Circadian rhythms are daily oscillations in physiology and behavior that can be assessed by recording body temperature, locomotor activity, or bioluminescent reporters, among other measures. These different types of data can vary greatly in waveform, noise characteristics, typical sampling rate, and length of recording. We developed 2 Shiny apps for exploration of these data, enabling visualization and analysis of circadian parameters such as period and phase. Methods include the discrete wavelet transform, sine fitting, the Lomb-Scargle periodogram, autocorrelation, and maximum entropy spectral analysis, giving a sense of how well each method works on each type of data. The apps also provide educational overviews and guidance for these methods, supporting the training of those new to this type of analysis. CIRCADA-E (Circadian App for Data Analysis-Experimental Time Series) allows users to explore a large curated experimental data set with mouse body temperature, locomotor activity, and PER2::LUC rhythms recorded from multiple tissues. CIRCADA-S (Circadian App for Data Analysis-Synthetic Time Series) generates and analyzes time series with user-specified parameters, thereby demonstrating how the accuracy of period and phase estimation depends on the type and level of noise, sampling rate, length of recording, and method. We demonstrate the potential uses of the apps through 2 in silico case studies.

中文翻译：

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CIRCADA：具有教育意义的用于探索实验和合成昼夜时间序列的闪亮应用程序。

昼夜节律是生理和行为的日常振荡，可通过记录体温，运动活动或生物发光报告物等来评估。这些不同类型的数据在波形，噪声特性，典型采样率和记录长度方面会有很大差异。我们开发了2个Shiny应用程序来探索这些数据，从而能够可视化和分析昼夜节律参数，例如周期和相位。方法包括离散小波变换，正弦拟合，Lomb-Scargle周期图，自相关和最大熵谱分析，从而使您了解每种方法在每种类型的数据上的效果如何。这些应用程序还提供了有关这些方法的教育性概述和指导，支持对此类分析的新手进行培训。CIRCADA-E（数据分析的昼夜应用程序-实验性时间序列）使用户能够探索大型的精选实验数据集，其中包括小鼠体温，运动活动以及从多个组织记录的PER2 :: LUC节律。CIRCADA-S（数据分析的昼夜应用程序-合成时间序列）使用用户指定的参数生成和分析时间序列，从而演示了周期和相位估计的准确性如何取决于噪声的类型和级别，采样率，记录长度和方法。我们通过2个计算机模拟案例研究证明了这些应用程序的潜在用途。CIRCADA-S（数据分析的昼夜应用程序-合成时间序列）使用用户指定的参数生成和分析时间序列，从而演示了周期和相位估计的准确性如何取决于噪声的类型和级别，采样率，记录长度和方法。我们通过2个计算机模拟案例研究证明了这些应用程序的潜在用途。CIRCADA-S（数据分析的昼夜应用程序-合成时间序列）使用用户指定的参数生成和分析时间序列，从而演示了周期和相位估计的准确性如何取决于噪声的类型和级别，采样率，记录长度和方法。我们通过2个计算机模拟案例研究证明了这些应用程序的潜在用途。