Experimental design and power calculation in omics circadian rhythmicity detection using the cosinor model
Year: 2023
Type: article
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).
Source: Statistics in Medicine
DOI: 10.1002/sim.9803
Language: English
Subfield: Endocrine and Autonomic Systems
Field: Neuroscience
Domain: Life Sciences
Open Access status: bronze
Funders: National Institutes of Health, National Institute on Aging, National Heart, Lung, and Blood Institute, National Institute of Mental Health, National Institute on Drug Abuse, National Institute of Arthritis and Musculoskeletal and Skin Diseases, U.S. National Library of Medicine, National Center for Advancing Translational Sciences
Awards: G1404629393, G25694937, G2604298292, G4454448656, Congruence of mouse model to human in transcriptomic response, G9000121316, G9000202542, G9000247995, G9000345996, University of Pittsburgh Clinical and Translational Science Institute, University of Florida Older Americans Independence Center (OAIC), Disease subtyping guided by clinical phenotype for precision medicine, Circadian Clock and Muscle Health
OpenAlex ID: W4379094018