ArticleGenome biology2025
spCLUE: a contrastive learning approach to unified spatial transcriptomics analysis across single-slice and multi-slice data.
Article in Genome biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- spMosaic: multi-sample integration for scalable spatial domain discovery based on spatial transcriptomics.Briefings in bioinformatics · 2026Article
- Cross-Propagative Graph Learning Reveals Spatial Tissue Domains in Multi-Modal Spatial Transcriptomics.Small methods · 2026Article
- spAttClu: a spatial domain clustering model leveraging spatially weighted graph attention and contrastive learning.Bioinformatics (Oxford, England) · 2026Article
- Single-Cell and Spatial Omics: Methods and Applications.MedComm · 2026Review
- Article
- STransfer: a transfer learning-enhanced graph convolutional network for clustering spatial transcriptomics data.Bioinformatics (Oxford, England) · 2026Article
- spCLUE: a contrastive learning approach to unified spatial transcriptomics analysis across single-slice and multi-slice data.Genome biology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Advances in spatial transcriptomics demand new tools to integrate data across tissue slices and identify consistent spatial domains. We introduce spCLUE, a comprehensive framework combining multi-view graph network, contrastive learning, attention mechanisms, and a batch prompting module to learn informative spot representations and integrate data from both aligned and unaligned samples. spCLUE outperforms nine single-slice and seven multi-slice methods when tested on diverse datasets and reveals biologically relevant domains across different tissues and conditions. spCLUE offers a powerful solution to spatial domain analysis and integration in spatial transcriptomics, enabling more accurate and interpretable studies of tissue organization.
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What OpenQuestion holds
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.