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

Author:

Mei Wenwen1,Jiang Zhiwen1,Chen Yang2,Chen Li3ORCID,Sancar Aziz4,Jiang Yuchao5ORCID

Affiliation:

1. Department of Biostatistics, University of North Carolina at Chapel Hill

2. Department of Statistics and the Michigan Institute of Data Science, University of Michigan

3. Department of Medicine and a member of the Center for Computational Biology and Bioinformatics, Indiana University School of Medicine

4. Biochemistry and Biophysics at the University of North Carolina School of Medicine

5. Department of Biostatistics and the Department of Genetics, University of North Carolina at Chapel Hill and a member of UNC Lineberger Comprehensive Cancer Center

Abstract

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.

Funder

NIH

UNC Computational Medicine Program

Publisher

Oxford University Press (OUP)

Subject

Molecular Biology,Information Systems

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