Deo Ameya, Carlsson Jessica, Lindlöf Angelica
Systems Biology Research Centre, University of Skövde, Box 408 Skövde, 541 28, Sweden.
J Bioinform Comput Biol. 2011 Dec;9(6):795-812. doi: 10.1142/s0219720011005793.
Low-density arrays for quantitative real-time PCR (qPCR) are increasingly being used as an experimental technique for miRNA expression profiling. As with gene expression profiling using microarrays, data from such experiments needs effective analysis methods to produce reliable and high-quality results. In the pre-processing of the data, one crucial analysis step is normalization, which aims to reduce measurement errors and technical variability among arrays that might have arisen during the execution of the experiments. However, there are currently a number of different approaches to choose among and an unsuitable applied method may induce misleading effects, which could affect the subsequent analysis steps and thereby any conclusions drawn from the results. The choice of normalization method is hence an important issue to consider. In this study we present the comparison of a number of data-driven normalization methods for TaqMan low-density arrays for qPCR and different descriptive statistical techniques that can facilitate the choice of normalization method. The performance of the normalization methods was assessed and compared against each other as well as against standard normalization using endogenous controls. The results clearly show that the data-driven methods reduce variation and represent robust alternatives to using endogenous controls.
用于定量实时聚合酶链反应（qPCR）的低密度阵列越来越多地被用作微小RNA（miRNA）表达谱分析的实验技术。与使用微阵列进行基因表达谱分析一样，此类实验的数据需要有效的分析方法才能产生可靠且高质量的结果。在数据预处理中，一个关键的分析步骤是归一化，其目的是减少实验执行过程中可能出现的阵列间测量误差和技术变异性。然而，目前有多种不同的方法可供选择，不适当的应用方法可能会产生误导性影响，这可能会影响后续的分析步骤，从而影响从结果中得出的任何结论。因此，归一化方法的选择是一个需要考虑的重要问题。在本研究中，我们展示了对qPCR的TaqMan低密度阵列的多种数据驱动归一化方法以及不同描述性统计技术的比较，这些技术有助于归一化方法的选择。对归一化方法的性能进行了评估，并相互比较，同时与使用内参的标准归一化进行比较。结果清楚地表明，数据驱动方法减少了变异性，是使用内参的可靠替代方法。
