Collaborative Research: Nonparametric Methods for Emerging Technologies in Bioinformatics

合作研究：生物信息学新兴技术的非参数方法

• 批准号: 0706963
• 负责人: Huixia Wang
• 金额: --
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-01 至 2011-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0706963&HistoricalAwards=false
• 关键词: Collaborative; Research; Nonparametric; Methods; Emerging

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.

拟议的研究针对蛋白质和基因表达微阵列实验中的两个重要统计学问题：(I)蛋白质裂解物阵列的量化，这是一种新兴的同时测量不同裂解组织样本的蛋白质含量的技术；(Ii)对探针水平的基因表达数据进行建模，特别是外显子拼接阵列，以检测选择性剪接，这是导致人类多样性的一个基本过程。研究人员提供了一个统计框架，允许非参数回归模型中的未知回归量值，并应用于蛋白质裂解物阵列数据的量化。研究人员还为混合效应模型开发了一种分位数回归方法，该方法适合于检测治疗和/或相互作用的影响，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的信息来提高推理方法在小样本问题上的性能。除了直接应用于基因和蛋白质表达数据之外，提案中开发的新原理在统计学上也很有趣。来自人类基因组计划的发现突显了细胞调节、蛋白质和基因之间相互作用的复杂性。一般认为，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。高通量技术，如微阵列，对于同时研究大量的生物成分是有价值的。特别是，蛋白质裂解产物和外显子拼接阵列已经开始显示出它们在癌症研究和其他生物医学研究中的重要作用。然而，来自这些技术的可靠结论取决于对蛋白质组和基因组数据的适当统计分析。本提案中开发的统计方法对于蛋白质裂解物阵列的适当定量和通过外显子拼接阵列检测替代剪接是及时和重要的。提出的非参数方法因其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的灵活性和适应性而特别吸引人。