The effect of normalization on microarray data analysis
The effect of normalization on microarray data analysis
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DOI：
10.1089/dna.2004.23.635
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发表时间：
2004-10-01
影响因子：
3.1
通讯作者：
Wilkins, D
中科院分区：
文献类型：
--
作者：
Ding, YY;Wilkins, D
本文介绍了微阵列分析中常用的几种标准化方法，并比较了这些方法对微阵列数据的影响。还讨论了背景扣除的重要性。研究主要集中在三个部分。第一个使用三种统计方法：t 检验、Wilcoxon 符号秩检验和符号检验来测量背景扣除数据和非背景扣除数据之间的差异。研究的第二部分使用相同的三种统计方法来比较使用不同归一化方法归一化的数据是否会产生相似的结果。研究的第三部分重点关注这些不同归一化的数据是否会影响基因选择（降维）的结果。对多个数据集进行比较以帮助识别相似性模式。这项研究的结论是，背景扣除可以产生影响，特别是对于一些数据质量较差的数据集。在大多数情况下，归一化方法的选择几乎没有什么区别，因为这些方法会产生类似的归一化数据。但是，根据第三部分的分析，我们发现，当对这些不同标准化的数据进行基因选择时，会得到一些不同的基因集。因此，归一化方法的选择可能会对最终分析产生一些影响。
This paper contains a description of several common normalization methods used in microarray analysis, and compares the effect of these methods on microarray data. The importance of background subtraction is also addressed. The research focuses on three parts. The first uses three statistical methods: t-test, Wilcoxon signed rank test, and sign test to measure the difference between background subtracted data and nonbackground subtracted data. The second part of the study uses the same three statistical methods to compare whether data normalized with different normalization methods yield similar results. The third part of the study focuses on whether these differently normalized data will influence the result of gene selection (dimension reduction). The comparisons are done for several data sets to help identify similarity patterns. The conclusion of this study is that background subtraction can make a difference, especially for some data sets with poorer quality data. The choice of normalization method, for the most part, makes little difference in the sense that the methods produce similarly normalized data. But, based on the third part of analysis, we found that when gene selection is performed on these differently normalized data, somewhat different gene sets are obtained. Thus, the choice of normalization method will likely have some effect on the final analysis.