scCircaSim: A Circadian Simulator for Single-Cell and Spatial Transcriptomics

Motivation: Simulated single-cell and spatial transcriptomic datasets that capture circadian dynamics while providing known ground truth remain scarce, limiting systematic evaluation of circadian analysis methods. Results: We developed scCircaSim to generate circadian single-cell and spatial transcriptomic data from a single-time-point reference. The framework combines phase progression with reference-derived network propagation of anchor gene rhythms and incorporates neighborhood effects for spatial simulation. Outputs retain reference heterogeneity and provide known cell or spot phases, rhythmicity labels, and gene-level mesor, amplitude, and phase values. Validation showed retention of reference structure and recovery of specified temporal patterns. Benchmarks of four cell phase inference methods and three rhythmic gene analysis methods revealed differences in phase accuracy, rhythmicity detection, and parameter recovery. scCircaSim supports reproducible method evaluation and preliminary experimental planning. Availability and Implementation: scCircaSim is implemented in R and available at https://github.com/Liwei-DynamicsLab/scCircaSim.

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Publication Details

Journal
bioRxiv (Cold Spring Harbor Laboratory)
Published
2026-11-06
DOI
https://doi.org/10.64898/2026.11.05.764753
Primary Topic
Single-cell and spatial transcriptomics
Type
preprint
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preprint

scCircaSim: A Circadian Simulator for Single-Cell and Spatial Transcriptomics

Zhibin Wang, Liang Zhao, Hong Zhou, Pin Sun et al.
3 links
bioRxiv (Cold Spring Harbor Laboratory)
Single-cell and spatial transcriptomics
preprint

scCircaSim: A Circadian Simulator for Single-Cell and Spatial Transcriptomics

Zhibin Wang, Liang Zhao, Hong Zhou, Pin Sun, Chenyu Li, Liwei Zhang, Qian Zhang, Yu Tian, Guozhang Tang, Xiaoxia Song
preprint en

Abstract

Motivation: Simulated single-cell and spatial transcriptomic datasets that capture circadian dynamics while providing known ground truth remain scarce, limiting systematic evaluation of circadian analysis methods. Results: We developed scCircaSim to generate circadian single-cell and spatial transcriptomic data from a single-time-point reference. The framework combines phase progression with reference-derived network propagation of anchor gene rhythms and incorporates neighborhood effects for spatial simulation. Outputs retain reference heterogeneity and provide known cell or spot phases, rhythmicity labels, and gene-level mesor, amplitude, and phase values. Validation showed retention of reference structure and recovery of specified temporal patterns. Benchmarks of four cell phase inference methods and three rhythmic gene analysis methods revealed differences in phase accuracy, rhythmicity detection, and parameter recovery. scCircaSim supports reproducible method evaluation and preliminary experimental planning. Availability and Implementation: scCircaSim is implemented in R and available at https://github.com/Liwei-DynamicsLab/scCircaSim.

bioRxiv (Cold Spring Harbor Laboratory)
Affiliated Hospital of Qingdao University (CN)
Single-cell and spatial transcriptomics
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