Circular Data Analysis for Spatial Omics
- 1The Ohio State University
Abstract
Many biological quantities in omics are inherently periodic or directional, including circadian phase, cell-cycle position, and cellular orientation. Treating such quantities as ordinary linear variables can introduce artificial discontinuities and obscure biological interpretation. Circular methods are well developed across directional statistics, bioinformatics, and spatial statistics, but practical guidance for omics applications remains limited. We present key considerations for circular data analysis in single-cell and spatial omics, including the provenance and observational unit of the circular quantity, the inferential or predictive objective, the distinction between tissue composition and residual spatial dependence, and the choice of appropriate models. We describe relevant approaches for circular representation, inference, clustering, regression, machine learning, and spatial analysis, together with representative R software. Their practical use is illustrated through spatial transcriptomic case studies of derived circadian peak phase in a mouse model of Alzheimer's disease and latent cell-cycle position in human ovarian cancer. These examples demonstrate how the definition of the angle, its observational unit, and tissue heterogeneity shape appropriate analysis and interpretation, including when spatial modeling is not warranted. We conclude with practical recommendations and directions for future methodological development.
Related
- Spatial multi-omics reveals novel vascular adipocyte and B cell biology in human abdominal aortic aneurysm
- Integrating multi-omics analysis reveals shared genetic architecture and spatial correlations between heart failure and osteoporosis
- Scale-resolved correlation functions unify spatial omics across platforms and resolutions
- REZCUE: Rescuing 3D spatial omics from archived FFPE tissue
- SPaRTA: Spatial Reasoning in Text-to-SQL for Omics Analysis
BibTeX
@misc{shin2026circular,
title = {{Circular Data Analysis for Spatial Omics}},
author = {J. Shin and J. Yoo and Y. Cho and A. Gupta and P.-H. Mao and K. Thakkar and T. A. Eddy and K. J. Chung and J. Kim and H. Jeon and J. Xie and D. Chung},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.29.755467},
url = {https://www.biorxiv.org/content/10.64898/2026.09.29.755467v1},
note = {CC BY-ND},
}
RIS
TY - UNPB
TI - Circular Data Analysis for Spatial Omics
AU - J. Shin
AU - J. Yoo
AU - Y. Cho
AU - A. Gupta
AU - P.-H. Mao
AU - K. Thakkar
AU - T. A. Eddy
AU - K. J. Chung
AU - J. Kim
AU - H. Jeon
AU - J. Xie
AU - D. Chung
PY - 2026
DA - 2026/09/29
PB - bioRxiv
DO - 10.64898/2026.09.29.755467
UR - https://www.biorxiv.org/content/10.64898/2026.09.29.755467v1
AB - Many biological quantities in omics are inherently periodic or directional, including circadian phase, cell-cycle position, and cellular orientation. Treating such quantities as ordinary linear variables can introduce artificial discontinuities and obscure biological interpretation. Circular methods are well developed across directional statistics, bioinformatics, and spatial statistics, but practical guidance for omics applications remains limited. We present key considerations for circular data analysis in single-cell and spatial omics, including the provenance and observational unit of the circular quantity, the inferential or predictive objective, the distinction between tissue composition and residual spatial dependence, and the choice of appropriate models. We describe relevant approaches for circular representation, inference, clustering, regression, machine learning, and spatial analysis, together with representative R software. Their practical use is illustrated through spatial transcriptomic case studies of derived circadian peak phase in a mouse model of Alzheimer's disease and latent cell-cycle position in human ovarian cancer. These examples demonstrate how the definition of the angle, its observational unit, and tissue heterogeneity shape appropriate analysis and interpretation, including when spatial modeling is not warranted. We conclude with practical recommendations and directions for future methodological development.
ER -