Computational framework for analysis of microarray gene expression data

描述(由申请人提供)：识别根据所研究的实验条件进行差异调控的转录本是分析DNA微阵列数据的关键步骤之一。目前使用的统计方法对于在差异表达研究中普遍存在的少量生物重复的实验变得特别无效。我们建议开发和验证一个新的数字框架来识别差异表达的转录本，重点是分析少量重复和中等表达水平的基因的实验。提出的方法是基于一种新的非参数方法来评估微阵列数据中的噪声分布，这些噪声分布直接来自分析的数据集。将实施三种不同的、单变量和多变量的方法来识别差异表达的基因，并将它们的结果与领先的先进统计方法的结果进行比较。在第一阶段的可行性研究中，我们将分析至少九个相似程度不同的正常组织在大鼠和小鼠中的差异基因表达。将使用通过Affymetrix微阵列获得的来自SymAtlas数据库(诺华研究基金会基因组研究所)的公开数据。新开发的数值方法的实用性将通过使用功能分析(如果有功能注释)和/或定量聚合酶链式反应分析对已确定的基因组生物标记物进行生物学和/或实验验证来确定。与公共健康相关：DNA微阵列技术能够同时分析在特定生物体、细胞或组织中表达的数千个转录本。它目前的应用包括基因图谱、基因调控研究、疾病生物标志物发现、毒理基因组学、药物基因组学以及临床诊断和预后。尽管最近取得了令人印象深刻的技术进步，但实现微阵列技术全部潜力的主要瓶颈仍然存在，包括功能基因注释不完整和缺乏有效的计算数据分析工具。该项目开发的分析方法将提高在生物重复数较少的实验中可靠地识别差异表达基因的能力，这将提高这项技术的整体有效性，并降低微阵列基因表达研究的成本。

DESCRIPTION (provided by applicant): Identification of transcripts that are differentially regulated in response to studied experimental conditions is one of critical steps in analysis of DNA microarray data. Currently employed statistical approaches become particularly ineffective for experiments with small number of biological replicates, which are prevalent in the differential expression studies. We propose to develop and validate a novel numerical framework for identification of differentially expressed transcripts, with emphasis on analysis of experiments with small number of replicates and genes with moderate levels of expression. The proposed approach is based on a novel, non-parametric method for assessment of noise distributions in microarray data, which are derived directly from the analyzed data set. Three distinct, univariate and multivariate methods for identification of differentially expressed genes will be implemented and their results will be compared to the results of leading advanced statistical methods. In the Phase I feasibility study we will analyze differential gene expression between at least nine normal tissues with varying levels of similarity, in rat and mouse. Publicly available data from SymAtlas database (Genomics Institute of the Novartis Research Foundation), obtained with Affymetrix microarrays, will be employed. The utility of newly developed numerical methods will be established through biological and/or experimental validation of identified genomic biomarkers using functional analysis (if functional annotation is available) and/or quantitative polymerase chain reaction analysis. PUBLIC HEALTH RELEVANCE: DNA microarray technology enables simultaneous profiling of thousands of transcripts expressed in particular organism, cells or tissues. Its current applications include gene profiling, gene regulation studies, disease biomarker discovery, toxicogenomics, pharmacogenomics, and clinical diagnostics and prognosis. Despite recent impressive technological advances, major bottlenecks to the realization of the full potential of the microarray technology exist and include incomplete functional gene annotation and the lack of effective computational data analysis tools. The analysis methods developed in this project will improve the ability to reliably identify differentially expressed genes in experiments with small number of biological replicates, which will improve the overall effectiveness of this technology and reduce the cost of microarray gene expression studies.