CoCo-ST detects global and local biological structures in spatial transcriptomics datasets

Spatial domain detection methods often focus on high-variance structures, such as tumour-adjacent regions with sharp gene expression changes, while missing low-variance structures with subtle gene expression shifts, like those between adjacent normal and early adenoma regions. Here, to address this, we introduce 'compare and contrast spatial transcriptomics' (CoCo-ST), a graph contrastive feature representation framework. By comparing a target sample with a background sample, CoCo-ST detects both high-variance, broadly shared structures and low-variance, tissue-specific features. It offers technical advantages, including multisample integration, batch-effect correction and scalability across technologies from spot-level Visium data to single-cell Xenium Prime 5K and subcellular Visium HD data. We benchmarked CoCo-ST against ten state-of-the-art spatial-domain-detection algorithms using mouse lung precancerous samples, demonstrating its superior ability to identify low-variance spatial structures overlooked by other methods. CoCo-ST also effectively distinguishes cell clusters and niche structures in Visium HD and Xenium Prime 5K data. CoCo-ST is accessible at GitHub ( https://github.com/WuLabMDA/CoCo-ST ).

Authors

Institutions

Publication Details

Journal
Nature Cell Biology
Published
2025-10-13
DOI
https://doi.org/10.1038/s41556-025-01781-z
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

CoCo-ST detects global and local biological structures in spatial transcriptomics datasets

Jia Wu, Xiuning Le, David A. Jaffray, Don L. Gibbons et al.
Nature Cell Biology
Single-cell and spatial transcriptomics
article

CoCo-ST detects global and local biological structures in spatial transcriptomics datasets

Jia Wu, Xiuning Le, David A. Jaffray, Don L. Gibbons, Lingzhi Hong, Hong Chen, Monique B. Nilsson, John Victor Heymach, Muhammad Aminu, Tina Cascone, Alissa Poteete, Nicholas E. Navin, Yuqiu Yang, Lauren A. Byers, Mehdi Chaib, Junya Fujimoto, Chao Cheng, Natalie I. Vokes, Bo Zhu, Jianjun Zhang, Ken Chen, Tao Wang, Jianrong Li, Bo Wang
article en

Abstract

Spatial domain detection methods often focus on high-variance structures, such as tumour-adjacent regions with sharp gene expression changes, while missing low-variance structures with subtle gene expression shifts, like those between adjacent normal and early adenoma regions. Here, to address this, we introduce 'compare and contrast spatial transcriptomics' (CoCo-ST), a graph contrastive feature representation framework. By comparing a target sample with a background sample, CoCo-ST detects both high-variance, broadly shared structures and low-variance, tissue-specific features. It offers technical advantages, including multisample integration, batch-effect correction and scalability across technologies from spot-level Visium data to single-cell Xenium Prime 5K and subcellular Visium HD data. We benchmarked CoCo-ST against ten state-of-the-art spatial-domain-detection algorithms using mouse lung precancerous samples, demonstrating its superior ability to identify low-variance spatial structures overlooked by other methods. CoCo-ST also effectively distinguishes cell clusters and niche structures in Visium HD and Xenium Prime 5K data. CoCo-ST is accessible at GitHub ( https://github.com/WuLabMDA/CoCo-ST ).

Nature Cell BiologyVol. 27(11)
Hiroshima University (JP), The University of Texas MD Anderson Cancer Center (US), Baylor College of Medicine (US), University of Toronto (CA), Southwestern Medical Center (US), Hiroshima University Hospital (JP), Southwestern Medical Center (US), The University of Texas Southwestern Medical Center (US)
Openalex Percentile: Top 75%
Single-cell and spatial transcriptomics
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.