Analysis of complex circadian time series data using wavelets

TL;DR

This paper presents pyBOAT, a Python toolkit for analyzing complex circadian signals with time-dependent properties, overcoming limitations of traditional methods by capturing dynamic oscillation changes in high-resolution data.

Contribution

The paper introduces pyBOAT, a novel open-source Python toolkit that enables detailed analysis of time-dependent circadian oscillations using wavelet-based methods.

Findings

pyBOAT effectively detects rhythms in complex signals.

It computes high-resolution spectral properties over time.

The toolkit is user-friendly with GUI and Python integration.

Abstract

Experiments that compare rhythmic properties across different genetic alterations and entrainment conditions underlie some of the most important breakthroughs in circadian biology. A robust estimation of the rhythmic properties of the circadian signals goes hand in hand with these discoveries. Widely applied traditional signal analysis methods such as fitting cosine functions or Fourier transformations rely on the assumption that oscillation periods do not change over time. However, novel high-resolution recording techniques have shown that, most commonly, circadian signals exhibit time-dependent changes of periods and amplitudes which cannot be captured with the traditional approaches. In this chapter we introduce a method to determine time-dependent properties of oscillatory signals, using the novel open-source Python-based Biological Oscillations Analysis Toolkit (pyBOAT). We show with examples how to detect rhythms, compute and interpret high-resolution time-dependent spectral results, analyze the main oscillatory component and to subsequently determine these main components time-dependent instantaneous period, amplitude and phase. We introduce step-by-step how such an analysis can be done by means of the easy-to-use point-and-click graphical user interface (GUI) provided by pyBOAT or executed within a Python programming environment. Concepts are explained using simulated signals as well as experimentally obtained time series.