Significance analysis of functional categories in gene expression studies: a structured permutation approach
Significance analysis of functional categories in gene expression studies: a structured permutation approach
复制标题
DOI：
10.1093/bioinformatics/bti260
复制
发表时间：
2005-05-01
期刊：
影响因子：
5.8
通讯作者：
Wright, FA
中科院分区：
文献类型：
--
作者：
Barry, WT;Nobel, AB;Wright, FA
动机：在高通量基因组学和蛋白质组学实验中，研究人员在一组实验条件下监测表达。为了获得更广泛的生物现象的理解，研究人员直到最近才被限制到事后分析显着的基因lists.Method：我们描述了一个一般的框架，功能和表达的显着性分析（SAFE），进行有效的测试基因类别从头开始。SAFE是一个两阶段的，基于排列的方法，可以应用到各种实验设计，占未知的基因之间的相关性，并使基于排列的估计错误率.Results：SAFE的实用性和灵活性说明与基因本体论和蛋白质家族数据库的基础上的人类肺癌和基因类别的微阵列数据集。在（1）肿瘤与正常组织、（2）多种肿瘤亚型和（3）生存时间的比较中观察到显著的基因类别。
Motivation: In high-throughput genomic and proteomic experiments, investigators monitor expression across a set of experimental conditions. To gain an understanding of broader biological phenomena, researchers have until recently been limited to post hoc analyses of significant gene lists.Method: We describe a general framework, significance analysis of function and expression (SAFE), for conducting valid tests of gene categories ab initio. SAFE is a two-stage, permutation-based method that can be applied to various experimental designs, accounts for the unknown correlation among genes and enables permutation-based estimation of error rates.Results: The utility and flexibility of SAFE is illustrated with a microarray dataset of human lung carcinomas and gene categories based on Gene Ontology and the Protein Family database. Significant gene categories were observed in comparisons of (1) tumor versus normal tissue, (2) multiple tumor subtypes and (3) survival times.