用于微阵列基因表达数据统计分析的错误发现率范式。
False discovery rate paradigms for statistical analyses of microarray gene expression data.
作者信息
Cheng Cheng, Pounds Stan
机构信息
Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN, USA.
出版信息
Bioinformation. 2007 Apr 10;1(10):436-46. doi: 10.6026/97320630001436.
Abstract
The microarray gene expression applications have greatly stimulated the statistical research on the massive multiple hypothesis tests problem. There is now a large body of literature in this area and basically five paradigms of massive multiple tests: control of the false discovery rate (FDR), estimation of FDR, significance threshold criteria, control of family-wise error rate (FWER) or generalized FWER (gFWER), and empirical Bayes approaches. This paper contains a technical survey of the developments of the FDR-related paradigms, emphasizing precise formulation of the problem, concepts of error measurements, and considerations in applications. The goal is not to do an exhaustive literature survey, but rather to review the current state of the field.
摘要
微阵列基因表达应用极大地推动了关于大规模多重假设检验问题的统计研究。目前该领域有大量文献，并且大规模多重检验基本上有五种范式：错误发现率（FDR）控制、FDR估计、显著性阈值标准、族系错误率（FWER）或广义FWER（gFWER）控制以及经验贝叶斯方法。本文对与FDR相关的范式发展进行了技术综述，重点强调问题的精确表述、误差度量概念以及应用中的考虑因素。目的不是进行详尽的文献综述，而是回顾该领域的当前状态。
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