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.

项目总结 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， 可以从不同的个人中生成复杂的信号集合 神经生理状态或临床情况。开发新的统计工具以 分析这些丰富的数据集已经成为限制医学发展的因素 诊断和生物医学研究。这项研究计划的目标是开发新的 复杂一般函数数据的非参数稳健分析方法 结构，如图像，使用深度的概念。在过去的二十年里， 数据深度概念的密集发展，这些概念已经变得强大 用于分析多变量和函数数据的非参数工具。建议的方法 在这个项目中是基于一般函数和示例的数据深度的概念 它提供了排名顺序。稳健的非参数统计在这方面特别相关 设置，因为通常可以对数据生成过程做出很少的假设 潜在的离群值可能很难检测到，可能会在许多情况下影响分析 不同的方式。不同可能的离群值类型的分类和 将开发用于探测它们的探索性/可视化工具。新方法 基于用于检查不同的函数或图像组 来自同一分布的人都提出了，并将予以研究。最近，国际刑警组织已经 开始与纽约州精神病研究所的研究人员合作，由Dr。 托德·奥格登在一个由正电子发射断层扫描(PET)大脑组成的数据集上 来自严重抑郁障碍患者样本的图像和 控制。PI还一直在与哥伦比亚大学的儿科医生Vidhu Thaker博士合作 关于分析不同性别儿童的身体质量指数(BMI)轨迹 儿童早期严重肥胖的程度。本项目中介绍的方法将 从这些数据集中提取出远远超出临床意义的信息 到目前为止已经完成了。特别是，所提出的基于深度的非参数方法将 用于：1)从中心向外对函数样本进行排序；2)识别 数据集和3)开发分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的进展 生物医学。