Department of Biostatistics, University of North Carolina at Chapel Hill.
Department of Statistics and the Michigan Institute of Data Science, University of Michigan.
Brief Bioinform. 2021 May 20;22(3). doi: 10.1093/bib/bbaa135.
Circadian rhythms are oscillations of behavior, physiology and metabolism in many organisms. Recent advancements in omics technology make it possible for genome-wide profiling of circadian rhythms. Here, we conducted a comprehensive analysis of seven existing algorithms commonly used for circadian rhythm detection. Using gold-standard circadian and non-circadian genes, we systematically evaluated the accuracy and reproducibility of the algorithms on empirical datasets generated from various omics platforms under different experimental designs. We also carried out extensive simulation studies to test each algorithm's robustness to key variables, including sampling patterns, replicates, waveforms, signal-to-noise ratios, uneven samplings and missing values. Furthermore, we examined the distributions of the nominal $P$-values under the null and raised issues with multiple testing corrections using traditional approaches. With our assessment, we provide method selection guidelines for circadian rhythm detection, which are applicable to different types of high-throughput omics data.
昼夜节律是许多生物体的行为、生理和代谢的波动。组学技术的最新进展使得对昼夜节律进行全基因组分析成为可能。在这里,我们对七种常用的昼夜节律检测算法进行了全面分析。使用金标准的昼夜节律和非昼夜节律基因,我们系统地评估了这些算法在不同实验设计下各种组学平台生成的经验数据集上的准确性和可重复性。我们还进行了广泛的模拟研究,以测试每个算法对关键变量(包括采样模式、重复、波形、信噪比、不均匀采样和缺失值)的稳健性。此外,我们还检查了在零假设下名义 $P$ 值的分布,并使用传统方法提出了对多重测试校正的问题。通过我们的评估,我们为昼夜节律检测提供了方法选择指南,适用于不同类型的高通量组学数据。