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 非凡复杂性数据的收集。大脑成像技术，例如 可以从不同的个体中产生复杂的信号集 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成为医疗发展的限制因素 诊断和生物医学研究。该研究建议的目的是开发新的 非参数和鲁棒方法，用于分析具有复杂性的一般功能数据 结构，例如图像，使用深度概念。在过去的二十年中 数据深度概念的深入发展，这些概念已变得强大 用于分析多元和功能数据的非参数工具。提出的方法 在此项目中，基于一般函数的数据深度概念和样本 它提供的等级顺序。强大的非参数统计在这方面特别重要 设置由于通常几乎没有关于数据生成过程的假设 可能很难检测到的潜在异常值可能会影响许多分析 不同的方式。对不同类型的异常值的分类法和 将开发用于检测它们的探索/可视化工具。新方法 基于新的包络测试，用于检查不同的函数或图像组是否 提出了来自同一分布的信息，并将研究。最近，Pi有 开始与纽约州精神病学研究所的调查员合作，该研究所由博士领导。 托德·奥格登（Todd Ogden） 来自主要抑郁症的个体样本的图像和样本 控件。 PI还一直与哥伦比亚的儿科医生Vidhu Thaker博士合作 大学，分析具有不同儿童的体重指数（BMI）轨迹 严重的幼儿肥胖症程度。该项目中介绍的方法将 从这些数据集的临床相关性信息中提取远远超出了 到目前为止完成了。特别是，拟议的基于深度的非参数方法将 用于以下：1）从中心外排名一个函数样本，2）识别在 数据集和3）为组差异开发非参数包络测试并确定 模式。我们认为，这项工作将促进不同领域的进展 生物医学。