Clustering nonstationary circadian rhythms using locally stationary wavelet representations

TL;DR

This paper introduces a wavelet-based clustering method for nonstationary circadian rhythm data, enabling effective analysis of complex biological signals affected by environmental factors like soil pollution.

Contribution

The authors develop a novel clustering approach using locally stationary wavelet processes and functional PCA to analyze nonstationary circadian data.

Findings

Successfully clusters plant circadian data based on time-frequency patterns

Demonstrates advantages over existing methods in handling nonstationary signals

Provides a quantitative framework for comparing circadian rhythm variations

Abstract

How does soil pollution affect a plant's circadian clock? Are there any differences between how the clock reacts when exposed to different concentrations of elements of the periodic table? If so, can we characterise these differences? We approach these questions by analysing and modelling circadian plant data, where the levels of expression of a luciferase reporter gene were measured at regular intervals over a number of days after exposure to different concentrations of lithium. A key aspect of circadian data analysis is to determine whether a time series (derived from experimental data) is `rhythmic' and, if so, to determine the underlying period. However, our dataset displays nonstationary traits such as changes in amplitude, gradual changes in period and phase-shifts. In this paper, we develop clustering methods using a wavelet transform. Wavelets are chosen as they are ideally suited to identifying discriminant local time and scale features. Furthermore, we propose treating the observed time series as realisations of locally stationary wavelet processes. This allows us to define and estimate the evolutionary wavelet spectrum. We can then compare, in a quantitative way, using a functional principal components analysis, the time-frequency patterns of the time series. Our approach uses a clustering algorithm to group the data according to their time-frequency patterns. We demonstrate the advantages of our methodology over alternative approaches and show that it successfully clusters our data.