A novel Bayesian hierarchical model for detecting differential circadian pattern in transcriptomic applications.

ABSTRACT: Circadian rhythm plays a critical role in regulating various physiological processes, and disruptions in these rhythms have been linked to a wide range of diseases. Identifying molecular biomarkers showing differential circadian (DC) patterns between biological conditions or disease status is important for disease prevention, diagnosis, and treatment. However, circadian pattern is characterized by three key components: amplitude, phase, and MESOR, which poses a great challenge for DC analysis. Existing statistical methods focus on detecting differential shape (amplitude and phase) but often overlook MESOR difference. Additionally, these methods lack flexibility to incorporate external knowledge such as differential circadian information from similar clinical and biological context to impro

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC11998590 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature