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Optimal time frequency analysis for biological data - pyBOAT

Gregor Mönke, Frieda A. Sorgenfrei, Christoph Schmal, Adrián E. Granada
doi: https://doi.org/10.1101/2020.04.29.067744
Gregor Mönke
1European Molecular Biology Laboratory, Meyerhofstraße 1, 69117 Heidelberg, Germany
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  • For correspondence: gregor.moenke@embl.de adrian.granada@charite.de
Frieda A. Sorgenfrei
2Austrian Centre of Industrial Biotechnology c/o University of Graz, Institute of Chemistry, NAWI Graz, BioTechMed Graz, Heinrichstrasse 28, 8010 Graz, Austria
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Christoph Schmal
3Institute for Theoretical Biology, Humboldt Universität zu Berlin, Philippstraße 13, 10115 Berlin, Germany
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Adrián E. Granada
4Charité Comprehensive Cancer Center, Charité Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
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  • For correspondence: gregor.moenke@embl.de adrian.granada@charite.de
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Abstract

Methods for the quantification of rhythmic biological signals have been essential for the discovery of function and design of biological oscillators. Advances in live measurements have allowed recordings of unprecedented resolution revealing a new world of complex heterogeneous oscillations with multiple noisy non-stationary features. However, our understanding of the underlying mechanisms regulating these oscillations has been lagging behind, partially due to the lack of simple tools to reliably quantify these complex non-stationary features. With this challenge in mind, we have developed pyBOAT, a Python-based fully automatic stand-alone software that integrates multiple steps of non-stationary oscillatory time series analysis into an easy-to-use graphical user interface. pyBOAT implements continuous wavelet analysis which is specifically designed to reveal time-dependent features. In this work we illustrate the advantages of our tool by analyzing complex non-stationary time-series profiles. Our approach integrates data-visualization, optimized sinc-filter detrending, amplitude envelope removal and a subsequent continuous-wavelet based time-frequency analysis. Finally, using analytical considerations and numerical simulations we discuss unexpected pitfalls in commonly used smoothing and detrending operations.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • Added a new main figure showing applications of pyBOAT to real world datasets. Added two new supplementary figures, showing details of the applications and showcasing the synthetic signal generator.

  • https://github.com/tensionhead/pyboat

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted June 05, 2020.
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Optimal time frequency analysis for biological data - pyBOAT
Gregor Mönke, Frieda A. Sorgenfrei, Christoph Schmal, Adrián E. Granada
bioRxiv 2020.04.29.067744; doi: https://doi.org/10.1101/2020.04.29.067744
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Optimal time frequency analysis for biological data - pyBOAT
Gregor Mönke, Frieda A. Sorgenfrei, Christoph Schmal, Adrián E. Granada
bioRxiv 2020.04.29.067744; doi: https://doi.org/10.1101/2020.04.29.067744

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