Rao Youlan, Lee Yoonkyung, Jarjoura David, Ruppert Amy S, Liu Chang-Gong, Hsu Jason C, Hagan John P
The Ohio State University, USA.
Stat Appl Genet Mol Biol. 2008;7(1):Article22. doi: 10.2202/1544-6115.1287. Epub 2008 Jul 21.
Normalization of expression levels applied to microarray data can help in reducing measurement error. Different methods, including cyclic loess, quantile normalization and median or mean normalization, have been utilized to normalize microarray data. Although there is considerable literature regarding normalization techniques for mRNA microarray data, there are no publications comparing normalization techniques for microRNA (miRNA) microarray data, which are subject to similar sources of measurement error. In this paper, we compare the performance of cyclic loess, quantile normalization, median normalization and no normalization for a single-color microRNA microarray dataset. We show that the quantile normalization method works best in reducing differences in miRNA expression values for replicate tissue samples. By showing that the total mean squared error are lowest across almost all 36 investigated tissue samples, we are assured that the bias correction provided by quantile normalization is not outweighed by additional error variance that can arise from a more complex normalization method. Furthermore, we show that quantile normalization does not achieve these results by compression of scale.
应用于微阵列数据的表达水平归一化有助于减少测量误差。不同的方法，包括循环局部加权回归（cyclic loess）、分位数归一化和中位数或均值归一化，已被用于对微阵列数据进行归一化。尽管有大量关于mRNA微阵列数据归一化技术的文献，但尚无比较微RNA（miRNA）微阵列数据归一化技术的出版物，而miRNA微阵列数据也存在类似的测量误差来源。在本文中，我们比较了循环局部加权回归、分位数归一化、中位数归一化和不进行归一化对单色miRNA微阵列数据集的性能。我们表明，分位数归一化方法在减少重复组织样本的miRNA表达值差异方面效果最佳。通过表明在几乎所有36个研究的组织样本中总均方误差最低，我们确信分位数归一化提供的偏差校正不会被更复杂的归一化方法可能产生的额外误差方差所抵消。此外，我们表明分位数归一化并非通过压缩尺度来实现这些结果。
