Statistical Considerations for Analysis of Microarray Experiments

被引：20
|
作者：
Owzar, Kouros [1 ]
Barry, William T. [1 ]
Jung, Sin-Ho [1 ]
机构：
[1] Duke Univ, Duke Univ CALGB Stat Ctr, Dept Biostat & Bioinformat, Durham, NC 27710 USA
来源：
CTS-CLINICAL AND TRANSLATIONAL SCIENCE | 2011年 / 4卷 / 06期
关键词：
microarrays; preprocessing; statistical inference; multiple testing; unsupervised learning; supervised learning; overfitting; validation; pathways; clinical trials; power; software; FALSE DISCOVERY RATE; GENE-EXPRESSION; FUNCTIONAL CATEGORIES; NORMALIZATION METHODS; SAMPLE-SIZE; CLASSIFICATION; CARCINOMAS;
D O I：
10.1111/j.1752-8062.2011.00309.x
中图分类号：
R-3 [医学研究方法]; R3 [基础医学];
学科分类号：
1001 ;
摘要：
Microarray technologies enable the simultaneous interrogation of expressions from thousands of genes from a biospecimen sample taken from a patient. This large set of expressions generates a genetic profile of the patient that may be used to identify potential prognostic or predictive genes or genetic models for clinical outcomes. The aim of this article is to provide a broad overview of some of the major statistical considerations for the design and analysis of microarrays experiments conducted as correlative science studies to clinical trials. An emphasis will be placed on how the lack of understanding and improper use of statistical concepts and methods will lead to noise discovery and misinterpretation of experimental results. Clin Trans Sci 2011; Volume 4: 466477
引用
收藏
页码：466 / 477
页数：12
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