Evaluation of qPCR curve analysis methods for reliable biomarker discovery: Bias, resolution, precision, and implications
Evaluation of qPCR curve analysis methods for reliable biomarker discovery: Bias, resolution, precision, and implications
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DOI：
10.1016/j.ymeth.2012.08.011
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发表时间：
2013-01-01
期刊：
影响因子：
4.8
通讯作者：
Vandesompele, Jo
中科院分区：
文献类型：
--
作者：
Ruijter, Jan M.;Pfaffl, Michael W.;Vandesompele, Jo
RNA转录物如mRNA或microRNA经常用作生物标志物以确定疾病状态或对治疗的反应。逆转录（RT）结合定量PCR（qPCR）已成为定量少量此类RNA分子的首选方法。随着RT-qPCR的民主化及其在生物医学研究或生物标志物发现中的日益广泛的使用，我们见证了基因表达数据分析方法数量的增长。这些方法中的大多数基于以下原理：扩增曲线相对于循环轴的位置是初始靶量的量度：曲线越晚，靶量越低。然而，大多数方法在用于确定该位置的数学算法以及在确定PCR反应的效率（每个循环的产物的倍数增加）并将其应用于计算中的方式上不同。此外，关于PCR效率是恒定的还是持续下降的存在争议。这共同导致了不同方法的发展来分析扩增曲线。在已发表的这些方法的比较中，可用的算法通常以受限或过时的方式应用，这对它们来说并不公平。因此，我们的目标是开发一种用于曲线分析性能的稳健和无偏评估的框架，由此使用先前公布的大型临床数据集彻底比较各种公开可用的曲线分析方法（Vermeulen等人，2009年）[11]。这些方法的原始开发者应用了他们的算法，并且是这项研究的共同作者。我们评估了曲线分析方法在表达水平、统计学显著性和患者分类准确性方面对转录生物标志物鉴定的影响。分析每个基因的浓度系列以及来自未发表的技术性能实验的数据集，以评估算法的精密度、偏倚和分辨率。虽然在考虑技术性能实验时，方法之间存在很大差异，但大多数方法在生物标志物数据上表现相对较好。提供每种方法的数据和分析结果，作为进一步开发和评价qPCR曲线分析方法的基准（http：qPCRDataMethods.hfrc.nl）。(C)2012 Elsevier Inc. All rights reserved.
RNA transcripts such as mRNA or microRNA are frequently used as biomarkers to determine disease state or response to therapy. Reverse transcription (RT) in combination with quantitative PCR (qPCR) has become the method of choice to quantify small amounts of such RNA molecules. In parallel with the democratization of RT-qPCR and its increasing use in biomedical research or biomarker discovery, we witnessed a growth in the number of gene expression data analysis methods. Most of these methods are based on the principle that the position of the amplification curve with respect to the cycle-axis is a measure for the initial target quantity: the later the curve, the lower the target quantity. However, most methods differ in the mathematical algorithms used to determine this position, as well as in the way the efficiency of the PCR reaction (the fold increase of product per cycle) is determined and applied in the calculations. Moreover, there is dispute about whether the PCR efficiency is constant or continuously decreasing. Together this has lead to the development of different methods to analyze amplification curves. In published comparisons of these methods, available algorithms were typically applied in a restricted or outdated way, which does not do them justice. Therefore, we aimed at development of a framework for robust and unbiased assessment of curve analysis performance whereby various publicly available curve analysis methods were thoroughly compared using a previously published large clinical data set (Vermeulen et al., 2009) [11]. The original developers of these methods applied their algorithms and are co-author on this study. We assessed the curve analysis methods' impact on transcriptional biomarker identification in terms of expression level, statistical significance, and patient-classification accuracy. The concentration series per gene, together with data sets from unpublished technical performance experiments, were analyzed in order to assess the algorithms' precision, bias, and resolution. While large differences exist between methods when considering the technical performance experiments, most methods perform relatively well on the biomarker data. The data and the analysis results per method are made available to serve as benchmark for further development and evaluation of qPCR curve analysis methods (http://qPCRDataMethods.hfrc.nl). (C) 2012 Elsevier Inc. All rights reserved.