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Zong W, Seney ML, Ketchesin KD, Gorczyca MT, Liu AC, Esser KA, Tseng GC, McClung CA, Huo Z. Experimental design and power calculation in omics circadian rhythmicity detection using the cosinor model. Stat Med 2023; 42:3236-3258. [PMID: 37265194 PMCID: PMC10425922 DOI: 10.1002/sim.9803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 03/27/2023] [Accepted: 05/09/2023] [Indexed: 06/03/2023]
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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Affiliation(s)
- Wei Zong
- Department of Biostatistics, University of Pittsburgh, PA, USA
| | - Marianne L. Seney
- Translational Neuroscience Program, Department of Psychiatry, Center for Neuroscience, University of Pittsburgh, PA, USA
| | - Kyle D. Ketchesin
- Translational Neuroscience Program, Department of Psychiatry, Center for Neuroscience, University of Pittsburgh, PA, USA
| | - Michael T. Gorczyca
- Department of Computational and Systems Biology, University of Pittsburgh, PA, USA
| | - Andrew C. Liu
- Department of Physiology and Aging, University of Florida, FL, USA
| | - Karyn A. Esser
- Department of Physiology and Aging, University of Florida, FL, USA
| | - George C. Tseng
- Department of Biostatistics, University of Pittsburgh, PA, USA
| | - Colleen A. McClung
- Translational Neuroscience Program, Department of Psychiatry, Center for Neuroscience, University of Pittsburgh, PA, USA
| | - Zhiguang Huo
- Department of Biostatistics, University of Florida, FL, USA
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Persons JL, Abhilash L, Lopatkin AJ, Roelofs A, Bell EV, Fernandez MP, Shafer OT. PHASE: An Open-Source Program for the Analysis of Drosophila Phase, Activity, and Sleep Under Entrainment. J Biol Rhythms 2022; 37:455-467. [PMID: 35727044 PMCID: PMC10362883 DOI: 10.1177/07487304221093114] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
The problem of entrainment is central to circadian biology. In this regard, Drosophila has been an important model system. Owing to the simplicity of its nervous system and the availability of powerful genetic tools, the system has shed significant light on the molecular and neural underpinnings of entrainment. However, much remains to be learned regarding the molecular and physiological mechanisms underlying this important phenomenon. Under cyclic light/dark conditions, Drosophila melanogaster displays crepuscular patterns of locomotor activity with one peak anticipating dawn and the other anticipating dusk. These peaks are characterized through an estimation of their phase relative to the environmental light cycle and the extent of their anticipation of light transitions. In Drosophila chronobiology, estimations of phases are often subjective, and anticipation indices vary significantly between studies. Though there is increasing interest in building flexible analysis tools in the field, none incorporates objective measures of Drosophila activity peaks in combination with the analysis of fly activity/sleep in the same program. To this end, we have developed PHASE, a MATLAB-based program that is simple and easy to use and (i) supports the visualization and analysis of activity and sleep under entrainment, (ii) allows analysis of both activity and sleep parameters within user-defined windows within a diurnal cycle, (iii) uses a smoothing filter for the objective identification of peaks of activity (and therefore can be used to quantitatively characterize them), and (iv) offers a series of analyses for the assessment of behavioral anticipation of environmental transitions.
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Affiliation(s)
- J L Persons
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, Michigan
| | - L Abhilash
- Advanced Science Research Center, The Graduate Center, City University of New York, New York City, NY
| | - A J Lopatkin
- Department of Biology, Barnard College, New York, NY.,Data Science Institute, Columbia University, New York, NY.,Department of Ecology, Evolution, and Environmental Biology, Columbia University, New York, NY
| | - A Roelofs
- Technology Services, College of Literature, Science and the Arts, University of Michigan, Ann Arbor, Michigan
| | - E V Bell
- Department of Neuroscience & Behavior, Barnard College, New York, NY
| | - Maria P Fernandez
- Department of Neuroscience & Behavior, Barnard College, New York, NY
| | - Orie T Shafer
- Advanced Science Research Center, The Graduate Center, City University of New York, New York City, NY
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Abstract
Circadian omics analyses present investigators with large amounts of data to consider and many choices for methods of analysis. Visualization is crucial as rhythmicity can take many forms and p-values offer an incomplete picture. Yet statically viewing the entirety of high-throughput datasets is impractical, and there is often limited ability to assess the impact of choices, such as significance threshold cutoffs. Nitecap provides an intuitive and unified web-based solution to these problems. Through highly responsive visualizations, Nitecap enables investigators to see dataset-wide behavior. It supports deep analyses, including comparisons of two conditions. Moreover, it focuses upon ease-of-use and enables collaboration through dataset sharing. As an application, we investigated cross talk between peripheral clocks in adipose and liver tissues and determined that adipocyte clock disruption does not substantially modulate the transcriptional rhythmicity of liver but does advance the phase of core clock gene Bmal1 (Arntl) expression in the liver. Nitecap is available at nitecap.org and is free-to-use.
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Affiliation(s)
- Thomas G Brooks
- Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Antonijo Mrčela
- Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Nicholas F Lahens
- Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Georgios K Paschos
- Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Tilo Grosser
- Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Carsten Skarke
- Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Garret A FitzGerald
- Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania.,Chronobiology and Sleep Institute, University of Pennsylvania, Philadelphia, Pennsylvania.,Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Gregory R Grant
- Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania.,Department of Genetics, University of Pennsylvania, Philadelphia, Pennsylvania
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