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

Michael T Ganger,Patrick Headley,Sarah J Ewing,Geoffrey D Dietz

doi:10.1186/s12859-020-03696-y

Michael T Ganger, Patrick Headley + Show 2 more

Open Access

https://doi.org/10.1186/s12859-020-03696-y

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Journal: BMC bioinformatics	Publication Date: Sep 29, 2020
Citations: 6	License type: open-access

Affiliation: Gannon University

Abstract

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.

Highlights

Quantitative polymerase chain reaction is the technique of choice for quantifying gene expression
Quantification of gene expression could be performed in a variety of different ways via different methodologies [6], but the most common is to use differences in mRNA concentrations to quantify what is called relative expression that utilizes the polymerase chain reaction (PCR) to make detection of differences in initial RNA concentration possible [7]
We begin with consideration of the case where the dependent variable (y) is ΔCðqwÞ, while the independent is a non-gene expression variable (x)

Summary

Introduction

Quantitative polymerase chain reaction (qPCR) is the technique of choice for quantifying gene expression. A central goal would be to elucidate a set of genes expressed and determine exactly how expression changes in response to external and internal signals and link this response to phenotypic changes. For this goal, quantification of gene expression could be performed in a variety of different ways via different methodologies [6], but the most common is to use differences in mRNA concentrations to quantify what is called relative expression that utilizes the polymerase chain reaction (PCR) to make detection of differences in initial RNA concentration possible [7]. Quantitative PCR (qPCR) has become the gold standard for such quantification and has become the technique of choice for diverse research questions [8,9,10]

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Conclusion