Development of High-Dimensional Data Analysis Methods for the Identification of Differentially Expressed Gene Sets

该提案的主要目标是开发改进的统计方法，用于检测在两种或更多种条件下差异表达的基因组，并应用这些方法来发现控制食物摄入、营养利用、能量调节和代谢的新的遗传和生理机制。 发达的统计方法将提供强有力的替代测试的富集或过度代表性，已成为流行的工具来解释微阵列实验。 所提出的方法通过以下方式获得优于现有方法的优点：（1）识别、说明和利用基因之间的依赖性;（2）保持关于基因集表达分布之间的差异程度的连续信息;（3）通过跨处理比较基因集而不是将基因集彼此比较来识别感兴趣的基因集;以及（4）捕获关于包含在联合表达分布中的差分表达的信息，而不是仅使用边缘分布。 除了在传统的微阵列实验中用于识别差异表达的基因集之外，所提出的方法还提供了一种新的和强大的方法来识别控制基因网络表达的遗传位点。 由统计学家和生物学家组成的综合研究团队将通过对其渐近特性的理论研究，通过比较其在模拟数据集上的性能来确定所提出的方法中的最佳方法，这些模拟数据集旨在模拟真实的数据集中发现的结构，并通过权衡其应用程序提供的生物学见解的价值来从各种微阵列实验中获得实际数据。 在这项研究中使用的渐近框架认为测试程序的统计特性的数据向量的维度（一组中的基因数）和样本量（实验单位的数量）变大。 这样一个框架允许使用非常高维的数据的方法进行评估，并产生从统计的角度来看本质上是有趣的结果。 开发的方法将用于研究猪的食物摄入和能量调节的遗传控制，并发现控制作为人类肥胖模型的小鼠群体中基因网络表达的遗传区域。 这些研究提供的见解可用于开发人类肥胖症的治疗策略。 此外，所提出的方法有更广泛的应用，几乎任何基于微阵列的差异基因表达的调查。 应用范围从鉴定在区分癌组织和非癌组织中起作用的基因组到鉴定对开发适合转化为生物燃料的高质量植物材料重要的基因组。 这项工作的总体目标是为科学研究人员提供强有力的工具，以确定各种生物现象背后最重要的基因。

The main objectives of this proposal are to develop improved statistical methods for detecting sets of genes that are differentially expressed across two or more conditions and to apply these methods to discover new genetic and physiological mechanisms that control food intake, nutrient utilization, energy regulation, and metabolism. The developed statistical methods will provide powerful alternatives to tests of enrichment or overrepresentation that have become popular tools for interpreting microarray experiments. The proposed methods gain advantages over existing methods by (1) recognizing, accounting for, and utilizing dependence among genes; (2) maintaining continuous information about the degree of difference between gene set expression distributions; (3) identifying interesting gene sets by comparison of gene sets across treatments rather than comparing gene sets to one another; and (4) capturing information about differential expression contained in the joint expression distributions rather than using only marginal distributions. In addition to their use for identifying differentially expressed gene sets in traditional microarray experiments, the proposed methods offer a new and powerful approach for identifying genetic loci that control the expression of gene networks. The integrated research team of statisticians and biologists will identify the best of the proposed methods by theoretical study of their asymptotic properties, by comparisons of their performance on simulated data sets designed to mimic structures found in real data sets, and by weighing the value of biological insights provided by their application to actual data from a variety of microarray experiments. The asymptotic framework used in this research considers the statistical properties of the testing procedures as both the dimension of the data vectors (number of genes in a set)and the sample size (number of experimental units) grow large. Such a framework permits evaluation of methods for use on data of very high dimension and produces results that are intrinsically interesting from the statistical point of view. The developed methods will be used to investigate genetic control of food intake and energy regulation in pigs and to discover genetic regions that control the expression of gene networks in a population of mice that serve as a model for human obesity. The insights provided by these studies may be used to develop treatment strategies for human obesity. In addition, the proposed methods have much broader application to nearly any microarray-based investigation of differential gene expression. Applications range from the identification of sets of genes that play a role in distinguishing cancerous tissue from non-cancerous tissue to the identification of sets of genes important for developing high-quality plant material suitable for conversion to biofuel. The general goal of this work is to provide scientific researchers with powerful tools for identifying the most important genes behind a wide variety of biological phenomena.