CosinorPy presents a python package for cosinor based rhythmometry. It is composed of four modules:
-
file_parser: reading and writting xlsx or csv files and generating synthetic data -
cosinor: single- or multi-component cosinor functions -
cosinor1: single-component cosinor specific functions -
cosinor_nonlin: generalized cosinor model and its analysis using nonlinear regression (added in release v2)
To use these modules include the following code in your python file:
from CosinorPy import file_parser, cosinor, cosinor1, cosinor_nonlin
CosinorPy can be used in a combination with different types of experimental data (e.g., qPCR data - independent measurement, bioluminescence data - dependent measurements, or even count data for which Poisson regression is used). Input data need to be formatted in accordance with the implementation of the file_parser module (see file_parser). This module implements several pre-processing functions that can be applied to the data, such as removal of outliers, removal of the linear component in the data, removal of the data outside a given time interval, etc. Moreover, the user might as well preprocess the data with alternative methods, e.g., with the application of a lowpass filter. When collecting the data, the user should follow the guidelines for circadian analyses as described in [1]. Moreover, before collecting the samples, the user can approximate the minimal required sample size to obtain the required accuracy [2] (see cosinor1.amplitude_detection, cosinor1.amplitude_confidence and cosinor1.acrophase_confidence). After the data has been imported, different types of analyses can be applied. These are described in more details in the examples below and in the papers [3,4].
CosinorPy can be installed using pip with the command:
pip install CosinorPy
To install the software version described in [3], the following command should be issued:
pip install CosinorPy==1.1
To install the software version described in [4], the following command should be issued:
pip install CosinorPy==2.1
Examples are given as interactive python notebook (ipynb) files:
demo_independent.ipynb: cosinor analysis of independent datademo_dependent.ipynb: cosinor analysis of population (dependent) datademo_independent_extended.ipynb: cosinor analysis of independent data with extended functionalities of multi-component cosinordemo_dependent_extended.ipynb: cosinor analysis of population (dependent) data with extended functionalities of multi-component cosinordemo_independent_nonlin.ipynb: cosinor analysis of independent data with extended a generalised cosinor modeldemo_dependent_nonlin.ipynb: cosinor analysis of population (dependent) data with extended a generalised cosinor modeldemo_csv.ipynb: reading from a csv filedemo_xlsx.ipynb: reading from an xlsx filemulti_vs_single.ipynb: multi-component versus single-component cosinor model
The repository as well includes the following R scripts: cosinor2_independent.R, cosinor2_independent_compare.R, cosinor2_dependent.R and cosinor2_dependent_compare.R. These can be used to reproduce some of the results obtained with CosinorPy using cosinor and cosinor2 R packages.
If you are using CosinorPy for your scientific work, please cite:
Moškon, M. "CosinorPy: A Python Package for cosinor-based Rhythmometry." BMC Bioinformatics 21.485 (2020).
The paper is available at https://www.doi.org/10.1186/s12859-020-03830-w.
Please direct your questions and comments to miha.moskon@fri.uni-lj.si
[1] Hughes, Michael E., et al. "Guidelines for genome-scale analysis of biological rhythms." Journal of biological rhythms 32.5 (2017): 380-393.
[2] Bingham, Christopher, et al. "Inferential statistical methods for estimating and comparing cosinor parameters." Chronobiologia 9.4 (1982): 397-439.
[3] Moškon, M. "CosinorPy: A Python Package for cosinor-based Rhythmometry." BMC Bioinformatics 21.485 (2020).
[4] Moškon, M. "Identification and characterisation of variable oscillatory behaviour using CosinorPy." https://doi.org/10.1101/2022.07.04.498691