Sicegar: R package for sigmoidal and double-sigmoidal curve fitting

Area of Science:

• Computational Biology
• Bioinformatics
• Data Analysis

Background:

• Sigmoidal and double-sigmoidal dynamics are prevalent in biological systems.
• Accurate characterization of these dynamics is crucial for biological interpretation.
• Existing methods may lack automation or comprehensive parameter reporting.

Purpose of the Study:

• To introduce sicegar, an R package for automated sigmoidal and double-sigmoidal data analysis.
• To provide robust classification of biological curves into "no signal," "sigmoidal," or "double-sigmoidal" categories.
• To report biologically meaningful parameters alongside curve classification.

Main Methods:

• Development of the sicegar R package.
• Rigorous fitting of mathematical models to sigmoidal and double-sigmoidal data.
• Classification based on model fit quality, with an "ambiguous" category for poor fits.

Main Results:

• sicegar successfully classifies sigmoidal and double-sigmoidal data.
• The package demonstrates high accuracy in simulations, even with significant noise.
• It reliably recovers underlying biological dynamics and provides detailed metrics.

Conclusions:

• sicegar offers an automated, accurate, and robust solution for analyzing sigmoidal biological data.
• The package enhances biological insights by providing interpretable parameters.
• It is available on CRAN with comprehensive documentation.