Hierarchical distribution matching enables comprehensive characterization of common and condition-specific cell niches in spatial omics data
- 1The Hong Kong University of Science and Technology
Abstract
Deciphering cell niches in complex tissues is essential for understanding tissue structure and disease. Recent advances in spatial omics have enabled subcellular resolution and accurate cell identity mapping. However, robust delineation of cell niches and disease-associated spatial patterns remains difficult. We introduce Harmonics, a novel computational framework that systematically identifies both common and condition-specific cell niches from spatial omics data through hierarchical distribution matching. Harmonics also includes a suite of downstream modules that facilitate comprehensive niche characterization. We demonstrate its scalability, accuracy and generalizability across datasets spanning diverse species, tissues, diseases, spatial modalities, and technological platforms. For condition-agnostic datasets, Harmonics outperforms baseline methods in both accuracy and robustness, and further demonstrates the capability to resolve niche structures at finer granularity. Across diverse diseases including pulmonary fibrosis, triple-negative breast cancer, and colorectal cancer, Harmonics enables precise identification of condition-specific niches and reveals disease-associated dynamics, subtype-specific spatial patterns, and structured immune architectures. We envision Harmonics as a practical and versatile tool for spatial niche analysis that can be applied across a wide range of biological contexts and seamlessly integrated with existing spatial omics workflows.
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BibTeX
@misc{liu2026hierarchical,
title = {{Hierarchical distribution matching enables comprehensive characterization of common and condition-specific cell niches in spatial omics data}},
author = {Y. Liu and J. Xiao and X. Ma and X. Wan and P. Jiang and Z. Wang and Y. Chen and C. Yang},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.12.758505},
url = {https://www.biorxiv.org/content/10.64898/2026.10.12.758505v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Hierarchical distribution matching enables comprehensive characterization of common and condition-specific cell niches in spatial omics data
AU - Y. Liu
AU - J. Xiao
AU - X. Ma
AU - X. Wan
AU - P. Jiang
AU - Z. Wang
AU - Y. Chen
AU - C. Yang
PY - 2026
DA - 2026/10/12
PB - bioRxiv
DO - 10.64898/2026.10.12.758505
UR - https://www.biorxiv.org/content/10.64898/2026.10.12.758505v1
AB - Deciphering cell niches in complex tissues is essential for understanding tissue structure and disease. Recent advances in spatial omics have enabled subcellular resolution and accurate cell identity mapping. However, robust delineation of cell niches and disease-associated spatial patterns remains difficult. We introduce Harmonics, a novel computational framework that systematically identifies both common and condition-specific cell niches from spatial omics data through hierarchical distribution matching. Harmonics also includes a suite of downstream modules that facilitate comprehensive niche characterization. We demonstrate its scalability, accuracy and generalizability across datasets spanning diverse species, tissues, diseases, spatial modalities, and technological platforms. For condition-agnostic datasets, Harmonics outperforms baseline methods in both accuracy and robustness, and further demonstrates the capability to resolve niche structures at finer granularity. Across diverse diseases including pulmonary fibrosis, triple-negative breast cancer, and colorectal cancer, Harmonics enables precise identification of condition-specific niches and reveals disease-associated dynamics, subtype-specific spatial patterns, and structured immune architectures. We envision Harmonics as a practical and versatile tool for spatial niche analysis that can be applied across a wide range of biological contexts and seamlessly integrated with existing spatial omics workflows.
ER -