Evaluation of normalization methods for microarray data.
Evaluation of normalization methods for microarray data.
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DOI：
10.1186/1471-2105-4-33
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发表时间：
2003-09-02
影响因子：
3
通讯作者：
Simon R
中科院分区：
文献类型：
--
作者：
Park T;Yi SG;Kang SH;Lee S;Lee YS;Simon R
微阵列技术允许同时监测数千个基因的表达水平。这项新技术有助于我们更系统地了解基因调控以及基因间的相互作用。然而，在微阵列实验中，观察到许多不期望的系统性变化。即使在重复实验中，也经常观察到一些变化。标准化是去除影响测量的基因表达水平的一些变异来源的过程。虽然已经提出了许多归一化方法，但很难决定哪种方法性能最好。归一化在微阵列数据分析的前期阶段起着重要作用。随后的分析结果高度依赖于归一化。在本文中，我们使用重复的载玻片之间的变异性来比较归一化方法的性能。我们还比较了归一化方法的偏差和均方误差使用模拟数据。我们的研究结果表明，强度相关的归一化通常比全局归一化方法更好，线性和非线性归一化方法的性能相似。这些结论是基于对在实验中获得的36个cDNA微阵列的3，840个基因的分析，以寻找皮层干细胞神经元分化过程中基因表达谱的变化。模拟研究证实了我们的发现。
Microarray technology allows the monitoring of expression levels for thousands of genes simultaneously. This novel technique helps us to understand gene regulation as well as gene by gene interactions more systematically. In the microarray experiment, however, many undesirable systematic variations are observed. Even in replicated experiment, some variations are commonly observed. Normalization is the process of removing some sources of variation which affect the measured gene expression levels. Although a number of normalization methods have been proposed, it has been difficult to decide which methods perform best. Normalization plays an important role in the earlier stage of microarray data analysis. The subsequent analysis results are highly dependent on normalization. In this paper, we use the variability among the replicated slides to compare performance of normalization methods. We also compare normalization methods with regard to bias and mean square error using simulated data. Our results show that intensity-dependent normalization often performs better than global normalization methods, and that linear and nonlinear normalization methods perform similarly. These conclusions are based on analysis of 36 cDNA microarrays of 3,840 genes obtained in an experiment to search for changes in gene expression profiles during neuronal differentiation of cortical stem cells. Simulation studies confirm our findings.