Department of Biotechnology, College of Sciences, University of Tehran, P.O. Box 1417614411, Tehran, Iran.
Biotechnol Lett. 2013 Jun;35(6):843-51. doi: 10.1007/s10529-013-1150-5. Epub 2013 Mar 2.
Low-density quantitative real-time PCR (qPCR) arrays are often used to profile expression patterns of microRNAs in various biological milieus. To achieve accurate analysis of expression of miRNAs, non-biological sources of variation in data should be removed through precise normalization of data. We have systematically compared the performance of 19 normalization methods on different subsets of a real miRNA qPCR dataset that covers 40 human tissues. After robustly modeling the mean squared error (MSE) in normalized data, we demonstrate lower variability between replicates is achieved using various methods not applied to high-throughput miRNA qPCR data yet. Normalization methods that use splines or wavelets smoothing to estimate and remove Cq dependent non-linearity between pairs of samples best reduced the MSE of differences in Cq values of replicate samples. These methods also retained between-group variability in different subsets of the dataset.
低密度定量实时 PCR (qPCR) 阵列常用于分析各种生物环境中小 RNA 的表达模式。为了实现 miRNA 表达的准确分析，应通过数据的精确归一化去除数据中生物学以外的变异源。我们系统地比较了 19 种归一化方法在涵盖 40 个人组织的真实 miRNA qPCR 数据集的不同子集中的性能。在对归一化数据的均方误差 (MSE) 进行稳健建模后，我们证明了使用尚未应用于高通量 miRNA qPCR 数据的各种方法可以实现更稳定的重复间变异性。使用样条或小波平滑来估计和去除样本对之间 Cq 依赖性非线性的归一化方法，最能降低重复样本 Cq 值差异的 MSE。这些方法还保留了数据集不同子集中的组间变异性。
