Novel Statistical Integration Methods for Multi-View Data

在大数据时代，从常规临床访问和医学研究中收集的患者数据的数量和种类都以前所未有的速度增长，例如，从医疗记录、基因组和神经成像数据源。联合分析从各种来源获得的此类数据（也称为多视图数据）以整合丰富的信息无疑为精准健康带来了新的希望，但尽管如此，它也带来了重要的和新出现的挑战。首先，来自每个源的数据通常是高维的，具有比主题数量更多的特征。其次，来自不同来源的数据可以是不同类型的（例如，连续的、分类的、计数和事件发生时间）和不同的格式，（例如，多变量、矩阵变量和张量变量）。最后，在大规模数据收集过程中，数据缺失是不可避免的。虽然已经开发了许多技术来分别对这些单独的数据类型或格式中的每一种进行建模，但目前很少有方法可以联合分析高维混合多视图数据，同时还可以正确处理通常不完整的多视图。拟议的研究将解决这些问题，并专注于以下具体目标：目标1将开发联合建模策略，以提取多视图数据中的低维特征，同时区分特定于不同数据源的共享和独特特征，并同时评估其预测能力，以预测感兴趣的结果。目标2将开发联合建模策略来聚类多视图数据，同时分离相关和不相关的特征，并同时将相关的子组成员关系与感兴趣的结果联系起来。目标3将提出在联合建模框架中嵌入贝叶斯加性回归树的方法，以将多视图数据与结果数据相关联。 该提案的重点是开发新的贝叶斯分层建模方法，这些方法非常适合将复杂的数据结构与潜在变量结合起来，重要的是能够进行精确的推理。受分析医学研究中的混合多视图数据的激励，所提出的研究将提供新颖的和计算高效的分析工具和策略（即，用户友好的R软件包），使医学研究中的科学家能够全面地探索他们的数据，并使我们更接近实现精准健康的最终目标。同时，所提出的方法创新在其他涉及异质多视图数据的科学领域也具有广泛的应用价值。

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.