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: 8236310
• 负责人: Armin Schwartzman
• 金额: $ 28.87万
• 依托单位: DANA-FARBER CANCER INST
• 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/8236310
• 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; Reading; 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; 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. PUBLIC HEALTH RELEVANCE: This proposal provides a unified view of feature detection in high-throughput data that applies to a large class of problems ranging from cancer proteomics to medical imaging to environmental monitoring. By taking advantage of the dependence structure, the methods developed in this proposal offer higher statistical power in the search for markers, facilitating the discovery of new true markers and reducing the number of false markers to be tested in follow-up studies.

描述（由申请人提供）：使用高通量技术在寻找疾病和健康风险标志物中，大规模多重测试已变得无处不在。虽然用于多次测试的统计方法通常在测试之间假定独立性，但许多实际情况都表现出依赖性和潜在的结构。在蛋白质组学数据的情况下，空间结构的示例是一维（1D）。在环境数据的情况下2D；在大脑成像数据的情况下。分析中忽略相关性可能会导致不同的集合和发现的特征的顺序，从而增加错误率和潜在的重要特征缺失。需要表征多个测试中相关性的效果，并将其纳入分析。 该提案的目的是开发多种测试方法，以增加数据中的相关性，以提高统计能力，控制错误率并获得适当解释的结果。这是通过两种不同的方式完成的。 （1）在目标1和2中，我们假设一个空间结构和固定的沿子相关性，其中感兴趣的信号由相对较少的单峰峰组成。我们使用随机场理论计算P值来测试平滑后观察到的数据的局部最大值的高度。我们从1D到3D域中开发这些方法，从相等宽度到不等宽度峰的峰。然后，我们适应这些方法并将这些方法应用于从高通量技术获得的各种类型的数据中，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，用于识别由于气候变化而导致热应激风险的地理区域；和脑成像数据，用于鉴定参与异常认知发展的解剖区域。 （2）在AIM 3中，我们假设一个一般的相关结构，不一定是固定或颈椎，并提出了有条件的边缘分析，其中通过对观察到的可能无效病例的边际分布进行调节来纳入相关性。尽管不仅仅是纯粹的重点是错误的发现率推断。 该提案为随机场提供了统一的信号检测视图，该视图广泛适用于从蛋白质组学到医学成像再到环境监测的大量问题。从统计的角度来看，它为在随机字段中控制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能力，因此在后续研究中将测试较小数量的错误标记。 公共卫生相关性：该提案在高通量数据中提供了统一的特征检测视图，该观点适用于从癌症蛋白质组学到医学成像再到环境监测的大量问题。通过利用依赖性结构，本提案中开发的方法在寻找标记方面提供了更高的统计能力，促进发现新的真实标记物并减少在后续研究中要测试的错误标记的数量。