Multiple testing methods for random fields and high-dimensional dependent data

随机场和高维相关数据的多种测试方法

• 批准号: 8790516
• 负责人: Armin Schwartzman
• 金额: $ 22.77万
• 依托单位: NORTH CAROLINA STATE UNIVERSITY RALEIGH
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-06-03 至 2017-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8790516
• 关键词: Biological Markers; Brain imaging; Charge; Child; Climate; Cognitive; Communities; Complex; Computer software; Computer-Assisted Image Analysis; Data; Dependence; Detection; Development; Disease; Environmental Monitoring; Exhibits; Family; Goals; Health; Heat Stress Disorders; Height; Lead; Malignant Neoplasms; Malignant neoplasm of lung; Mass Spectrum Analysis; Medical Imaging; Methods; Modeling; Noise; North America; Output; Performance; Procedures; Proteins; Proteomics; Risk; Risk Marker; Sampling; Signal Transduction; Statistical Methods; Structure; Testing; Width; base; cancer proteomics; climate change; conditioning; follow-up; high throughput technology; interest; method development; protein structure; reading ability; simulation; statistics; theories; tool; user-friendly

DESCRIPTION (provided by applicant): Large-scale multiple testing has become ubiquitous in the search for disease and health risk markers using high-throughput technologies. While statistical methods for multiple testing often assume independence between the tests, many real situations exhibit dependence and an underlying structure. Examples of spatial structure are one-dimensional (1D) in the case of proteomic data; 2D in the case of environmental data; and 3D in the case of brain imaging data. Ignoring correlation in the analysis may lead to a different set and ordering of discovered features, resulting in increased error rates and potential missing of important features. There is a need to characterize the effect of correlation in multiple testing and incorporate it into the analysis. The goal of this proposal is to develop multiple testing methods that incorporate the correlation in the data in order to increase statistical power, control error rates and obtain appropriately interpretable results. This is done in two different ways. (1) In Aims 1 and 2, we assume a spatial structure and stationary ergodic correlation, where the signal of interest consists of a relatively small number of unimodal peaks. We use random field theory to compute p-values for testing the heights of local maxima of the observed data after smoothing. We develop these methods in complexity from 1D to 3D domains, and from peaks of equal width to peaks of unequal width. We then adapt and apply these methods to various types of data obtained from high-throughput technologies, specifically: mass- spectrometry data for identifying protein biomarkers of cancer; climate model output data for identification of geographical regions at risk for heat stress as a result of climate change; and brain imaging data for identification of anatomical regions involved in abnormal cognitive development. (2) In Aim 3, we assume a general correlation structure, not necessarily stationary or ergodic, and propose a conditional marginal analysis, where correlation is incorporated through conditioning on the observed marginal distribution of likely null cases. Although not exclusively, emphasis throughout is placed on false discovery rate inference. This proposal provides a unified view of signal detection for random fields that applies broadly to a large class of problems ranging from proteomics to medical imaging to environmental monitoring. From a statistical point of view, it provides a new answer to the problem of controlling FDR in random fields. By taking advantage of the dependence structure, the methods developed in this proposal offer higher statistical power in the search for markers, so that a smaller number of false markers will be tested in follow-up studies.

描述（由申请人提供）：使用高通量技术寻找疾病和健康风险标记物时，大规模多重测试已变得无处不在。虽然多重测试的统计方法通常假设测试之间是独立的，但许多实际情况表现出依赖性和底层结构。空间结构的例子是蛋白质组数据中的一维 (1D)；环境数据为二维；大脑成像数据中的 3D 数据。忽略分析中的相关性可能会导致所发现的特征的集合和顺序不同，从而导致错误率增加并可能丢失重要特征。需要表征多重测试中相关性的影响并将其纳入分析中。 该提案的目标是开发多种测试方法，将数据中的相关性纳入其中，以提高统计功效、控制错误率并获得适当的可解释结果。这是通过两种不同的方式完成的。 (1) 在目标 1 和 2 中，我们假设空间结构和平稳遍历相关性，其中感兴趣的信号由相对少量的单峰峰值组成。我们使用随机场理论来计算 p 值，以测试平滑后观测数据的局部最大值的高度。我们开发了从 1D 到 3D 域、从等宽峰到不等宽峰的复杂方法。然后，我们将这些方法应用于从高通量技术获得的各种类型的数据，特别是：用于识别癌症蛋白质生物标志物的质谱数据；气候模型输出数据，用于识别因气候变化而面临热应激风险的地理区域；和大脑成像数据，用于识别参与异常认知发展的解剖区域。 (2) 在目标 3 中，我们假设一个一般的相关结构，不一定是固定的或遍历的，并提出了一种条件边际分析，其中通过对可能零案例的观察到的边际分布进行调节来合并相关性。尽管不是唯一的，但自始至终都强调错误发现率推断。 该提案提供了随机场信号检测的统一视图，广泛适用于从蛋白质组学到医学成像到环境监测的一大类问题。从统计学的角度来看，它为随机场中控制FDR的问题提供了新的答案。通过利用依赖性结构，本提案中开发的方法在寻找标记时提供了更高的统计功效，以便在后续研究中测试更少数量的错误标记。