Likelihood-based Tests for Detecting Circadian Rhythmicity and Differential Circadian Patterns in Transcriptomic Applications Article Swipe
Haocheng Ding
,
Lingsong Meng
,
Andrew C. Liu
,
Michelle L. Gumz
,
Andrew J. Bryant
,
Colleen A. McClung
,
George C. Tseng
,
Karyn A. Esser
,
Zhiguang Huo
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1101/2021.02.23.432538
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1101/2021.02.23.432538
Circadian rhythmicity in transcriptomic profiles has been shown in many physiological processes, and the disruption of circadian patterns has been founded to associate with several diseases. In this paper, we developed a series of likelihood-based methods to detect (i) circadian rhythmicity (denoted as LR rhythmicity) and (ii) differential circadian patterns comparing two experimental conditions (denoted as LR diff). In terms of circadian rhythmicity detection, we demonstrated that our proposed LR rhythmicity could better control the type I error rate compared to existing methods under a wide variety of simulation settings. In terms of differential circadian patterns, we developed methods in detecting differential amplitude, differential phase, differential basal level, and differential fit, which also successfully controlled the type I error rate. In addition, we demonstrated that the proposed LR diff could achieve higher statistical power in detecting differential fit, compared to existing methods. The superior performance of LR rhythmicity and LR diff was demonstrated in two real data applications, including a brain aging data (gene expression microarray data of human postmortem brain) and a time-restricted feeding data (RNA sequencing data of human skeletal muscles). An R package for our methods is publicly available on GitHub https://github.com/diffCircadian/diffCircadian .
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2021.02.23.432538
- https://www.biorxiv.org/content/biorxiv/early/2021/02/24/2021.02.23.432538.full.pdf
- OA Status
- green
- Cited By
- 5
- References
- 40
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3171637067
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3171637067Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2021.02.23.432538Digital Object Identifier
- Title
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Likelihood-based Tests for Detecting Circadian Rhythmicity and Differential Circadian Patterns in Transcriptomic ApplicationsWork title
- Type
-
preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
-
2021-02-24Full publication date if available
- Authors
-
Haocheng Ding, Lingsong Meng, Andrew C. Liu, Michelle L. Gumz, Andrew J. Bryant, Colleen A. McClung, George C. Tseng, Karyn A. Esser, Zhiguang HuoList of authors in order
- Landing page
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https://doi.org/10.1101/2021.02.23.432538Publisher landing page
- PDF URL
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https://www.biorxiv.org/content/biorxiv/early/2021/02/24/2021.02.23.432538.full.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.biorxiv.org/content/biorxiv/early/2021/02/24/2021.02.23.432538.full.pdfDirect OA link when available
- Concepts
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Circadian rhythm, Differential (mechanical device), Biology, Transcriptome, Neuroscience, Gene expression, Gene, Genetics, Aerospace engineering, EngineeringTop concepts (fields/topics) attached by OpenAlex
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5Total citation count in OpenAlex
- Citations by year (recent)
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2024: 3, 2022: 2Per-year citation counts (last 5 years)
- References (count)
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40Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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