A statistical perspective on gene expression data analysis
A statistical perspective on gene expression data analysis
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DOI：
10.1002/sim.1350
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发表时间：
2003-02-15
影响因子：
2
通讯作者：
Panageas, KS
中科院分区：
文献类型：
--
作者：
Satagopan, JM;Panageas, KS
生物技术的快速发展引起了人们对使用寡核苷酸和斑点cdna基因表达微阵列进行医学研究的越来越大的兴趣。这些阵列正被广泛用于了解各种疾病的潜在遗传结构，最终目标是提供更好的诊断、预防和治疗。这项技术允许同时测量数千个基因的表达水平，从而产生大量数据。统计学家的作用对于基因表达研究的成功设计以及对由此产生的大量数据的分析和解释至关重要。本文讨论了基因表达研究中常见的假设，并描述了一些适用于解决这些假设的统计方法。提供了执行统计方法的S-PLUS和SAS程序。一项未发表的肿瘤学研究的基因表达数据被用来说明这些方法。版权所有(C)2003 John Wiley Sons，Ltd.
Rapid advances in biotechnology have resulted in an increasing interest in the use of oligonucleotide and spotted cDNA gene expression microarrays for medical research. These arrays are being widely used to understand the underlying genetic structure of various diseases, with the ultimate goal to provide better diagnosis, prevention and cure. This technology allows for measurement of expression levels from several thousands of genes simultaneously, thus resulting in an enormous amount of data. The role of the statistician is critical to the successful design of gene expression studies, and the analysis and interpretation of the resulting voluminous data. This paper discusses hypotheses common to gene expression studies, and describes some of the statistical methods suitable for addressing these hypotheses. S-plus and SAS codes to perform the statistical methods are provided. Gene expression data from an unpublished oncologic study is used to illustrate these methods. Copyright (C) 2003 John Wiley Sons, Ltd.