Application of the common base method to regression and analysis of covariance (ANCOVA) in qPCR experiments and subsequent relative expression calculation

• Main Authors: Michael T. Ganger, Geoffrey D. Dietz, Patrick Headley, Sarah J. Ewing
• Format: Article
• Language: English
• Published: BMC 2020-09-01
• Series: BMC Bioinformatics
• Subjects: Confidence intervals; Linear relationship; Lognormal; qPCR analysis; Statistics
• Online Access: http://link.springer.com/article/10.1186/s12859-020-03696-y

Abstract Background 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. Results 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. Conclusions 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.