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.

该项目解决了许多与基因组数据一起工作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结果，生成了许多不同类型的高吞吐量实验数据集，然后问题就变成了如何建模这些数据。在许多问题中，目标是优先考虑一个希望研究的基因组的哪些部分。尽管通常认为不同的数据类型在无条件或条件意义上是线性相关的，但在许多情况下，相关性的性质尚不清楚。这项研究重点是使用高维基因组数据来放松线性假设的多元分析方法。在项目过程中将研究两类问题。第一个是隐藏的马尔可夫模型，第二个是多个测试过程，其使用已与基因组数据集变得司空见惯。该项目提出了两种方法的新型多元扩展，其目标是以合理的理论统计原理为特征，同时在大数据集中同时进行计算。该方法将使用几个实际数据集以及通过模拟研究来评估。这项工作将涉及统计学家与生物学家之间的相互作用。这项工作的广泛使用将是在任何生物环境中优先考虑分子进行后续研究。这对于希望找到新的治疗靶标或进一步提高基本病因理解的生物学家和科学家将很有用。该项目的教育目标包括宾夕法尼亚州研究生的新课程组成部分以及统计研究生的培训。