Novel Statistical Inference for Biomedical Big Data

生物医学大数据的新颖统计推断

• 批准号: 10252023
• 负责人: ALI SHOJAIE
• 金额: $ 41.5万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-05 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10252023
• 关键词: Address; Adoption; Behavioral; Big Data Methods; Biological; Biological Assay; Biological Markers; Code; Collection; Communities; Computer software; Data; Data Sources; Development; Dimensions; Disease; Electronic Health Record; Evaluation; Fostering; Galaxy; Genetic study; Goals; Heart; Imaging technology; Individual; Linear Models; Measurement; Measures; Medical Imaging; Methods; Modeling; Molecular; Multiomic Data; Outcome; Phenotype; Procedures; R programming language; Research Personnel; Sample Size; Scientist; Screening procedure; Software Tools; Structure; System; Testing; Trans-Omics for Precision Medicine; Uncertainty; Work; base; big biomedical data; computational pipelines; data integration; design; diverse data; effective therapy; experimental study; heterogenous data; high dimensionality; interest; machine learning method; member; novel; open source; public health relevance; screening; simulation; statistical and machine learning; structured data; tool; treatment strategy; user friendly software

Project Summary This project develops novel statistical inference procedures for biomedical big data (BBD), including data from diverse omics platforms, various medical imaging technologies and electronic health records. Statistical inference, i.e., assess- ing uncertainty, statistical significance and confidence, is a key step in computational pipelines that aim to discover new disease mechanisms and develop effective treatments using BBD. However, the development of statistical inference procedures for BBD has lagged behind technological advances. In fact, while point estimation and variable selection procedures for BBD have matured over the past two decades, existing inference procedures are either limited to simple methods for marginal inference and/or lack the ability to integrate biomedical data across multiple studies and plat- forms. This paucity is, in large part, due to the challenges of statistical inference in high-dimensional models, where the number of features is considerably larger than the number of subjects in the study. Motivated by our team's extensive and complementary expertise in analyzing multi-omics data from heterogenous studies, including the TOPMed project on which multiple team members currently collaborate, the current proposal aims to address these challenges. The first aim of the project develops a novel inference procedure for conditional parameters in high-dimensional models based on dimension reduction, which facilitates seamless integration of external biological information, as well as biomedical data across multiple studies and platforms. To expand the application of this method to very high-dimensional models that arise in BBD applications, the second aim develops a data-adaptive screening procedure for selecting an optimal subset of relevant variables. The third aim develops a novel inference procedure for high-dimensional mixed linear models. This method expands the application domain of high-dimensional inference procedures to studies with longitu- dinal data and repeated measures, which arise commonly in biomedical applications. The fourth aim develops a novel data-driven procedure for controlling the false discovery rate (FDR), which facilitates the integration of evidence from multiple BBD sources, while minimizing the false negative rate (FNR) for optimal discovery. Upon evaluation using ex- tensive simulation experiments and application to multi-omics data from the TOPMed project, the last aim implements the proposed methods into easy-to-use open-source software tools leveraging the R programming language and the capabilities of the Galaxy workflow system, thus providing an expandable platform for further developments for BBD methods and tools.

项目概要 该项目为生物医学大数据（BBD）开发新颖的统计推断程序，包括来自不同领域的数据 组学平台、各种医学成像技术和电子健康记录，即评估。 计算不确定性、统计显着性和置信度是旨在发现新的计算管道的关键步骤 然而，统计推断的发展还不够。 事实上，BBD 的程序已经落后于技术进步，而点估计和变量选择。 BBD 程序在过去二十年里已经成熟，现有的推理程序要么仅限于简单的 边际推理方法和/或缺乏跨多个研究和平台整合生物医学数据的能力 这种缺乏在很大程度上是由于高维模型中统计推断的挑战，其中 由于我们团队的广泛研究，特征的数量远远大于研究对象的数量。 以及分析来自异质研究的多组学数据的互补专业知识，包括 TOPMed 项目 目前多个团队成员正在合作，当前的提案旨在解决这些挑战。 该项目的目标是开发一种基于高维模型中条件参数的新颖推理程序 降维，有利于外部生物信息以及生物医学信息的无缝集成 跨多个研究和平台的数据将该方法的应用扩展到非常高维的模型。 BBD 应用中出现的第二个目标是开发一种数据自适应筛选程序，用于选择最佳的 第三个目标是为高维混合线性开发一种新颖的推理程序。 该方法将高维推理过程的应用领域扩展到纵向研究。 第四个目标是开发一种新颖的数据和重复测量。 用于控制错误发现率（FDR）的数据驱动程序，有助于整合来自 多个 BBD 源，最小化假阴性率（FNR）以实现最佳发现。 TOPMed项目的多组学数据的密集模拟实验和应用，最后一个目标实现 将所提出的方法转化为易于使用的开源软件工具，利用 R 编程语言和 Galaxy工作流程系统的功能，从而为BBD的进一步开发提供可扩展的平台 方法和工具。