Analyzing qPCR data: Better practices to facilitate rigor and reproducibility.

Area of Science:

• Molecular Biology
• Bioinformatics
• Biostatistics

Background:

• Quantitative PCR (qPCR) is a standard RNA quantitation method.
• Many qPCR studies lack MIQE and FAIR data compliance.
• The 2^-ΔΔCT method has limitations regarding amplification efficiency and reference gene stability.

Purpose of the Study:

• Promote sharing of raw qPCR data and analysis scripts.
• Model the complete qPCR analytical workflow.
• Improve rigor, reproducibility, and transparency in qPCR research.

Main Methods:

• Utilized a recently published qPCR dataset.
• Modeled the workflow from raw fluorescence to differential expression.
• Provided documented R scripts for analysis.
• Employed Analysis of Covariance (ANCOVA) for statistical modeling.
• Used simulations to validate ANCOVA's applicability.

Main Results:

• ANCOVA demonstrates greater statistical power and robustness than the 2^-ΔΔCT method.
• General-purpose repositories (e.g., figshare, GitHub) facilitate FAIR data principles.
• Transparent graphical examples enhance interpretability of gene behavior.

Conclusions:

• Sharing raw data and analysis scripts is crucial for qPCR reproducibility.
• ANCOVA is a powerful and flexible alternative for qPCR data analysis.
• Adherence to FAIR principles and transparent reporting enhance scientific rigor.