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

Abstract

Background: Real-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.

Methods: We developed an R package to implement methods for quality assessment, analysis and testing qPCR data for statistical significance. Double Delta C_T and standard curve models were implemented to quantify the relative expression of target genes from C_T 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.

Results: Using 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.

Conclusion: The 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.

Keywords: Data analysis; Quality assessment; R package; qPCR.

Figure 1. Amplification efficiency and standard curves of c-myc and GAPDH.

Seven different dilutions of RNA were used as an input to synthesize cDNA, then to a real-time quantitative PCR reaction with c-myc and GAPDH primers. (A) ΔC_T values were calculated by subtracting the C_T values from three independent samples of the control gene(GAPDH) from the target c-myc. Averages and standard deviations are shown as points and error bars. The blue line represents the linear trend between the ΔC_T and log10 of the input amount. (B) C_T values from three independent samples of c-myc and GAPDH are shown with the corresponding log10 input amounts.

Figure 2. Relative expression of c-myc in human brain and kidney tissues.

Total RNA from human brain and kidney tissues were used to synthesize cDNA and real-time quantitative PCR reaction with c-myc and GAPDH primers. C_T values from six independent replicates were used to calculate the expression of c-myc in the kidney normalized by GAPDH and relative to the brain using The ΔΔC_T (A) and the standard curve methods (B). Averages and standard deviations are shown as bars and error bars.

Figure 3. A conceptual workflow of the analysis of RT-qPCR data.

A graphic description of the input, intermediary values and final output of different steps of the analysis pipeline and their relations. Colors represent distinct steps and the numbers are the order in which they are applied in a typical analysis.