A Modeling Framework for Multi-View Data, with Applications to the Pioneer 100 Study and Protein Interaction Networks

多视图数据建模框架，及其在 Pioneer 100 研究和蛋白质相互作用网络中的应用

• 批准号: 9361170
• 负责人: Jacob Bien
• 金额: $ 34万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2021-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9361170
• 关键词: Address; Adoption; Agreement; Algorithms; Biology; Biomedical Research; Clinical Data; Communities; Complex; Computer software; Conflict (Psychology); Data; Data Set; Detection; Development; Dimensions; Disease; Foundations; Future; Gene Expression; Genetic; Genomics; Goals; Health; Human; Individual; Learning; Measurement; Measures; Medical Genetics; Meta-Analysis; Methodology; Methods; Modeling; Participant; Patients; Principal Component Analysis; Proteins; Proteomics; Records; Research Personnel; Resources; Set protein; Statistical Data Interpretation; Statistical Methods; Technology; Testing; Time; Trust; Validation; Variant; genomic data; improved; insight; metabolomics; novel strategies; open source

New advances in biomedical research have made it possible to collect multiple data “views” — for example, genetic, metabolomic, and clinical data — for a single patient. Such multi-view data promises to offer deeper insights into a patient's health and disease than would be possible if just one data view were available. However, in order to achieve this promise, new statistical methods are needed. This proposal involves developing statistical methods for the analysis of multi-view data. These methods can be used to answer the following fundamental question: do the data views contain redundant information about the observations, or does each data view contain a different set of information? The answer to this question will provide insight into the data views, as well as insight into the observations. If two data views contain redundant information about the observations, then those two data views are related to each other. Furthermore, if each data view tells the same “story” about the observations, then we can be quite confident that the story is true. The investigators will develop a unified framework for modeling multi-view data, which will then be applied in a number of settings. In Aim 1, this framework will be applied to multi-view multivariate data (e.g. a single set of patients, with both clinical and genetic measurements), in order to determine whether a single clustering can adequately describe the patients across all data views, or whether the patients cluster separately in each data view. In Aim 2, the framework will be applied to multi-view network data (e.g. a single set of proteins, with both binary and co-complex interactions measured), in order to determine whether the nodes belong to a single set of communities across the data views, or a separate set of communities in each data view. In Aim 3, the framework will be applied to multi-view multivariate data in order to determine whether the observations can be embedded in a single latent space across all data views, or whether they belong to a separate latent space in each data view. In Aims 1–3, the methods developed will be applied to the Pioneer 100 study, and to the protein interactome. In Aim 4(a), the availability of multiple data views will be used in order to develop a method for tuning parameter selection in unsupervised learning. In Aim 4(b), protein communities that were identified in Aim 2 will be validated experimentally. High-quality open source software will be developed in Aim 5. The methods developed in this proposal will be used to determine whether the findings from multiple data views are the same or different. The application of these methods to multi-view data sets, including the Pioneer 100 study and the protein interactome, will improve our understanding of human health and disease, as well as fundamental biology.

生物医学研究的新进展使得收集多种数据“视图”成为可能——例如， 单个患者的遗传、代谢组学和临床数据。这种多视图数据有望提供更深入的 与仅提供一个数据视图相比，可以更深入地了解患者的健康和疾病。然而，在 为了实现这一承诺，需要新的统计方法。 该提案涉及开发用于分析多视图数据的统计方法。这些方法可以 用于回答以下基本问题：数据视图是否包含有关数据的冗余信息 观察，或者每个数据视图是否包含一组不同的信息？这个问题的答案将提供 洞察数据视图以及洞察观察结果。如果两个数据视图包含冗余信息 关于观察结果，那么这两个数据视图是相互关联的。此外，如果每个数据视图都告诉 如果我们的观察结果有相同的“故事”，那么我们就可以非常确信这个故事是真实的。 研究人员将开发一个统一的多视图数据建模框架，然后将其应用于 一些设置。在目标 1 中，该框架将应用于多视图多元数据（例如，单组 患者的临床和遗传测量），以确定单个聚类是否可以 充分描述所有数据视图中的患者，或者患者是否在每个数据中单独聚类 看法。在目标 2 中，该框架将应用于多视图网络数据（例如，一组蛋白质，同时具有 测量二元和复合体相互作用），以确定节点是否属于单个集合 跨数据视图的社区，或每个数据视图中的一组单独的社区。在目标 3 中，框架 将应用于多视图多元数据，以确定观察结果是否可以嵌入 跨所有数据视图的单个潜在空间，或者它们是否属于每个数据视图中的单独潜在空间。 在目标 1-3 中，开发的方法将应用于 Pioneer 100 研究和蛋白质相互作用组。在 目标 4(a)，将使用多个数据视图的可用性来开发调整参数的方法 无监督学习中的选择。在目标 4(b) 中，将验证目标 2 中确定的蛋白质群落 实验性地。目标 5 将开发高质量的开源软件。 本提案中开发的方法将用于确定来自多个数据视图的结果是否 相同或不同。这些方法在多视图数据集上的应用，包括 Pioneer 100 研究 和蛋白质相互作用组，将提高我们对人类健康和疾病以及基本原理的理解 生物学。