ArticleJournal of biological rhythms2018
Bootstrapping and Empirical Bayes Methods Improve Rhythm Detection in Sparsely Sampled Data.
Article in Journal of biological rhythms, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Meta-analysis of Diurnal Transcriptomics in Mouse Liver Reveals Low Repeatability of Rhythm Analyses.Journal of biological rhythms · 2023Pooled it
- Neuroinflammatory suppression of astrocytic BMAL1 defines a selective reactive astrocyte program.Journal of neuroinflammation · 2026Article
- Keep It Simple: Using README Files to Advance Standardization in Chronobiology.Clocks & sleep · 2023Article
- Article
- Integrative Analysis of Rhythmicity: From Biology to Urban Environments and Sustainability.International journal of environmental research and public health · 2022Review
- Aging disrupts circadian gene regulation and function in macrophages.Nature immunology · 2022Article
- Time-course RNASeq of Camponotus floridanus forager and nurse ant brains indicate links between plasticity in the biological clock and behavioral division of labor.BMC genomics · 2022Article
- Interpreting machine learning models to investigate circadian regulation and facilitate exploration of clock function.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Applications of cosinor rhythmometry in pharmacology.Journal of pharmacokinetics and pharmacodynamics · 2021Article
- Genome-wide circadian rhythm detection methods: systematic evaluations and practical guidelines.Briefings in bioinformatics · 2021Article
- Comment on "Circadian rhythms in the absence of the clock geneScience (New York, N.Y.) · 2021Article
- CosinorPy: a python package for cosinor-based rhythmometry.BMC bioinformatics · 2020Article
- TimeTrial: An Interactive Application for Optimizing the Design and Analysis of Transcriptomic Time-Series Data in Circadian Biology Research.Journal of biological rhythms · 2020Article
- Methods detecting rhythmic gene expression are biologically relevant only for strong signal.PLoS computational biology · 2020Article
- LimoRhyde: A Flexible Approach for Differential Analysis of Rhythmic Transcriptome Data.Journal of biological rhythms · 2019Article
- Circadian Proteomic Analysis Uncovers Mechanisms of Post-Transcriptional Regulation in Metabolic Pathways.Cell systems · 2018Article
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Authors and funding
3 authors.
Funding
Abstract
There is much interest in using genome-wide expression time series to identify circadian genes. However, the cost and effort of such measurements often limit data collection. Consequently, it is difficult to assess the experimental uncertainty in the measurements and, in turn, to detect periodic patterns with statistical confidence. We show that parametric bootstrapping and empirical Bayes methods for variance shrinkage can improve rhythm detection in genome-wide expression time series. We demonstrate these approaches by building on the empirical JTK_CYCLE method (eJTK) to formulate a method that we term BooteJTK. Our procedure rapidly and accurately detects cycling time series by combining information about measurement uncertainty with information about the rank order of the time series values. We exploit a publicly available genome-wide data set with high time resolution to show that BooteJTK provides more consistent rhythm detection than existing methods at typical sampling frequencies. Then, we apply BooteJTK to genome-wide expression time series from multiple tissues and show that it reveals biologically sensible tissue relationships that eJTK misses. BooteJTK is implemented in Python and is freely available on GitHub at https://github.com/alanlhutchison/BooteJTK .
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.