Theory and Methods for Simultaneous Signal Analysis in Integrative Genomics

综合基因组学同步信号分析的理论和方法

• 批准号: 1613005
• 负责人: Sihai Zhao
• 金额: $ 35.37万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-15 至 2019-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1613005&HistoricalAwards=false
• 关键词: Theory; Methods; Simultaneous; Signal; Analysis

The main objective of this project is to develop new methodology and theory for statistical challenges motivated by integrative genomics, a collection of quantitative approaches in genomics research that centers around the joint analysis of multiple datasets. Integrative analysis has tremendous potential to precipitate the next wave of scientific discoveries in genomics and is also a crucial component of emerging conceptions of data science in general. This project aims to develop new analysis procedures for 1) detecting whether two or more datasets share the same significant genomic features, 2) identifying these features, and 3) leveraging them to improve genomic prediction models.This project opens important questions in integrative genomics up to rigorous methodological and theoretical development by framing them in terms of cutting-edge statistical issues, including signal detection, multiple testing, and high-dimensional classification. The proposed methodological research will develop new nonparametric tests and false discovery rate control procedures for detecting and identifying shared genomic features, as well as new nonparametric empirical Bayes approaches for high-dimensional integrative classification and regression. The proposed theoretical research will explore the fundamental limits of these problems. The results of this project will lead to more powerful and rigorous methods and theory for integrative analysis in genomics and elsewhere.

该项目的主要目标是开发新的方法和理论，以应对整合基因组学带来的统计挑战，整合基因组学是基因组学研究中的一系列定量方法，围绕多个数据集的联合分析。 综合分析具有巨大的潜力，可以促成基因组学的下一波科学发现，也是新兴数据科学概念的重要组成部分。本项目旨在开发新的分析程序，用于1）检测两个或多个数据集是否具有相同的重要基因组特征，2）识别这些特征，3）利用它们来改进基因组预测模型。本项目通过将整合基因组学中的重要问题纳入尖端统计问题，包括信号检测，多重测试和高维分类。拟议的方法学研究将开发新的非参数检验和错误发现率控制程序，用于检测和识别共享的基因组特征，以及新的非参数经验贝叶斯方法，用于高维综合分类和回归。拟议中的理论研究将探讨这些问题的基本限制。该项目的结果将为基因组学和其他领域的综合分析带来更强大和更严格的方法和理论。