict the outcome of interest. Aim 2 will develop joint modeling strategies to cluster multi-view data, while separating relevant and irrelevant features, and simultaneously relate the associated subgroup memberships to the outcome of interest. Aim 3 will propose methods to embed the Bayesian Additive Regression Trees in the joint modeling framework to relate multi-view data to the outcome data.  This proposal focuses on developing novel Bayesian hierarchical modelling approaches, which are well suited to incorporate complex data structures with latent variables and importantly enable exact inference. Motivated by analyzing mixed multi-view data in medical studies, the proposed research will provide novel and computationally efficient analytic tools and strategies (i.e., user friendly R packages) to allow scientists in medical research to explore their data holistically and to bring us closer to meeting the ultimate goals in precision health. In the meanwhile, the methodological innovations proposed are widely applicable in other scientific domains that involve heterogenous multi-view data.