Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering , Imperial College London , London SW7 2AZ , U.K.
School of Life Sciences and Department of Statistics , University of Warwick , Coventry CV4 7AL , U.K.
Anal Chem. 2019 Jun 4;91(11):7426-7434. doi: 10.1021/acs.analchem.9b01466. Epub 2019 May 14.
Real-time PCR is a highly sensitive and powerful technology for the quantification of DNA and has become the method of choice in microbiology, bioengineering, and molecular biology. Currently, the analysis of real-time PCR data is hampered by only considering a single feature of the amplification profile to generate a standard curve. The current "gold standard" is the cycle-threshold ( C) method which is known to provide poor quantification under inconsistent reaction efficiencies. Multiple single-feature methods have been developed to overcome the limitations of the C method; however, there is an unexplored area of combining multiple features in order to benefit from their joint information. Here, we propose a novel framework that combines existing standard curve methods into a multidimensional standard curve. This is achieved by considering multiple features together such that each amplification curve is viewed as a point in a multidimensional space. Contrary to only considering a single-feature, in the multidimensional space, data points do not fall exactly on the standard curve, which enables a similarity measure between amplification curves based on distances between data points. We show that this framework expands the capabilities of standard curves in order to optimize quantification performance, provide a measure of how suitable an amplification curve is for a standard, and thus automatically detect outliers and increase the reliability of quantification. Our aim is to provide an affordable solution to enhance existing diagnostic settings through maximizing the amount of information extracted from conventional instruments.
实时 PCR 是一种高度敏感和强大的 DNA 定量技术，已成为微生物学、生物工程和分子生物学的首选方法。目前，实时 PCR 数据分析受到限制，因为它仅考虑扩增谱的单个特征来生成标准曲线。目前的“金标准”是循环阈值 (C) 方法，该方法已知在反应效率不一致的情况下提供较差的定量结果。已经开发了多种单特征方法来克服 C 方法的局限性；然而，在结合多个特征以利用它们的联合信息方面，仍有一个未被探索的领域。在这里，我们提出了一个新的框架，将现有的标准曲线方法结合到多维标准曲线中。这是通过同时考虑多个特征来实现的，使得每个扩增曲线都被视为多维空间中的一个点。与仅考虑单个特征相反，在多维空间中，数据点不会完全落在标准曲线上，这使得可以根据数据点之间的距离对扩增曲线进行相似性度量。我们表明，该框架扩展了标准曲线的功能，以优化定量性能，提供对扩增曲线适合标准的程度的衡量，从而自动检测异常值并提高定量的可靠性。我们的目标是通过从常规仪器中提取尽可能多的信息来增强现有的诊断设置，提供一种经济实惠的解决方案。
