New Techniques to Combine Measures of Statistical Significance from Heterogeneous Data Sources with Application to Analysis of Genomic Data

该项目的动机是对大规模基因组数据进行综合分析，其中一个重要问题是如何有效地结合来自异构数据源的统计显著性或p值。尽管最近在理论和应用研究方面取得了进展，但在处理关键数据特征方面仍然存在统计和计算方面的挑战，例如复杂的相关性、数据的离散性以及可用的先验知识，这些都可以用来增强信号检测。该项目将开发新的统计方法来解决这些挑战，并增加检测有效信号的统计能力。这项研究将促进统计理论和方法以及广泛应用方面的创新。这些研究活动将利用以项目为导向的教育，促进多学科互动，并有利于下一代工程师和科学家的STEM教育，特别是在统计领域代表性不足的少数民族成员。具体而言，该项目将遵循不同于普通文献的新策略，开发有效而强大的p值组合检验。与单独设计和研究测试不同，该项目将战略性地解决基于一般测试家族的问题。该项目有三个具体的研究目标。第一个目标是解决应用p值组合方法分析复杂异构数据的计算挑战。PI将开发快速准确的算法，以控制一般相关性和p值离散性下一般测试族的错误率。第二个目标是通过利用相关信息、结合先验知识、自动适应互补过程和揭示渐近最优性来提高p值组合对复杂数据综合分析的能力。最后，PI将将开发的方法应用于神经退行性疾病的大规模基因组数据的综合分析。该项目的结果有望推动全球高维复杂数据分析的假设检验方法。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This project is motivated by integrative analysis of large-scale genomic data, where an important question is how to effectively combine statistical significances, or p-values, from heterogeneous data sources. Despite recent advances in theoretical and applied studies, statistical and computational challenges remain in addressing critical data features, such as complex correlations, discreteness of data, and availability of prior knowledge that could have been utilized to boost signal detection. This project will develop novel statistical methods to address the challenges and increase the statistical power for detecting valid signals. The research will facilitate innovations in statistical theory and methodology as well as in broad applications. The research activities will leverage project-oriented education, promote multi-disciplinary interactions, and benefit STEM education for the next generation of engineers and scientists, especially members of minorities underrepresented in the statistics field. Specifically, the project will develop efficient and powerful p-value combination tests by following a new strategy different from common literature. Instead of designing and studying tests individually, the project will strategically resolve problems based on general families of tests. The project has three specific research aims. The first aim is to tackle the computational challenges in applying the p-value combination approach into analyzing complex heterogeneous data. The PI will develop fast and accurate algorithms to control the error rates of general families of tests under general correlations and the discreteness of the p-values. The second aim is to increase the power of p-value combination for integrative analysis of complex data through utilizing the correlation information, incorporating prior knowledge, automatically adapting to complementary procedures, and revealing asymptotic optimality properties. Finally, the PI will apply the developed methods into the integrative analysis of large-scale genomic data of neurodegenerative diseases. Results of the project are expected to advance global hypothesis testing methods for high-dimensional complex data analysis.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.