ArticleBriefings in bioinformatics2021
Likelihood-based tests for detecting circadian rhythmicity and differential circadian patterns in transcriptomic applications.
Article in Briefings in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 32 citations in OpenAlex.
- BAYESIAN RHYTHMIC MODEL FOR JOINTLY DETECTING CIRCADIAN BIOMARKERS AND PREDICTING MOLECULAR CIRCADIAN TIME IN HUMAN POST-MORTEM BRAIN TRANSCRIPTOME.bioRxiv : the preprint server for biology · 2026Article
- DCPR: a deep learning framework for circadian phase reconstruction.BMC bioinformatics · 2025Article
- Multidimensional Modeling to Maximize Adaptations to eXercise: the MJournal of applied physiology (Bethesda, Md. : 1985) · 2025Article
- A Bayesian framework for genome-wide circadian rhythmicity biomarker detection.Briefings in bioinformatics · 2025Article
- Cancer-induced FOXP1 disrupts and reprograms skeletal-muscle circadian transcription in cachexia.Cell reports · 2025Article
- Single-Cell Multiomic Analysis of Circadian Rhythmicity in Mouse Liver.bioRxiv : the preprint server for biology · 2025Article
- Control of circadian muscle glucose metabolism through the BMAL1-HIF axis in obesity.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- A novel Bayesian hierarchical model for detecting differential circadian pattern in transcriptomic applications.Briefings in bioinformatics · 2025Article
- The Core Circadian Clock Factor, Bmal1, Transduces Sex-specific Differences in Both Rhythmic and Nonrhythmic Gene Expression in the Mouse Heart.Function (Oxford, England) · 2025Article
- Maize inbreds show allelic variation for diel transcription patterns.bioRxiv : the preprint server for biology · 2024Article
- Transcriptomic Plasticity of the Circadian Clock in Response to Photoperiod: A Study in Male Melatonin-Competent Mice.Journal of biological rhythms · 2024Article
- New role for cardiomyocytebioRxiv : the preprint server for biology · 2024Article
- A Comparative Study of Algorithms Detecting Differential Rhythmicity in Transcriptomic Data.Bioinformatics and biology insights · 2024Article
- A comparative study of algorithms detecting differential rhythmicity in transcriptomic data.bioRxiv : the preprint server for biology · 2023Article
- Experimental design and power calculation in omics circadian rhythmicity detection using the cosinor model.Statistics in medicine · 2023Article
- Defining the age-dependent and tissue-specific circadian transcriptome in male mice.Cell reports · 2023Article
- DiffCircaPipeline: a framework for multifaceted characterization of differential rhythmicity.Bioinformatics (Oxford, England) · 2023Article
- The 2022 On-site Padua Days on Muscle and Mobility Medicine hosts the University of Florida Institute of Myology and the Wellstone Center, March 30 - April 3, 2022 at the University of Padua and Thermae of Euganean Hills, Padua, Italy: The collection of abstracts.European journal of translational myology · 2022Article
- Systematic modeling-driven experiments identify distinct molecular clockworks underlying hierarchically organized pacemaker neurons.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Nitecap: An Exploratory Circadian Analysis Web Application.Journal of biological rhythms · 2022Article
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
Abstract
Circadian rhythmicity in transcriptomic profiles has been shown in many physiological processes, and the disruption of circadian patterns has been found to associate with several diseases. In this paper, we developed a series of likelihood-based methods to detect (i) circadian rhythmicity (denoted as LR_rhythmicity) and (ii) differential circadian patterns comparing two experimental conditions (denoted as LR_diff). In terms of circadian rhythmicity detection, we demonstrated that our proposed LR_rhythmicity could better control the type I error rate compared to existing methods under a wide variety of simulation settings. In terms of differential circadian patterns, we developed methods in detecting differential amplitude, differential phase, differential basal level and differential fit, which also successfully controlled the type I error rate. In addition, we demonstrated that the proposed LR_diff could achieve higher statistical power in detecting differential fit, compared to existing methods. The superior performance of LR_rhythmicity and LR_diff was demonstrated in four real data applications, including a brain aging data (gene expression microarray data of human postmortem brain), a time-restricted feeding data (RNA sequencing data of human skeletal muscles) and a scRNAseq data (single cell RNA sequencing data of mouse suprachiasmatic nucleus). An R package for our methods is publicly available on GitHub https://github.com/diffCircadian/diffCircadian.
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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.