Multivariate Statistical Methods for Genomic Data Integration

基因组数据整合的多元统计方法

• 批准号: 1262538
• 负责人: Debashis Ghosh
• 金额: $ 54.56万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-06-01 至 2014-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1262538&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.

该项目解决了许多使用基因组数据的数据分析师所面临的关键建模问题。对于一组个体或观察，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的基因组部分。虽然通常假设不同的数据类型在无条件或有条件的意义上是线性相关的，但在许多设置中，相关性的性质是未知的。本研究的重点是多维分析方法与高维基因组数据，放松线性假设。在项目过程中将研究两类问题。第一个是隐马尔可夫模型，第二个是多个测试程序，其使用已成为常见的基因组数据集。该项目提出了两种类型方法的新型多变量扩展，其目标是以合理的理论统计原理为特征，同时在大数据集上计算可行。将使用若干真实的数据集以及通过模拟研究对该方法进行评估，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优先考虑在任何生物环境中进行后续研究的分子。这将是有用的生物学家和科学家研究疾病的过程谁希望找到新的治疗目标或进一步推进基本病因学的理解。该项目的教育目标包括宾夕法尼亚州立大学研究生的新课程组成部分和统计学研究生的培训。