ArticleStatistics in medicine2023
Experimental design and power calculation in omics circadian rhythmicity detection using the cosinor model.
Article in Statistics in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Circadian clock disruption impairs immune oscillation in chronic endogenous hypercortisolism: a multi-level analysis from a multicentre clinical trial.EBioMedicine · 2024Trial
- Research note: Cosinor-based rhythmometry reveals how heat stress disrupts the circadian rhythm of core body temperature in broilers.Poultry science · 2026Article
- Digital Phenotyping via Passive Network Traffic Monitoring: Prospective Observational Study in University Students.JMIR formative research · 2026Observational
- BAYESIAN RHYTHMIC MODEL FOR JOINTLY DETECTING CIRCADIAN BIOMARKERS AND PREDICTING MOLECULAR CIRCADIAN TIME IN HUMAN POST-MORTEM BRAIN TRANSCRIPTOME.bioRxiv : the preprint server for biology · 2026Article
- Circadian clock control of ribosome composition promotes rhythmic translation and termination fidelity.Cell reports · 2025Article
- Optimization of experimental designs for biological rhythm discovery.PLoS computational biology · 2025Article
- A Bayesian framework for genome-wide circadian rhythmicity biomarker detection.Briefings in bioinformatics · 2025Article
- Minding Averages: Comment on "Experimental Design and Power Calculation in Omics Circadian Rhythmicity Detection Using the Cosinor Model" by Zong et al.Statistics in medicine · 2025Article
- Altered circadian gene expression in primary human airway epithelial cells in asthma.ERJ open research · 2024Article
- Comparative transcriptomic rhythms in the mouse and human prefrontal cortex.Frontiers in neuroscience · 2024Article
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Authors and funding
9 authors.
Funding
Abstract
Circadian clocks are 24-h endogenous oscillators in physiological and behavioral processes. Though recent transcriptomic studies have been successful in revealing the circadian rhythmicity in gene expression, the power calculation for omics circadian analysis have not been fully explored. In this paper, we develop a statistical method, namely CircaPower, to perform power calculation for circadian pattern detection. Our theoretical framework is determined by three key factors in circadian gene detection: sample size, intrinsic effect size and sampling design. Via simulations, we systematically investigate the impact of these key factors on circadian power calculation. We not only demonstrate that CircaPower is fast and accurate, but also show its underlying cosinor model is robust against variety of violations of model assumptions. In real applications, we demonstrate the performance of CircaPower using mouse pan-tissue data and human post-mortem brain data, and illustrate how to perform circadian power calculation using mouse skeleton muscle RNA-Seq pilot as case study. Our method CircaPower has been implemented in an R package, which is made publicly available on GitHub ( https://github.com/circaPower/circaPower).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.