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A Bayesian framework for genome-wide circadian rhythmicity biomarker detection.

Haocheng Ding1, Lingsong Meng2, Yutao Zhang2

  • 1Department of Biostatistics, Data Science, and Epidemiology, Augusta University, 1120 15th Street, Augusta, 30912 GA, United States.

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|October 28, 2025
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This study introduces BayesCircRhy, a new Bayesian method for detecting circadian rhythms in gene expression data. It improves accuracy and reliability in identifying circadian genes, crucial for understanding health and disease.

Keywords:
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Area of Science:

  • Chronobiology
  • Genomics
  • Biostatistics

Background:

  • Circadian rhythms, driven by gene feedback loops, regulate essential physiological and behavioral processes.
  • Accurate detection of circadian biomarkers is vital, especially with expanding omics datasets.
  • Existing methods for circadian rhythm analysis in transcriptomics face limitations.

Purpose of the Study:

  • To develop a novel Bayesian framework for detecting circadian rhythms in genome-wide transcriptomic data.
  • To incorporate prior biological knowledge and control for multiple testing using a false discovery rate (FDR) approach.
  • To provide a robust and accurate analytical tool for circadian gene identification.

Main Methods:

  • Developed a Bayesian hierarchical model incorporating prior biological information.
  • Utilized a reverse jump Markov chain Monte Carlo (RJMCMC) technique for model selection.
  • Implemented a false discovery rate (FDR) approach for multiple testing adjustment.

Main Results:

  • The proposed method, BayesCircRhy, demonstrated superior FDR control compared to competing methods in simulations.
  • BayesCircRhy showed robustness against various error distributions and outperformed existing approaches.
  • Validated in human and mouse RNA-sequencing data, identifying known and novel circadian genes.

Conclusions:

  • BayesCircRhy offers a powerful and reliable framework for circadian rhythm detection in transcriptomics.
  • The method enhances the identification of circadian genes, contributing to a better understanding of health and aging.
  • This Bayesian approach provides a valuable tool for analyzing complex biological rhythms in omics studies.