Statistical Design & Analysis of Microarray Experiments

描述(申请人提供)：DNA微阵列是一种新的、很有前途的生物技术，它允许同时监测数千个基因的表达水平。微阵列正越来越多地应用于生物和医学研究，以解决广泛的问题，如肿瘤的分类和宿主基因组对细菌感染的反应的研究。该项目的长期目标是为DNA微阵列实验的设计和分析开发新的统计方法。该建议的具体目标分为四个方面，从早期的设计和前处理阶段到更高层次的分析，即实验设计，所有这些方面都与提高微阵列实验的效率和可靠性有关。适当的实验设计对于确保在给定实验约束的情况下准确和准确地回答生物学问题是至关重要的。将为时间序列和多因素实验开发灵活的设计和分析方法，这些实验监测随着时间的推移对治疗和细胞类型等因素的基因表达反应。二、前置处理。图像分析和归一化是所有微阵列实验的组成部分，可以对更高水平的分析产生重大影响。将得出现场和幻灯片质量统计数据，以及将这些统计数据纳入后续分析的程序。提出了基于稳健局部回归的归一化方法，以适应不同类型的染料偏差并利用阵列上斑点的控制序列。生病了。模式发现和识别。为了阐明基因表达水平与其他协变量或反应之间的关系，提出了新的聚类、区分和多重测试方法。这包括评估治疗干预措施的效果，发现时间或空间基因表达模式，以及确定与癌症发病率和存活率等临床结果相关的基因。四、软件开发。作为该项目的一部分开发的统计方法将在用于统计计算的R语言基础上建立的程序包中实施。为便利生物信息资源的使用和整合，将提供一个网络浏览器界面。

DESCRIPTION (provided by applicant): DNA microarrays are a new and promising biotechnology which allow the monitoring of expression levels for thousands of genes simultaneously. Microarrays are being applied increasingly in biological and medical research to address a wide range of problems, such as the classification of tumors and the study of host genomic responses to bacterial infections. The broad, long-term objectives of this project are to develop novel statistical methods for the design and analysis of DNA microarray experiments. The specific aims of the proposal fall into four areas, all of which are concerned with improving the efficiency and reliability of microarray experiments, from the early design and pre-processing stages to higher level analyses.I. Experimental design. Proper experimental design is essential to ensure that biological questions are answered accurately and precisely given experimental constraints. Flexible designs and methods of analysis will be developed for time-series and multifactorial experiments, which monitor the gene expression response over time for factors such as treatment and cell type. II. Pre-processing. Image analysis and normalization are components of all microarray experiments and can have a substantial impact on higher level analyses. Spot and slide quality statistics will be derived as well as procedures for incorporating these statistics in subsequent analyses. Normalization methods based on robust local regression are proposed to accommodate different types of dye biases and to exploit control sequences spotted on the array. Ill. Pattern discovery and recognition. New methods for clustering, discrimination, and multiple testing are proposed in order to elucidate associations between gene expression levels and other covariates or responses. This includes assessing the effects of treatment interventions, the discovery of temporal or spatial gene expression patterns, and the identification of genes associated with clinical outcomes such as cancer incidence and survival. IV. Software development. Statistical methods developed as part of this project will be implemented in packages built on the R language for statistical computing. To facilitate use and integration with biological information resources a web-browser interface will be provided.