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首页> 外文期刊>Journal of Bioinformatics and Computational Biology >How to choose a normalization strategy for miRNA quantitative real-time (QPCR) arrays
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How to choose a normalization strategy for miRNA quantitative real-time (QPCR) arrays

机译：如何为miRNA定量实时（QPCR）阵列选择归一化策略

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摘要

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.
机译：用于定量实时PCR（qPCR）的低密度阵列正越来越多地用作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，来自此类实验的数据也需要有效的分析方法来产生可靠且高质量的结果。在数据的预处理中，一个关键的分析步骤是归一化，该步骤旨在减少实验执行过程中可能出现的阵列之间的测量误差和技术差异。但是，当前有多种不同的方法可供选择，不合适的应用方法可能会引起误导效应，这可能会影响后续的分析步骤，从而影响从结果得出的任何结论。因此，归一化方法的选择是要考虑的重要问题。在本研究中，我们比较了qqPCR的TaqMan低密度阵列的多种数据驱动的标准化方法以及可以促进标准化方法选择的各种描述性统计技术的比较。评估归一化方法的性能，并相互比较，以及使用内源性对照与标准归一化进行比较。结果清楚地表明，数据驱动的方法减少了变异，并代表了使用内源性控件的可靠选择。

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