Publication Date
11-1-2025
Journal
Nature Cell Biology
DOI
10.1038/s41556-025-01781-z
PMID
41083603
PMCID
PMC12611760
PubMedCentral® Posted Date
10-13-2025
PubMedCentral® Full Text Version
Post-print
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).
Keywords
Animals, Transcriptome, Gene Expression Profiling, Mice, Humans, Algorithms, Lung Neoplasms, Single-Cell Analysis, Gene Expression Regulation, Neoplastic, Databases, Genetic, Computational Biology, Bioinformatics, Cancer models, Computational biology and bioinformatics
Published Open-Access
yes
Recommended Citation
Aminu, Muhammad; Zhu, Bo; Vokes, Natalie; et al., "CoCo-St Detects Global and Local Biological Structures in Spatial Transcriptomics Datasets" (2025). Faculty, Staff and Students Publications. 6227.
https://digitalcommons.library.tmc.edu/baylor_docs/6227