Hargreaves, Jessica Kate orcid.org/0000-0002-7173-7902, Knight, Marina Iuliana orcid.org/0000-0001-9926-6092, Pitchford, Jonathan William orcid.org/0000-0002-8756-0902 et al. (4 more authors) (2019) Wavelet spectral testing:application to nonstationary circadian rhythms. Annals of Applied Statistics. pp. 1817-1846. ISSN 1932-6157
Abstract
Rhythmic data are ubiquitous in the life sciences. Biologists need reliable statistical tests to identify whether a particular experimental treatment has caused a significant change in a rhythmic signal. When these signals display nonstationary behaviour, as is common in many biological systems, the established methodologies may be misleading. Therefore, there is a real need for new methodology that enables the formal comparison of nonstationary processes. As circadian behaviour is best understood in the spectral domain, here we develop novel hypothesis testing procedures in the (wavelet) spectral domain, embedding replicate information when available. The data are modelled as realisations of locally stationary wavelet processes, allowing us to define and rigorously estimate their evolutionary wavelet spectra. Motivated by three complementary applications in circadian biology, our new methodology allows the identification of three specific types of spectral difference. We demonstrate the advantages of our methodology over alternative approaches, by means of a comprehensive simulation study and real data applications, using both published and newly generated circadian datasets. In contrast to the current standard methodologies, our method successfully identifies differences within the motivating circadian datasets, and facilitates wider ranging analyses of rhythmic biological data in general.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details. |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Mathematics (York) The University of York > Faculty of Sciences (York) > Biology (York) |
Depositing User: | Pure (York) |
Date Deposited: | 20 Feb 2019 11:40 |
Last Modified: | 14 Feb 2025 00:09 |
Published Version: | https://doi.org/10.1214/19-AOAS1246 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1214/19-AOAS1246 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:142815 |
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