ArticleBMC bioinformatics2020
CosinorPy: a python package for cosinor-based rhythmometry.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers.
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Who cites it
51 citing papers in PubMed, 99 citations in OpenAlex.
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- Distinct diurnal temperature rhythm patterns in critical illness myopathy: secondary analysis of two prospective trials.Annals of intensive care · 2025Article
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- The redox rhythm gates immune-induced cell death distinctly from the genetic clock.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Diurnal rhythm of the human plasma proteome.Clinical proteomics · 2025Article
- Combining Time-Restricted Wheel Running and Feeding During the Light Phase Increases Running Intensity Under High-Fat Diet Conditions Without Altering the Total Amount of Daily Running.International journal of molecular sciences · 2025Article
- Circadian Biomarkers in Humans: Methodological Insights into the Detection of Melatonin and Cortisol.Biomolecules · 2025Review
- Biorhythms derived from consumer wearables predict postoperative complications in children.Science advances · 2025Article
- Circadian-Tuned Peptide Drug/Gene Co-Delivery Nanocomplexes to Enhance Glioblastoma Targeting and Transfection.International journal of molecular sciences · 2025Article
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Corrections and comments
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Authors and funding
1 author at 1 institution in 1 country.
Funding
Abstract
backgroundEven though several computational methods for rhythmicity detection and analysis of biological data have been proposed in recent years, classical trigonometric regression based on cosinor still has several advantages over these methods and is still widely used. Different software packages for cosinor-based rhythmometry exist, but lack certain functionalities and require data in different, non-unified input formats.
resultsWe present CosinorPy, a Python implementation of cosinor-based methods for rhythmicity detection and analysis. CosinorPy merges and extends the functionalities of existing cosinor packages. It supports the analysis of rhythmic data using single- or multi-component cosinor models, automatic selection of the best model, population-mean cosinor regression, and differential rhythmicity assessment. Moreover, it implements functions that can be used in a design of experiments, a synthetic data generator, and import and export of data in different formats.
conclusionCosinorPy is an easy-to-use Python package for straightforward detection and analysis of rhythmicity requiring minimal statistical knowledge, and produces publication-ready figures. Its code, examples, and documentation are available to download from https://github.com/mmoskon/CosinorPy . CosinorPy can be installed manually or by using pip, the package manager for Python packages. The implementation reported in this paper corresponds to the software release v1.1.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.