Verducci Joseph S, Melfi Vincent F, Lin Shili, Wang Zailong, Roy Sashwati, Sen Chandan K
Davis Heart and Lung Research Institute, Department of Surgery, The Ohio State University, Columbus, Ohio, USA.
Physiol Genomics. 2006 May 16;25(3):355-63. doi: 10.1152/physiolgenomics.00314.2004. Epub 2006 Mar 22.
DNA microarray represents a powerful tool in biomedical discoveries. Harnessing the potential of this technology depends on the development and appropriate use of data mining and statistical tools. Significant current advances have made microarray data mining more versatile. Researchers are no longer limited to default choices that generate suboptimal results. Conflicting results in repeated experiments can be resolved through attention to the statistical details. In the current dynamic environment, there are many choices and potential pitfalls for researchers who intend to incorporate microarrays as a research tool. This review is intended to provide a simple framework to understand the choices and identify the pitfalls. Specifically, this review article discusses the choice of microarray platform, preprocessing raw data, differential expression and validation, clustering, annotation and functional characterization of genes, and pathway construction in light of emergent concepts and tools.
DNA微阵列是生物医学发现中的一种强大工具。利用这项技术的潜力取决于数据挖掘和统计工具的开发与恰当使用。当前的重大进展使微阵列数据挖掘更具通用性。研究人员不再局限于产生次优结果的默认选择。通过关注统计细节，可解决重复实验中相互矛盾的结果。在当前动态环境中，对于打算将微阵列作为研究工具的研究人员而言，有许多选择和潜在陷阱。本综述旨在提供一个简单框架，以理解这些选择并识别其中的陷阱。具体而言，这篇综述文章根据新出现的概念和工具，讨论了微阵列平台的选择、原始数据预处理、差异表达与验证、聚类、基因注释与功能表征以及通路构建。
