Quantitative polymerase chain reaction (qPCR) is the technique of choice for quantifying gene expression. While the technique itself is well established, approaches for the analysis of qPCR data continue to improve. Here we expand on the common base method to develop procedures for testing linear relationships between gene expression and either a measured dependent variable, independent variable, or expression of another gene. We further develop functions relating variables to a relative expression value and develop calculations for determination of associated confidence intervals. Traditional qPCR analysis methods typically rely on paired designs. The common base method does not require such pairing of samples. It is therefore applicable to other designs within the general linear model such as linear regression and analysis of covariance. The methodology presented here is also simple enough to be performed using basic spreadsheet software.

中文翻译：

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通用基础方法在qPCR实验中协方差（ANCOVA）回归和分析以及后续相对表达计算中的应用

定量聚合酶链反应（qPCR）是定量基因表达的一种选择技术。尽管该技术本身已经很成熟，但分析qPCR数据的方法仍在不断改进。在这里，我们扩展通用基础方法，以开发程序来测试基因表达与测得的因变量，自变量或另一个基因的表达之间的线性关系。我们进一步开发将变量与相对表达值相关联的函数，并开发计算以确定相关的置信区间。传统的qPCR分析方法通常依赖于配对设计。通用基本方法不需要这种样本配对。因此，它适用于一般线性模型中的其他设计，例如线性回归和协方差分析。