CLUSTERING NONSTATIONARY CIRCADIAN RHYTHMS USING LOCALLY STATIONARY WAVELET REPRESENTATIONS
CLUSTERING NONSTATIONARY CIRCADIAN RHYTHMS USING LOCALLY STATIONARY WAVELET REPRESENTATIONS
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DOI：
10.1137/16m1108078
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发表时间：
2018-01-01
影响因子：
1.6
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.
有节奏的过程存在于所有生物和生态尺度上，是生命系统在不断变化的环境中有效运作的基础。因此，支持这些节律的生化机制具有重要意义，特别是在污染或气候和土地利用变化等人为挑战的背景下。在这里，我们开发和测试了一种新的方法，用于聚集有节奏的生物数据，重点是昼夜振荡。该方法将局部平稳小波时间序列建模与函数主成分分析相结合，提取了一系列节律数据的时间尺度模式。我们通过模拟研究和实际数据应用，使用已公布的昼夜节律数据集和新生成的数据集，展示了我们的方法相对于替代方法的优势。新的数据集记录了植物对土壤污染物引起的不同程度压力的反应，在这个生物系统中，现有的假定静止的方法被证明是不合适的。我们的方法以一种有趣的方式成功地将昼夜节律数据聚集在一起，从而促进了对生物节律对环境变化的反应的更广泛的分析。
Rhythmic processes are found at all biological and ecological scales, and are fundamental to the efficient functioning of living systems in changing environments. The biochemical mechanisms underpinning these rhythms are therefore of importance, especially in the context of anthropogenic challenges such as pollution or changes in climate and land use. Here we develop and test a new method for clustering rhythmic biological data with a focus on circadian oscillations. The method combines locally stationary wavelet time series modelling with functional principal components analysis and thus extracts the time-scale patterns arising in a range of rhythmic data. We demonstrate the advantages of our methodology over alternative approaches, by means of a simulation study and real data applications, using both a published circadian dataset and a newly generated one. The new dataset records plant response to various levels of stress induced by a soil pollutant, a biological system where existing methods which assume stationarity are shown to be inappropriate. Our method successfully clusters the circadian data in an interesting way, thereby facilitating wider ranging analyses of the response of biological rhythms to environmental changes.