pam: An R Package for Fast and Efficient Processing of Pulse-Amplitude Modulation Data.

Area of Science:

• Plant physiology
• Photosynthesis research
• Computational biology

Background:

• Pulse-amplitude modulation (PAM) fluorometry is crucial for characterizing photosynthesis and determining photosynthetic parameters.
• Manual regression analysis of PAM data is time-consuming, prone to errors, and hinders processing of large datasets.

Purpose of the Study:

• To develop an automated R package for streamlined analysis of PAM light curve data.
• To provide researchers with a fast, reproducible, and efficient tool for deriving photosynthetic kinetic parameters.

Main Methods:

• Development of an R package named 'pam' that automates regression analysis for PAM data.
• Implementation of established models including Vollenweider (1965), Platt et al. (1980), Eilers and Peeters (1988), and Walsby (1997).
• Demonstration of a complete workflow: data reading, model fitting, validation, and export using a 20-file dataset.

Main Results:

• The 'pam' R package successfully processed all test data, yielding reliable photosynthetic parameters.
• Automated analysis was significantly faster (under 20 seconds) than traditional methods like Excel Solver.
• The package ensures reproducible results, crucial for large-scale photosynthetic studies.

Conclusions:

• The 'pam' R package offers a substantial improvement for analyzing PAM fluorometry data, enhancing efficiency and reproducibility.
• This tool is freely available, promoting wider adoption in photosynthetic research and computational biology.
• Automating kinetic parameter derivation accelerates scientific discovery in plant physiology.