Collaborative Research: Nonparametric Methods for Emerging Technologies in Bioinformatics

提出的研究针对蛋白质和基因表达微阵列实验中两个重要的统计问题：(1)蛋白质裂解物阵列的定量，这是一种新兴的技术，可以同时直接测量不同裂解组织样品的蛋白质含量；（II）建立探针水平的基因表达数据，特别是外显子平铺阵列，以检测选择性剪接，这是导致人类多样性的重要过程。研究人员提供了一个统计框架，允许在非参数回归模型中未知的回归值，并应用于蛋白质裂解物阵列数据的量化。研究人员还开发了混合效应模型的分位数回归方法，该方法适用于检测治疗和/或相互作用效应，而不需要对模型进行参数分布假设。研究人员建议利用跨基因信息来提高小样本问题推理方法的性能。该提案中提出的新原则在统计上的有趣之处，超出了它们对基因和蛋白质表达数据的直接应用。人类基因组计划的发现强调了细胞调控、蛋白质和基因之间相互作用的复杂性。人们普遍认为，生物功能和生物活性是由与蛋白质相互作用的基因亚群以高度受控的方式控制的。微阵列等高通量技术对于同时研究大量生物组分具有重要价值。特别是，蛋白质裂解物和外显子平铺阵列已经开始在癌症研究和其他生物医学研究中发挥重要作用。然而，这些技术的可靠结论依赖于对蛋白质组学和基因组学数据的适当统计分析。本文提出的统计方法对于蛋白质裂解物阵列的定量分析和通过外显子平铺阵列检测选择性剪接是及时而重要的。所提出的非参数方法特别有吸引力，因为它在模拟探针水平的基因表达数据以及蛋白质裂解物阵列数据方面具有灵活性和适应性。

The proposed research targets two important statistical problems in protein and gene expression microarray experiments: (I) quantification of the protein lysate arrays, an emerging technology for directly measuring protein contents of different lysed tissue samples simultaneously; (II) modeling probe level gene expressiondata, in particular, the exon tiling arrays to detect alternative splicing, which is an essential process resulting in much of the human diversity. The investigators provide a statistical framework that allows for unknown regressor values in a nonparametric regression model, with applications to the quantification of protein lysate array data. The investigators also develop a quantile regression approach for mixed-effect models that are appropriate for detecting treatment and/or interaction effects without parametric distributional assumptions on the model. The investigators propose to make use of information across genes to enhance performance of the inferential methods in small sample problems. The new principles developed in the proposal are statistically interesting beyond their direct applications to gene and protein expression data.Findings from the Human Genome Project highlight the intricacy of interactions between cell regulation,proteins and genes. It is generally understood that biological functions and biological activities are controlled by subsets of genes interacting with proteins in a highly controlled manner. High throughput technologies such as microarrays are valuable for studying a large number of biological components simultaneously. In particular, the protein lysate and exon tiling arrays have begun to show their important roles in cancer study and other biomedical research. However, sound conclusions from these technologies depend on appropriate statistical analysis of the proteomic and genomic data. The statistical methods developed in the proposal are timely and important for proper quantification of the protein lysate arrays and for detecting alternative splicing through the exon tiling arrays. The nonparametric approach proposed is especially appealing due to its flexibility and adaptivity in modeling probe level gene expression data as well as protein lysate array data.