基因表达微阵列数据分析揭开神秘面纱。

Peter C Roberts
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引用次数: 33

摘要

基因表达微阵列的使用越来越多,并将结果数据存入公共存储库,这意味着更多的研究人员对直接使用该技术或通过对公开可用数据进行元分析感兴趣。可用于数据分析的工具通常是为该领域的专家开发的,这使得一般研究界很难使用它们。对于那些有兴趣进入这个领域的人,尤其是那些没有统计学背景的人来说,很难理解为什么实验结果会如此多变。本综述的目的是通过一个典型的微阵列实验的工作流程,以表明在每一步所做的决定,从平台的选择到统计分析方法,再到生物解释,都是这种可变性的来源。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Gene expression microarray data analysis demystified.

The increasing use of gene expression microarrays, and depositing of the resulting data into public repositories, means that more investigators are interested in using the technology either directly or through meta analysis of the publicly available data. The tools available for data analysis have generally been developed for use by experts in the field, making them difficult to use by the general research community. For those interested in entering the field, especially those without a background in statistics, it is difficult to understand why experimental results can be so variable. The purpose of this review is to go through the workflow of a typical microarray experiment, to show that decisions made at each step, from choice of platform through statistical analysis methods to biological interpretation, are all sources of this variability.

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