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

BMC Bioinformatics

Michael T. Ganger¹  Sarah J. Ewing¹  Geoffrey D. Dietz²  Patrick Headley² 

[1] Department of Biology, Gannon University, 16541-0001, Erie, PA, USA;Department of Mathematics, Gannon University, 16541-0001, Erie, PA, USA;

关键词: Confidence intervals;    Linear relationship;    Lognormal;    qPCR analysis;    Statistics;   

DOI  :  10.1186/s12859-020-03696-y

来源: Springer

【 摘 要 】

BackgroundQuantitative 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.ResultsHere 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.ConclusionsTraditional 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.

【 授权许可】

CC BY