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CircadiPy: An open-source toolkit for analyzing chronobiology time series
João Pedro Carvalho-Moreira1, Leonardo de Oliveira Guarnieri1, Matheus Costa Passos2
1Departamento de Fisiologia e Biofísica, Instituto de Ciências Biológicas - Universidade Federal de Minas Gerais, Belo Horizonte, Brasil; Centro de Tecnologia e Pesquisa em Magneto Ressonância, Programa de Pós-Graduação em Engenharia Elétrica - Universidade Federal de Minas Gerais, Belo Horizonte, Brasil.
Background:
Chronobiology is the scientific field focused on studying periodicity in biological processes. In mammals, most physiological variables exhibit circadian rhythmicity, such as metabolism, body temperature, locomotor activity, and sleep. The biological rhythmicity can be statistically evaluated by examining the time series and extracting parameters that correlate to the period of oscillation, its amplitude, phase displacement, and overall variability.
New Method:
We have developed a library called CircadiPy, which encapsulates methods for chronobiological analysis and data inspection, serving as an open-access toolkit for the analysis and interpretation of chronobiological data. The package was designed to be flexible, comprehensive and scalable in order to assist research dealing with processes affected or influenced by rhythmicity.
Results:
The results demonstrate the toolkit's capability to guide users in analyzing chronobiological data collected from various recording sources, while also providing precise parameters related to the circadian rhythmicity.
Comparison With Existing Methods:
The analysis methodology from this proposed library offers an opportunity to inspect and obtain chronobiological parameters in a straightforward and cost-free manner, in contrast to commercial tools.
Conclusions:
Moreover, being an open-source tool, it empowers the community with the opportunity to contribute with new functions, analysis methods, and graphical visualizations given the simplified computational method of time series data analysis using an easy and comprehensive pipeline within a single Python object.
