Pradervand Sylvain, Weber Johann, Thomas Jérôme, Bueno Manuel, Wirapati Pratyaksha, Lefort Karine, Dotto G Paolo, Harshman Keith
Lausanne DNA Array Facility, Center for Integrative Genomics, University of Lausanne, CH-1015 Lausanne, Switzerland.
RNA. 2009 Mar;15(3):493-501. doi: 10.1261/rna.1295509. Epub 2009 Jan 28.
Profiling miRNA levels in cells with miRNA microarrays is becoming a widely used technique. Although normalization methods for mRNA gene expression arrays are well established, miRNA array normalization has so far not been investigated in detail. In this study we investigate the impact of normalization on data generated with the Agilent miRNA array platform. We have developed a method to select nonchanging miRNAs (invariants) and use them to compute linear regression normalization coefficients or variance stabilizing normalization (VSN) parameters. We compared the invariants normalization to normalization by scaling, quantile, and VSN with default parameters as well as to no normalization using samples with strong differential expression of miRNAs (heart-brain comparison) and samples where only a few miRNAs are affected (by p53 overexpression in squamous carcinoma cells versus control). All normalization methods performed better than no normalization. Normalization procedures based on the set of invariants and quantile were the most robust over all experimental conditions tested. Our method of invariant selection and normalization is not limited to Agilent miRNA arrays and can be applied to other data sets including those from one color miRNA microarray platforms, focused gene expression arrays, and gene expression analysis using quantitative PCR.
利用 miRNA 微阵列分析细胞中的 miRNA 水平正成为一种广泛应用的技术。尽管 mRNA 基因表达阵列的标准化方法已经成熟，但 miRNA 阵列标准化目前尚未得到详细研究。在本研究中，我们调查了标准化对使用安捷伦 miRNA 阵列平台生成的数据的影响。我们开发了一种方法来选择不变的 miRNA（不变量），并使用它们来计算线性回归标准化系数或方差稳定标准化（VSN）参数。我们将基于不变量的标准化与使用默认参数的缩放标准化、分位数标准化和 VSN 进行了比较，同时也与不进行标准化进行了比较，使用了具有强烈 miRNA 差异表达的样本（心脏 - 大脑比较）以及仅有少数 miRNA 受到影响的样本（鳞状癌细胞中 p53 过表达与对照相比）。所有标准化方法都比不进行标准化表现更好。基于不变量集和分位数的标准化程序在所有测试的实验条件下最为稳健。我们的不变量选择和标准化方法不仅限于安捷伦 miRNA 阵列，还可应用于其他数据集，包括来自单色 miRNA 微阵列平台、聚焦基因表达阵列以及使用定量 PCR 进行的基因表达分析的数据集。
