Optimal Analysis for Rhythmic Time-Dependent Biological Data - pyBOAT

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 time-dependent (non-stationary) features. However, our understanding of the underlying mechanisms regulating these oscillations has been lagging behind, partially due to the lack of tools to reliably quantify features as they change in time. With this challenge in mind, we have developed pyBOAT, a framework that integrates multiple optimal steps of non-stationary oscillatory time series analysis into an open source fully automatic stand-alone software with an easy-to-use graphical user interface. pyBOAT combines data-visualization, optimized sinc-filter detrending, amplitude envelope removal and a subsequent continuous-wavelet based time-frequency analysis. Using synthetic and real-world rhythmic data, we show that our method successfully identifies all time-dependent properties even at high levels of noise. In addition, we discuss how widely used smoothing and detrending operations can lead to unexpected artifacts and interpretation bias.