ArticleNature communications2026
Spatial-aware detection of copy number alterations from spatial transcriptomics using SpaCNA.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Benchmarking copy number alteration inference methods for spatial transcriptomics.Nature communications · 2026Article
- Spatial-aware detection of copy number alterations from spatial transcriptomics using SpaCNA.Nature communications · 2026Article
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Authors and funding
6 authors.
Funding
Abstract
Spatial transcriptomics (ST) profiles genome-wide gene expression while preserving spatial context, yet accurate detection of copy number alterations (CNAs) in tumor ST data remains challenging. Here, we present SpaCNA, a computational framework that integrates multi-modal information of ST for robust CNA detection. SpaCNA aggregates expression from neighboring spots with similar morphological features and leverages a hidden Markov random field model incorporating spatial continuity for reliable CNA detection in ST datasets. Further, SpaCNA can reconstruct 3D CNA profiles with spatial continuity across consecutive slices when applied to 3D ST datasets. Extensive benchmarking on simulated data and real cancer datasets demonstrates SpaCNA's superior accuracy, achieving up to 0.95 F1-score in CNA detection and tumor region identification. In applications to breast cancer and colorectal cancer, SpaCNA reveals tumor boundaries and spatially distinct subclones with context-dependent interactions within the microenvironment. Notably, SpaCNA performs CNA detection in a 3D ST dataset of head and neck squamous cell carcinoma, revealing the tumor evolution trajectory of three subclones in 3D space. By providing accurate CNA inference, SpaCNA facilitates the analysis of intratumoral heterogeneity and spatial cancer biology.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.