A Quantitative Polymerase Chain Reaction Analysis Solution in R
Adam E. Ahmed, Allison B. Reiss, Lora J. Kasselman. Biomedical Research, NYU Winthrop Hospital, Mineola, New York, United States
Purpose of Study Quantitative polymerase chain reaction (qPCR) is a widely used technique in molecular biology. One popular application of qPCR is to analyze multiple DNA sequences and compare treatment groups against each other to quantify gene expression through fold-differences. This analysis, however, requires handling cumbersome and meticulous data. Automatic qPCR analysis can be done with currently available scripts and other programs. However, these require expertise in computational biology, statistics, and/or programming. The objective of this project is to provide novice and advanced researchers as well as health care professionals with an accurate and efficient solution to analyzing qPCR Ct datasets, whether small or large.
Methods Used Openly distributed data analysis solutions are highly valuable for researchers and we present an R script, PCRAnalysis, that provides comprehensive and elementary statistical results for researchers of all backgrounds and expertise. PCRAnalysis receives inputs of the raw cycle threshold (Ct) values of sequences and can compare groups of genes or treatments to calculate and display fold-differences; intermediate as well as all final calculations are displayed throughout the computation.
Summary of Results Results are presented in automatically formatted tables and graphs; These results include means, 2ΔΔCt (fold-change) values, confidence intervals, standard deviations, standard errors of the means, t-tests and ANOVA tests. The script was tested by tasking a novice computer science biomedical researcher with converting raw Ct values from a real experimental dataset, to finalized fold changes and graphical results using PCRAnalysis. They had low proficiency in R and were successful in producing the desired results very quickly, which were then confirmed by comparing manually calculated results for the same experimental dataset.
Conclusions PCRAnalysis can be easily used by people of all levels of computer proficiency to automatically analyze Ct values in qPCR experiments. Readily comprehensible PCR results can positively impact the clinical experience by allowing application of key gene expression information into the care plan formulated by clinicians to prevent and treat disease states such as cancer and cardiovascular diseases. PCRAnalysis as well as instructions are archived at https://doi.org/10.5281/zenodo.1487643.
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