Pcr: an R package for quality assessment, analysis and testing of qPCR data.

Mahmoud Ahmed,Deok Ryong Kim

doi:10.7717/peerj.4473

Abstract

BackgroundReal-time quantitative PCR (qPCR) is a broadly used technique in the biomedical research. Currently, few different analysis models are used to determine the quality of data and to quantify the mRNA level across the experimental conditions.MethodsWe developed an R package to implement methods for quality assessment, analysis and testing qPCR data for statistical significance. Double Delta CT and standard curve models were implemented to quantify the relative expression of target genes from CT in standard qPCR control-group experiments. In addition, calculation of amplification efficiency and curves from serial dilution qPCR experiments are used to assess the quality of the data. Finally, two-group testing and linear models were used to test for significance of the difference in expression control groups and conditions of interest.ResultsUsing two datasets from qPCR experiments, we applied different quality assessment, analysis and statistical testing in the pcr package and compared the results to the original published articles. The final relative expression values from the different models, as well as the intermediary outputs, were checked against the expected results in the original papers and were found to be accurate and reliable.ConclusionThe pcr package provides an intuitive and unified interface for its main functions to allow biologist to perform all necessary steps of qPCR analysis and produce graphs in a uniform way.

Highlights

Real-time quantitative PCR is a commonly used technique to analyze the relative gene expression level in the biomedical research
We introduce an open source R package for performing quality assessment, modeling and testing for statistical significance of quantitative PCR (qPCR) data in a uniform way
The pcr package implement two methods for relative quantification of mRNA expression proposed originally by Livak & Schmittgen (2001), in addition to the necessary steps to check the assumption of these methods

Summary

INTRODUCTION

Real-time quantitative PCR (qPCR) is a commonly used technique to analyze the relative gene expression level in the biomedical research. All processes for assessing the quality of the data, performing the analysis and reporting the results are not done in the most uniform way across the literature (Bustin & Nolan, 2004). How to cite this article Ahmed and Kim (2018), pcr: an R package for quality assessment, analysis and testing of qPCR data. We introduce an open source R package for performing quality assessment, modeling and testing for statistical significance of qPCR data in a uniform way. The pcr package implement two methods for relative quantification of mRNA expression proposed originally by Livak & Schmittgen (2001), in addition to the necessary steps to check the assumption of these methods. We start by describing the process for generating the data in the original papers, briefly introduce the methods and apply them to the original data using the pcr

MATERIALS & METHODS

Statistical methods

RESULTS & DISCUSSION

Limitations & future directions

CONCLUSION