ArticleJournal of biological rhythms2019
LimoRhyde: A Flexible Approach for Differential Analysis of Rhythmic Transcriptome Data.
Article in Journal of biological rhythms, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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Who cites it
46 citing papers in PubMed.
- Circadian rhythm disruption by PARP inhibitors correlates with treatment toxicity in patients with ovarian cancer and is a predictor of side effects.EBioMedicine · 2025Trial
- The Liver Clock Tunes Transcriptional Rhythms in Skeletal Muscle to Regulate Mitochondrial Function.Journal of biological rhythms · 2026Article
- easyClock: a user-friendly desktop application for circadian rhythm analysis and visualization.BMC bioinformatics · 2025Article
- A circadian transcriptional subnetwork and EARLY FLOWERING 3 control timing of senescence and grain nutrition in bread wheat.The New phytologist · 2025Article
- Proteomic aging signatures across mouse organs and life stages.The EMBO journal · 2025Article
- A novel Bayesian hierarchical model for detecting differential circadian pattern in transcriptomic applications.Briefings in bioinformatics · 2025Article
- Designing and Evaluating Circadian Experiments on Mouse Skin.The Journal of investigative dermatology · 2025Review
- Carbonyl reductase 1: a novel regulator of blood pressure in Down syndrome.Bioscience reports · 2025Article
- Time to start taking time seriously: how to investigate unexpected biological rhythms within infectious disease research.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025Review
- Thousands of oscillating LncRNAs in the mouse testis.Computational and structural biotechnology journal · 2024Article
- Detecting Rhythmic Gene Expression in Single-cell Transcriptomics.Journal of biological rhythms · 2024Article
- Rhythmic lipid and gene expression responses to chilling in panicoid grasses.Journal of experimental botany · 2024Article
- Detecting Rhythmic Gene Expression in Single Cell Transcriptomics.bioRxiv : the preprint server for biology · 2024Article
- Temperature-driven coordination of circadian transcriptional regulation.PLoS computational biology · 2024Article
- Coordination of rhythmic RNA synthesis and degradation orchestrates 24- and 12-h RNA expression patterns in mouse fibroblasts.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Tumor circadian clock strength influences metastatic potential and predicts patient prognosis in luminal A breast cancer.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- A Comparative Study of Algorithms Detecting Differential Rhythmicity in Transcriptomic Data.Bioinformatics and biology insights · 2024Article
- Molecular characterization of the circadian clock in patients with Parkinson's disease-CLOCK4PD Study protocol.PloS one · 2024Article
- Temperature-driven coordination of circadian transcriptome regulation.bioRxiv : the preprint server for biology · 2023Article
- A comparative study of algorithms detecting differential rhythmicity in transcriptomic data.bioRxiv : the preprint server for biology · 2023Article
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2 authors.
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Abstract
Unraveling the effects of genetic or environmental perturbations on biological rhythms requires detecting changes in rhythmicity across multiple conditions. Although methods to detect rhythmicity in genome-scale data are well established, methods to detect changes in rhythmicity or changes in average expression between experimental conditions are often ad hoc and statistically unreliable. Here we present LimoRhyde (linear models for rhythmicity, design), a flexible approach for analyzing transcriptome data from circadian systems. Borrowing from cosinor regression, LimoRhyde decomposes circadian or zeitgeber time into multiple components to fit a linear model to the expression of each gene. The linear model can accommodate any number of additional experimental variables, whether discrete or continuous, making it straightforward to detect differential rhythmicity and differential expression using state-of-the-art methods for analyzing microarray and RNA-seq data. In this approach, differential rhythmicity corresponds to a statistical interaction between an experimental variable and circadian time, whereas differential expression corresponds to the main effect of an experimental variable while accounting for circadian time. To validate LimoRhyde's performance, we applied it to simulated data. To demonstrate LimoRhyde's versatility, we applied it to murine and human circadian transcriptome datasets acquired under various experimental designs. Our results show how LimoRhyde systematizes the analysis of such data, and suggest that LimoRhyde could prove valuable for assessing how circadian systems respond to perturbations.
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