Nonparametric depth-based methods for analyzing high-dimensional data. Applications to biomedical research

用于分析高维数据的基于非参数深度的方法。

• 批准号: 9807861
• 负责人: Sara Lopez-Pintado
• 金额: $ 21.6万
• 依托单位: NORTHEASTERN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-16 至 2021-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9807861
• 关键词: Affect; Area; Bioinformatics; Biomedical Research; Body mass index; Brain; Brain imaging; Cardiovascular system; Child; Childhood; Classification; Clinical; Collaborations; Collection; Complex; Computing Methodologies; Data; Data Analyses; Data Collection; Data Set; Detection; Development; Diagnosis; Dimensions; Disease; Endocrinologist; Functional Imaging; Future; Goals; Growth; Health; Health Sciences; Image; Imaging Device; Imaging technology; Individual; Institutes; Lead; Location; Major Depressive Disorder; Measures; Medical; Mental disorders; Methodology; Methods; Modeling; Morbid Obesity; Multivariate Analysis; Neurosciences; New York; Nonparametric Statistics; Outcome; Pattern; Population; Positron-Emission Tomography; Process; Public Health; Research; Research Personnel; Research Project Grants; Research Proposals; Sampling; Shapes; Signal Transduction; Statistical Methods; Structure; Taxonomy; Techniques; Test Result; Testing; Tweens; Universities; Visualization software; Work; base; clinical Diagnosis; clinically relevant; early childhood; early-onset obesity; functional group; high dimensionality; indexing; multidimensional data; neurophysiology; novel; novel strategies; obesity in children; pediatrician; tool; user-friendly

PROJECT SUMMARY Technological development in many emerging research fields has provided us with large collections of data of extraordinary complexity. Brain imaging technology, for example, can generate complex collections of signals from individuals in different neurophysiological states or clinical conditions. Developing new statistical tools to analyze these rich data sets has become a limiting factor for the advancement of medical diagnosis and biomedical research. The goal of this research proposal is to develop new nonparametric and robust methods for analyzing general functional data with complicated structure, such as images, using the idea of depth. In the last two decades there has been an intensive development of notions of data depth, which have become powerful nonparametric tools for analyzing multivariate and functional data. The methods proposed in this project are based on a notion of data depth for general functions and the sample rank-order it provides. Robust nonparametric statistics are particularly relevant in this setting since usually few assumptions can be made about the data generating process and potential outliers, which may be very difficult to detect, can affect the analysis in many different ways. A taxonomy of the different possible types of outliers and exploratory/visualization tools for detecting them will be developed. New approaches based on novel envelope tests for checking if different groups of functions or images come from the same distribution are proposed and will be studied. Recently, the PI has started collaborating with investigators at New York State Psychiatric Institute, led by Dr. Todd Ogden, on a data set that consists of positron emission tomography (PET) brain images from a sample of individuals with major depressive disorders and a sample of controls. The PI has also been working with Dr. Vidhu Thaker, a pediatrician at Columbia University, on analyzing body mass index (BMI) trajectories of children with different degrees of severe early childhood obesity. The methods introduced in this project will extract from these data sets information of clinical relevance far beyond what has been accomplished so far. In particular, the proposed depth-based nonparametric methods will be used to: 1) rank a sample of functions from center-outwards, 2) identify outliers in the data set and 3) develop nonparametric envelope tests for groups differences and identify patterns. We believe that this work will boost the progress in different areas of biomedicine.

项目摘要 许多新兴研究领域的技术发展为我们提供了大量的 极其复杂的数据集合。例如，大脑成像技术， 可以从不同的个体中产生复杂的信号集合， 神经生理状态或临床状况。开发新的统计工具， 分析这些丰富的数据集已经成为医学进步的限制因素。 诊断和生物医学研究。这项研究计划的目标是开发新的 非参数和强大的方法来分析一般功能数据与复杂的 结构，如图像，使用深度的想法。在过去的二十年里， 数据深度概念的深入发展， 用于分析多元和函数数据的非参数工具。提出的方法 基于一般函数的数据深度概念， 它提供的等级顺序。稳健的非参数统计在这方面特别重要。 因为通常很少有关于数据生成过程的假设 和潜在的异常值，这可能是非常难以检测，可以影响分析，在许多 不同的方式不同可能类型的离群值的分类， 将开发用于探测它们的探索性/可视化工具。新方法 基于新颖的包络测试，用于检查是否不同组的函数或图像 来自相同的分布被提出并且将被研究。最近，PI 开始与纽约州立精神病研究所的研究人员合作。 托德奥格登，在一个数据集，包括正电子发射断层扫描（PET）的大脑 图像从一个样本的个人与严重抑郁症和一个样本， 对照PI还与哥伦比亚的儿科医生Vidhu Thaker博士合作 大学，对分析儿童的身体质量指数（BMI）的轨迹不同， 严重的儿童早期肥胖。本项目中介绍的方法将 从这些数据集中提取出的临床相关信息远远超出了 到目前为止完成。特别是，所提出的基于深度的非参数方法将 用于：1）从中心向外排列函数样本，2）识别 数据集和3）开发组差异的非参数包络检验，并确定 模式.我们相信，这项工作将推动在不同领域取得进展， 生物医药