---
title: "scRhythmBayes: Bayesian single-cell method to infer circadian phase and rhythm-state remodeling"
id: "biorxiv-7-decoding-cell-level-circadian-phase-and-rhythm-state-remodeling-in-single-cell"
canonical_url: "https://medichelpline.com/clinical-feed/biorxiv-7-decoding-cell-level-circadian-phase-and-rhythm-state-remodeling-in-single-cell"
content_type: "clinical_feed_article"
specialty: "General"
source_name: "bioRxiv (Biomedical Preprints)"
source_url: "https://www.biorxiv.org/content/10.64898/2026.10.18.759931v1?rss=1"
published_at: "2026-10-22T12:00:00.000Z"
evidence_level: "Verified Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# scRhythmBayes: Bayesian single-cell method to infer circadian phase and rhythm-state remodeling
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/biorxiv-7-decoding-cell-level-circadian-phase-and-rhythm-state-remodeling-in-single-cell
- **Specialty:** [General](https://medichelpline.com/clinical-feed/general.md)
- **Primary Source:** bioRxiv (Biomedical Preprints)
- **Source URL:** [Original Journal Publication](https://www.biorxiv.org/content/10.64898/2026.10.18.759931v1?rss=1)
- **Published At:** 2026-10-22T12:00:00.000Z
- **Evidence Rating:** Verified Feed
## Executive GIST (TL;DR)
- The authors introduced **scRhythmBayes**, a probabilistic algorithm designed to infer cell-level circadian phase and gene-level rhythm-state changes from heterogeneous single-cell transcriptomes. - The approach combines a seed-gene count likelihood with a stratum-aware circular prior to estimate unobserved cellular phase from datasets where sample-level zeitgeber time does not specify each cell’s phase. - The model propagates cell-level phase support into gene-level rhythmic modeling and produces posterior probabilities for rhythm-state categories: rhythm gain, rhythm loss, amplitude remodeling, and phase shifts. - In heterogeneous simulations the study team reported higher phase-recovery accuracy compared with existing phase-inference methods and maintained balanced performance across rhythmic gene detection, parameter recovery, and rhythm-state classification. - The authors ran stress tests that defined practical data requirements for seed-gene support, zeitgeber anchoring, temporal sampling density, and cell number; the abstract reports these tests but does not provide numeric thresholds in the source text. - Applied to real data, scRhythmBayes identified distinct **circadian** phase occupancy and rhythm-state organization between **periportal** and **pericentral hepatocytes** in mouse liver, implying a temporal dimension to liver zonation. - The pipeline also found cell-type-specific rhythmic markers that differed from conventional static markers, suggesting cellular identity includes temporal programs not captured by average expression. - In human heart disease datasets the investigators observed cell-type-selective temporal disorganization rather than uniform circadian disruption across the tissue. - Code and data are available via a GitHub repository referenced in the article footnotes; the preprint is not peer-reviewed and the source lists supplementary material and external links for data and code.
## Clinical Analysis & Structured Key Points
Decoding Cell-level Circadian Phase and Rhythm-state Remodeling in Single-cell Transcriptomes With scRhythmBayes | bioRxiv Skip to main content New Results Decoding Cell-level Circadian Phase and Rhythm-state Remodeling in Single-cell Transcriptomes With scRhythmBayes Qian Zhang , Chenyu Li , Ioannis Konstantinos Deligiannis , Wenhong Jiao , Weigang Liu , Celia Pilar Martinez-Jimenez , Zhibin Wang , Kenneth A. Dyar , Liwei Zhang , Pin Sun doi: https://doi.org/10.64898/2026.10.18.759931 Qian Zhang 1 Affiliated Hospital of Qingdao University, Cardiac Ultrasound Department, Qingdao, China; Find this author on Google Scholar Find this author on PubMed Search for this author on this site Chenyu Li 1 Affiliated Hospital of Qingdao University, Cardiac Ultrasound Department, Qingdao, China; Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ioannis Konstantinos Deligiannis 2 Institute of Biotechnology and Biomedicine (BIOTECMED); Find this author on Google Scholar Find this author on PubMed Search for this author on this site Wenhong Jiao 1 Affiliated Hospital of Qingdao University, Cardiac Ultrasound Department, Qingdao, China; Find this author on Google Scholar Find this author on PubMed Search for this author on this site Weigang Liu 1 Affiliated Hospital of Qingdao University, Cardiac Ultrasound Department, Qingdao, China; Find this author on Google Scholar Find this author on PubMed Search for this author on this site Celia Pilar Martinez-Jimenez 2 Institute of Biotechnology and Biomedicine (BIOTECMED); Find this author on Google Scholar Find this author on PubMed Search for this author on this site Zhibin Wang 1 Affiliated Hospital of Qingdao University, Cardiac Ultrasound Department, Qingdao, China; Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kenneth A. Dyar 3 Metabolic Physiology, Institute for Diabetes and Cancer, Helmholtz Munich, German Research Center for Environmental Health and German Center for Diabetes Research (DZD) Find this author on Google Scholar Find this author on PubMed Search for this author on this site Liwei Zhang 1 Affiliated Hospital of Qingdao University, Cardiac Ultrasound Department, Qingdao, China; Find this author on Google Scholar Find this author on PubMed Search for this author on this site Pin Sun 1 Affiliated Hospital of Qingdao University, Cardiac Ultrasound Department, Qingdao, China; Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: sunpinqdu{at}163.com Abstract Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Current circadian omics analysis tools have been developed primarily for bulk time-series. However, heterogeneous single-cell datasets require analysis and interpretation of rhythmic programs across different cells, cell types, and biological conditions. In these datasets, cellular circadian phase is usually unobserved, and sample-level zeitgeber time provides temporal information but does not define the exact phase of every cell. Existing methods address only selected aspects of this problem and do not model how cell-level phase evidence informs gene-level rhythmic parameters and rhythm-state remodeling. Here we present scRhythmBayes, a new probabilistic algorithm for phase-resolved circadian analysis in heterogeneous single-cell transcriptomes. scRhythmBayes combines a seed-gene count likelihood with a stratum-aware circular prior to infer cell-level circadian phase, incorporates phase support into gene-level rhythmic modeling, and assigns posterior probabilities to interpretable rhythm-state categories, including rhythm gain, rhythm loss, amplitude remodeling and phase shifts. In heterogeneous simulations, scRhythmBayes achieved higher phase-recovery accuracy than existing phase-inference methods and maintained balanced performance across rhythmic gene detection, parameter recovery, and rhythm-state classification. Stress tests defined practical requirements for seed-gene support, zeitgeber anchoring, temporal sampling density, and cell number. In mouse liver, scRhythmBayes revealed distinct circadian phase occupancy and rhythm-state organization between periportal and pericentral hepatocytes, supporting a temporal dimension of liver zonation. It also identified cell-type-specific rhythmic markers that were largely distinct from conventional static markers, indicating that cellular identity includes temporal programs not captured by average expression. In human heart disease data, phase-coordination and phase-distribution analyses identified cell-type-selective temporal disorganization rather than uniform circadian disruption across the tissue. scRhythmBayes therefore provides a statistically grounded approach for resolving circadian organization across cells, genes, cell types and biological conditions. Competing Interest Statement The authors have declared no competing interest. Footnotes https://github.com/Liwei-DynamicsLab/scRhythmBayes Funder Information Declared Natural Science Foundation of Shandong Province , ZR2024MH343 Helmholtz Pioneer Campus Generalitat Valenciana , CIDEXG/2023/30 Agencia Estatal de Investigación , ATR2024-154995 Spanish Ministry of Science and Innovation , PID2024-157730OB-I00 Helmholtz Munich German Center for Diabetes Research (DZD) German Research Foundation (DFG) , 536691227 Copyright The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. Back to top Previous Next Posted October 22, 2026. Download PDF Supplementary Material Data/Code Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Decoding Cell-level Circadian Phase and Rhythm-state Remodeling in Single-cell Transcriptomes With scRhythmBayes Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Decoding Cell-level Circadian Phase and Rhythm-state Remodeling in Single-cell Transcriptomes With scRhythmBayes Qian Zhang , Chenyu Li , Ioannis Konstantinos Deligiannis , Wenhong Jiao , Weigang Liu , Celia Pilar Martinez-Jimenez , Zhibin Wang , Kenneth A. Dyar , Liwei Zhang , Pin Sun bioRxiv 2026.10.18.759931; doi: https://doi.org/10.64898/2026.10.18.759931 Share This Article: Copy Citation Tools Decoding Cell-level Circadian Phase and Rhythm-state Remodeling in Single-cell Transcriptomes With scRhythmBayes Qian Zhang , Chenyu Li , Ioannis Konstantinos Deligiannis , Wenhong Jiao , Weigang Liu , Celia Pilar Martinez-Jimenez , Zhibin Wang , Kenneth A. Dyar , Liwei Zhang , Pin Sun bioRxiv 2026.10.18.759931; doi: https://doi.org/10.64898/2026.10.18.759931 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Areas All Articles Animal Behavior and Cognition (8164) Biochemistry (19027) Bioengineering (15171) Bioinformatics (45317) Biophysics (22958) Cancer Biology (20102) Cell Biology (27290) Clinical Trials (138) Developmental Biology (14141) Ecology (21390) Epidemiology (2067) Evolutionary Biology (25776) Genetics (16340) Genomics (23826) Immunology (19015) Microbiology (43172) Molecular Biology (18332) Neuroscience (95029) Paleontology (712) Pathology (3031) Pharmacology and Toxicology (5196) Physiology (8277) Plant Biology (16264) Scientific Communication and Education (2115) Synthetic Biology (4641) Systems Biology (10378) Zoology (2426)
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