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.
Highlights
Even 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
The whole analysis is available as interactive Python notebooks (IPYNB) at https://github.com/mmoskon/CosinorPy
This scenario complies with a transcriptomics data analysis or an analysis of Quantitative polymerase chain reaction (qPCR) data
Summary
We opted to perform a differential rhythmicity analysis using a 1-component model to obtain more informative results, namely the significance of amplitude change and acrophase shift. We again use CosinorPy to identify the most suitable model, and assess the rhythmicity parameters and significance of periodicity in the data (see https://github.com/ mmoskon/CosinorPy/blob/master/demo_dependent.ipynb). The reported acrophases and their corresponding p-values fully comply with the results obtained with the CosinorPy package (see Additional file 9: Table 9 and Additional file 10: Table 10). Case study 3: additional benefits of the multi‐component cosinor To investigate the benefits of multi-component cosinor models we applied CosinorPy to a larger dataset downloaded from the JTK Cycle repository [7] We analysed these data with both, single-component cosinor, as well as multi-component cosinor models with up to three components (see https://github.com/mmoskon/CosinorPy/blob/master/multi_vs_single.ipynb).
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
