Genome-wide circadian rhythm detection methods: systematic evaluations and practical guidelines

ABSTRACT 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. Key points Various methods have been developed for circadian rhythm detection on a genome-wide scale using omics technologies, yet there has not been a comprehensive summary and evaluation of all existing methods to date. Using gold-standard circadian and non-circadian genes, we systematically evaluated the accuracy and reproducibility of seven existing algorithms for circadian rhythm detection on empirical datasets generated from various omics platforms. We 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. We examined the distributions of the nominal p -values under the null and raised issues with multiple testing corrections using the Benjamini-Hochberg procedure due to gene-gene correlation and testing being overly conservative. We provide method selection guidelines for circadian rhythm detection, which are applicable to different types of high-throughput omics data.