Cross-study validation and combined analysis of gene expression microarray data
Cross-study validation and combined analysis of gene expression microarray data
复制标题
DOI：
10.1093/biostatistics/kxm033
复制
发表时间：
2008-04-01
期刊：
影响因子：
2.1
通讯作者：
Gabrielson, Edward
中科院分区：
文献类型：
--
作者：
Garrett-Mayer, Elizabeth;Parmigiani, Giovanni;Gabrielson, Edward
使用基于杂交的阵列在基因组规模上对转录水平的调查已经导致了我们对许多人类疾病的生物学的理解的巨大进步。与此同时，这些调查也引起了争议，因为结论的概率性质以及解决同一生物学问题的研究结果之间出现的明显差异。在这篇文章中，我们提出了简单有效的数据分析和可视化工具，用于衡量一项研究的结果被其他研究复制的程度，并将多项研究整合到一项分析中。我们描述了这些方法的背景下，乳腺癌的研究和说明，它是可能的，以确定一个实质性的生物相关的人类基因组的子集内的杂交结果是可靠的。该子集通常随所用平台、研究的组织和取样的群体而变化。尽管存在重要差异，但也可以开发简单的表达测量方法，以便在平台，研究，实验室和人群之间进行比较。重要的生物信号通常被保留或增强。交叉研究验证和微阵列结果的组合需要仔细但不过于复杂的统计思维，并且可以成为基因组分析的常规组成部分。
Investigations of transcript levels on a genomic scale using hybridization-based arrays have led to formidable advances in our understanding of the biology of many human illnesses. At the same time, these investigations have generated controversy because of the probabilistic nature of the conclusions and the surfacing of noticeable discrepancies between the results of studies addressing the same biological question. In this article, we present simple and effective data analysis and visualization tools for gauging the degree to which the findings of one study are reproduced by others and for integrating multiple studies in a single analysis. We describe these approaches in the context of studies of breast cancer and illustrate that it is possible to identify a substantial biologically relevant subset of the human genome within which hybridization results are reliable. The subset generally varies with the platforms used, the tissues studied, and the populations being sampled. Despite important differences, it is also possible to develop simple expression measures that allow comparison across platforms, studies, laboratories and populations. Important biological signals are often preserved or enhanced. Cross-study validation and combination of microarray results requires careful, but not overly complex, statistical thinking and can become a routine component of genomic analysis.