CAREER: Next-Generation Methods for Statistical Integration of High-Dimensional Disparate Data Sources

随着多组学、神经成像和可穿戴技术的进步，多视角数据(从多个来源的相同样本收集)越来越常见。例如，身体活动跟踪器、连续血糖监测仪和动态血压监测仪等可穿戴设备可以同时佩戴，以提供对不同受试者特征的测量。在整合来自不同优势的并发信息以更好地理解视图之间的关联和改进对健康结果的预测方面存在巨大的潜力。现有的数据集成工具对异常值很敏感，并且不是为混合数据类型(例如，连续偏斜的血糖测量、零膨胀的活动计数、睡眠/清醒的二元指标)而设计的。PI将开发一个更强大的多视图数据集成框架，能够更好地解释异常值，更好地匹配实际收集的混合类型的数据，并更准确地区分常见信号和特定于视图的信号。新方法将在开放源码软件中实施，并配有可重复使用的工作流程示例，为其他研究人员提供即时和方便的访问途径。教育部分的核心是为学生开发结构化研究体验(SRE)。SRE提高了学生的书面交流、软件开发和可重复的研究技能，这些都是传统课程所缺乏的。这将改善学生进行研究的准备，并扩大他们的STEM就业机会。来自传统代表性不足群体的学生的参与将积极影响他们的保留率，并将扩大代表性不足群体在STEM中的参与度。流行的降维方法，如主成分分析和判别分析，都是针对单视图数据而定制的，因此无法在全球水平上发现协调的多视点信号。另一方面，现有的多视点降维方法存在依赖高斯性假设、无法捕捉联合功能信号以及缺乏理论保证等问题。PI将通过以下方式解决这些缺陷：(I)开发用于倾斜连续、二元和零膨胀视图类型的联合降维框架；(Ii)用于混合功能多视图数据的联合降维框架；以及(Iii)基于分层低等级约束的跨视图同时提取信号的新范例。这项工作将为数据集成带来迫切需要的新统计方法，与通过PI的跨学科合作处理可穿戴式监视器、微生物组和多组学数据的研究人员直接相关。拟议的结构化研究体验将集中于模拟研究的设计和重复性，并与拟议研究的计算部分保持一致，包括学生直接参与多个模拟研究。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Multi-view data (collected on the same samples from multiple sources) are increasingly common with advances in multi-omics, neuroimaging and wearable technologies. For example, wearable devices such as physical activity trackers, continuous glucose monitors and ambulatory blood pressure monitors are worn concurrently to provide measurements of distinct subjects’ characteristics. There is enormous potential in integrating that concurrent information from the distinct vantages to better understand between-view associations and improve prediction of health outcomes. Existing tools for data integration are sensitive to outliers, and are not designed for mixed data types (e.g. continuous skewed glucose measurements, zero-inflated activity counts, binary indicators of sleep/wake). The PI will develop a more robust framework for multi-view data integration that is better able to account for outliers, better match the mixed types of data actually collected, and be more accurate in separating common from view-specific signals. The new methods will be implemented in open-source software accompanied by reproducible workflow examples, providing immediate and easy access for other researchers. The educational component centers on the development of structured research experiences (SRE) for students. SRE enhances students written communication, software development and reproducible research skills, all of which are lacking in traditional curriculum. This will improve students’ preparation for conducting research, and widen their STEM employment opportunities. The involvement of students from traditionally underrepresented groups will positively impact their retention rate and will broaden the participation of underrepresented groups in STEM.Popular dimension reduction methods, such as principal component analysis and discriminant analysis, are tailored for single-view data, and thus fail to discover coordinated multi-view signals on a global level. On the other hand, existing multi-view dimension reduction methods suffer from reliance on the Gaussianity assumption, an inability to capture joint functional signals, and a lack of theoretical guarantees. The PI will address these drawbacks by (i) developing a joint dimension reduction framework for skewed continuous, binary and zero-inflated view types; (ii) a joint dimension reduction framework for mixed functional multi-view data and (iii) a new paradigm for simultaneous extraction of signals across views based on hierarchical low-rank constraints. This work will lead to critically needed new statistical methods for data integration with direct relevance for researchers working with wearable monitors, microbiome and multi-omics data through interdisciplinary collaborations of the PI. The proposed structured research experiences will center on the design and reproducibility of simulations studies, and align with computational components of the proposed research, including direct students’ involvement in multiple simulation studies.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.