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

生物医学研究的新进展使收集多个数据“视图”成为可能--例如， 遗传、代谢组学和临床数据--针对单个患者。这种多视图数据有望提供更深入的 如果只有一个数据视图可用，那么可以更深入地了解患者的健康和疾病。但在 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 观察，还是每个数据视图包含不同的信息集？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 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 同样的“故事”的观察，那么我们可以很有信心，这个故事是真实的。 研究人员将开发一个统一的艾德框架来建模多视图数据，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变量数据（例如，单个集合 的患者，具有临床和遗传测量），以确定单个聚类是否可以 在所有数据视图中充分描述患者，或者患者是否在每个数据中单独聚类 风景在目标2中，该框架将应用于多视图网络数据（例如，一组蛋白质， 测量的二元和共复合物相互作用），以便确定节点是否属于单个组 跨数据视图的社区，或每个数据视图中的单独社区集。目标3：框架 将应用于多视图多变量数据，以确定观测是否可以嵌入到 所有数据视图中的单个潜在空间，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先锋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，以便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中鉴定的艾德蛋白质群落 实验性的目标5将开发高质量的开放源码软件。 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 是相同的还是不同的。将这些方法应用于多视图数据集，包括先锋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的理解， 生物学

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.