Multivariate Statistical Methods for Genomic Data Integration

基因组数据整合的多元统计方法

• 批准号: 1457935
• 负责人: Debashis Ghosh
• 金额: $ 47.04万
• 依托单位: University of Colorado at Denver
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-07-01 至 2017-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1457935&HistoricalAwards=false
• 关键词: Multivariate; Statistical; Methods; Genomic; Data

This project addresses a key modeling issue faced by many data analysts working with genomic data. For a set of individuals or observations, many different types of high throughput experimental datasets are generated, and the question then becomes how to model these data. In many problems, the goal is to prioritize which parts of the genome one wishes to study. While it is commonly assumed that the different data types are linearly correlated in either an unconditional or conditional sense, in many settings the nature of the correlation is unknown. This research focuses on multivariate methods of analysis with high-dimensional genomic data that relax the linearity assumption. Two classes of problems will be studied during the course of the project. The first is Hidden Markov Models and the second is multiple testing procedures, whose use have become commonplace with genomic datasets. This project proposes novel multivariate extensions of both types of method with a goal of being characterized by sound theoretical statistical principles while simultaneously being computationally feasible on big datasets. The methodology will be evaluated using several real datasets as well as through simulation studies.This work will involve an interplay between statisticians and biologists. The broader use of this work will be to prioritize molecules for follow-up studies in any biological setting. It will be useful for biologists and scientists studying disease processes who wish to find new therapeutic targets or further advance basic etiological understanding. The educational goals of the project include new course components for graduate students at Penn State and training of graduate students in Statistics.

该项目解决了许多处理基因组数据的数据分析师面临的关键建模问题。对于一组个体或观察结果，会生成许多不同类型的高通量实验数据集，然后问题就变成了如何对这些数据进行建模。在许多问题中，目标是优先考虑希望研究基因组的哪些部分。虽然通常假设不同的数据类型在无条件或条件意义上线性相关，但在许多设置中，相关性的性质是未知的。本研究重点关注放宽线性假设的高维基因组数据的多变量分析方法。项目过程中将研究两类问题。第一个是隐马尔可夫模型，第二个是多重测试程序，其使用在基因组数据集中已变得司空见惯。该项目提出了这两种方法的新颖的多变量扩展，其目标是具有合理的理论统计原理，同时在大数据集上计算可行。该方法将使用几个真实数据集以及通过模拟研究进行评估。这项工作将涉及统计学家和生物学家之间的相互作用。这项工作的更广泛用途将是在任何生物环境中优先考虑用于后续研究的分子。对于研究疾病过程、希望找到新的治疗靶点或进一步推进基本病因学了解的生物学家和科学家来说，它将很有用。该项目的教育目标包括为宾夕法尼亚州立大学研究生提供新的课程内容以及对研究生进行统计学培训。