Analyzing qPCR data: Better practices to facilitate rigor and reproducibility

Quantitative PCR (qPCR) remains a widely used, cost-effective method for RNA quantitation, yet many published studies inadequately comply with MIQE (Minimum Information for Publication of Quantitative Real-Time PCR Experiments) and FAIR (Findable, Accessible, Interoperable, Reproducible) data principles. Widespread reliance on the 2^-ΔΔCT method often overlooks critical factors such as amplification efficiency variability and reference gene stability. Furthermore, the absence of raw data and analysis code limits the community's ability to evaluate potential biases and reproduce findings. Here, we primarily aim to encourage researchers to share raw qPCR fluorescence data along with detailed analysis scripts that start from raw input and produce final figures and statistical tests. Using our recently published dataset, we model the complete qPCR analytical workflow-from raw fluorescence curves through to differential expression-highlighting key decision points that can influence results. We provide fully documented R scripts illustrating how ANCOVA (Analysis of Covariance), a flexible multivariable linear modeling approach, generally offers greater statistical power and robustness compared to 2^-ΔΔCT. Additionally, simulations support ANCOVA's applicability across diverse experimental conditions. We also demonstrate how general-purpose data repositories (e.g., figshare) and code repositories (e.g., GitHub) facilitate adherence to FAIR principles and promote transparency in qPCR research. Finally, we offer graphical examples that transparently depict both target and reference gene behavior within the same figure, enhancing interpretability. This work establishes practical resources and conceptual foundations to improve rigor, reproducibility, and openness in qPCR data analysis.Image 1.