Statistical Design & Analysis of Microarray Experiments

描述（由申请人提供）：DNA微阵列是一种新的和有前途的生物技术，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。这个项目的广泛的，长期的目标是开发新的统计方法的设计和分析的DNA微阵列实验。该提案的具体目标分为四个领域，所有这些领域都与提高微阵列实验的效率和可靠性有关，从早期设计和预处理阶段到更高层次的分析。 实验设计。适当的实验设计是必不可少的，以确保生物学问题得到准确和精确的实验约束的答案。将为时间序列和多因子实验开发灵活的设计和分析方法，这些实验监测基因表达随时间推移对治疗和细胞类型等因素的反应。二.预处理。图像分析和标准化是所有微阵列实验的组成部分，并且可以对更高水平的分析产生实质性影响。将导出斑点和载玻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。提出了基于鲁棒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的控制序列。伊利诺伊州模式发现和识别。提出了聚类、区分和多重检验的新方法，以阐明基因表达水平与其他协变量或反应之间的关联。这包括评估治疗干预的效果，发现时间或空间基因表达模式，以及鉴定与癌症发病率和生存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基于R语言的统计计算包中实现。为便于使用和整合生物信息资源，将提供网络浏览器界面。

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.