Fundel K, Haag J, Gebhard P M, Zimmer R, Aigner T
Institute for Informatics, Ludwig-Maximilians-Universität München, Germany.
Osteoarthritis Cartilage. 2008 Aug;16(8):947-55. doi: 10.1016/j.joca.2007.12.007. Epub 2008 Feb 6.
Normalization of mRNA data, i.e., the calculation of mRNA expression values comparable in between different experiments, is a major issue in biomedical and orthopaedic/rheumatology research, both for single-gene technologies [Northern blotting, conventional and quantitative polymerase chain reaction (qPCR)] and large-scale gene expression experiments. In this study, we tested several established normalization methods for their effects on gene expression measurements.
Five standard normalization strategies were applied on a previously published data set comparing peripheral and central late stage osteoarthritic cartilage samples.
The different normalization procedures had profound effects on the distribution as well as the significance values of the gene expression levels. All applied normalization procedures, except the median absolute deviation scaling, showed a bias towards up- or down-regulation of genes as visualized in volcano plots. Of interest, the P-values were much more depending on the normalization procedure than the fold changes. Ten commonly used housekeeping genes showed a significant variability in between the different specimens investigated. The gene expression analysis by cDNA arrays was confirmed for these genes by qPCR.
This study documents how much normalization strategies influence the outcome of gene expression profiling analysis (i.e., the detection of regulated genes). Different normalization approaches can significantly change the P-values and fold changes of a large number of genes. Thus, it is of vital importance to check every individual step of gene expression data analysis for its appropriateness. The use of global robustness and quality measures for analyzing individual outcomes can help in estimating the reliability of final microarray study results.
mRNA数据的标准化，即计算不同实验间可比的mRNA表达值，是生物医学和骨科/风湿病学研究中的一个主要问题，无论是对于单基因技术[Northern印迹法、传统和定量聚合酶链反应（qPCR）]还是大规模基因表达实验。在本研究中，我们测试了几种既定的标准化方法对基因表达测量的影响。
对先前发表的比较外周和中央晚期骨关节炎软骨样本的数据集应用了五种标准的标准化策略。
不同的标准化程序对基因表达水平的分布以及显著性值有深远影响。除中位数绝对偏差缩放外，所有应用的标准化程序在火山图中均显示出对基因上调或下调的偏向。有趣的是，P值比倍数变化更依赖于标准化程序。在所研究的不同样本之间，十个常用的看家基因显示出显著的变异性。通过qPCR证实了这些基因在cDNA阵列上的基因表达分析。
本研究证明了标准化策略对基因表达谱分析结果（即调控基因的检测）的影响程度。不同的标准化方法可显著改变大量基因的P值和倍数变化。因此，检查基因表达数据分析的每一个步骤是否合适至关重要。使用全局稳健性和质量指标来分析个体结果有助于评估最终微阵列研究结果的可靠性。
