easyClock: a user-friendly desktop application for circadian rhythm analysis and visualization

TL;DR

easyClock is a user-friendly desktop app that simplifies the analysis and visualization of circadian rhythms without requiring coding skills.

Contribution

easyClock introduces an accessible, open-source tool for circadian data analysis that supports batch processing and statistical modeling.

Findings

easyClock allows batch analysis of multiple time series data files with various waveforms and noise levels.

The application integrates linear mixed-effects modeling to assess inter-individual variability and group differences.

A re-analysis of transcriptomic data demonstrates the utility of easyClock as an accessible circadian analysis tool.

Abstract

Circadian rhythms regulate a wide range of biological processes, and their precise characterization is essential for understanding behavioral and physiological fluctuations. However, existing tools to analyze circadian data often require coding expertise or rely on specific data acquisition software, limiting their general applicability. Here, we present easyClock, an intuitive and interactive application designed to streamline circadian rhythm analysis and visualization. The easyClock application enables simultaneous processing of multiple files, allowing users to batch-analyze and visualize diverse sets of time series data. To enhance data analysis efficiency and provide comparable results, this application integrates comprehensive methods for handling data with various waveforms and noises. Additionally, easyClock can assess inter-individual variability and group differences using linear mixed-effects modeling. All statistical results and graphs are easily viewed and exported for any selected range of data. As a demonstration, we present a re-analysis of a time-series transcriptomic dataset, highlighting the value of easyClock as an accessible, open-source tool. This easy-to-use application requires no programming expertise and can be directly installed on Windows and macOS machines in a single step.

The online version contains supplementary material available at 10.1186/s12859-025-06340-9.

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