WAVELET SPECTRAL TESTING: APPLICATION TO NONSTATIONARY CIRCADIAN RHYTHMS
WAVELET SPECTRAL TESTING: APPLICATION TO NONSTATIONARY CIRCADIAN RHYTHMS
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DOI：
10.1214/19-aoas1246
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发表时间：
2019-09-01
影响因子：
1.8
通讯作者：
Davis, Seth J.
中科院分区：
文献类型：
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作者：
Hargreaves, Jessica K.;Knight, Marina, I;Davis, Seth J.
在生命科学中，节律数据无处不在。生物学家需要可靠的统计测试来确定特定的实验性治疗是否导致了节律信号的显著变化。当这些信号表现出非平稳行为时，就像许多生物系统中常见的那样，所建立的方法可能具有误导性。因此，确实需要一种新的方法来实现非平稳过程的正式比较。由于昼夜节律行为在谱域中得到了最好的理解，因此我们在(小波)谱域中开发了新的假设检验方法，在可用的情况下嵌入复制信息。数据被建模为局部平稳小波过程的实现，允许我们定义并严格估计它们的演化小波谱。受昼夜节律生物学中三种互补应用的启发，我们的新方法允许识别三种特定类型的光谱差异。我们通过一项全面的模拟研究和实际数据应用，使用已公布的和新生成的昼夜节律数据集，展示了我们的方法相对于其他方法的优势。与当前的标准方法相比，我们的方法成功地识别了激励昼夜节律数据集中的差异，并促进了对节律生物数据的更广泛的分析。
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.