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CosinorPy: a python package for cosinor-based rhythmometry.

Miha Moškon1

  • 1Faculty of Computer and Information Science, University of Ljubljana, Večna pot 113, 1000, Ljubljana, Slovenia. miha.moskon@fri.uni-lj.si.

BMC Bioinformatics
|October 30, 2020
PubMed
Summary

CosinorPy is a new Python package for analyzing biological data rhythms using cosinor methods. It offers advanced features and unifies data formats, simplifying rhythmicity detection and analysis for researchers.

Keywords:
Circadian analysisCosinorPythonRegressionRhythmicity analysis

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Area of Science:

  • Chronobiology
  • Bioinformatics
  • Computational Biology

Background:

  • Classical cosinor regression remains valuable for biological rhythm analysis despite new methods.
  • Existing software for cosinor analysis lacks unified data formats and comprehensive functionalities.

Purpose of the Study:

  • To introduce CosinorPy, a Python package consolidating and enhancing cosinor-based rhythmometry tools.
  • To provide a user-friendly, versatile solution for analyzing rhythmic biological data.

Main Methods:

  • Implementation of single- and multi-component cosinor models.
  • Automatic model selection and population-mean cosinor regression.
  • Differential rhythmicity assessment and experimental design tools.

Main Results:

  • CosinorPy integrates and extends functionalities of existing cosinor packages.
  • The package supports diverse analyses, including synthetic data generation and flexible data import/export.
  • It produces publication-ready figures with minimal statistical expertise required.

Conclusions:

  • CosinorPy offers an accessible, powerful tool for rhythmicity detection and analysis in biological data.
  • The package is easily installable via pip and available with comprehensive documentation and examples.
  • CosinorPy empowers researchers with advanced cosinor analysis capabilities for publication.