ArticlePatterns (New York, N.Y.)2024
CellContrast: Reconstructing spatial relationships in single-cell RNA sequencing data via deep contrastive learning.
Article in Patterns (New York, N.Y.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Weighted sliced inverse regression for scalable supervised dimensionality reduction of spatial transcriptomics data.Nature communications · 2026Article
- Stepwise multi-scale reconstruction of cell spatial organization from single-cell RNA sequencing data with Cell2space.Briefings in bioinformatics · 2026Article
- Inference of spatial chromatin accessibility via integration of spatial transcriptomics and single-cell multi-omics data.Nature communications · 2026Article
- Navigating cell maps by deep learning integration of single-cell and spatially resolved transcriptomics.Briefings in bioinformatics · 2026Article
- FineST: contrastive learning integrates histology and spatial transcriptomics for nuclei-resolved ligand-receptor analysis.Nature communications · 2026Article
- stSCI: A multi-task learning framework for integrative analysis of single-cell and spatial transcriptomics data.Innovation (Cambridge (Mass.)) · 2026Article
- Reconstructing multi-scale tissue spatial architecture from single-cell RNA-seq with REMAP.bioRxiv : the preprint server for biology · 2026Article
- A Systematic Review of Contrastive Learning in Medical AI: Foundations, Biomedical Modalities, and Future Directions.Bioengineering (Basel, Switzerland) · 2026Review
- Decoding wound healing: cellular insights and technological advances.npj biomedical innovations · 2026Review
- Polyomino reconstructs spatial transcriptomic profiles with single-cell resolution via a region-allocation method.Genome research · 2025Article
- Spatial-scERA: a method for reconstructing spatial single-cell enhancer activity in multicellular organisms.Nucleic acids research · 2025Article
- CellContrast: Reconstructing spatial relationships in single-cell RNA sequencing data via deep contrastive learning.Patterns (New York, N.Y.) · 2024Article
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Authors and funding
7 authors.
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Abstract
A vast amount of single-cell RNA sequencing (SC) data have been accumulated via various studies and consortiums, but the lack of spatial information limits its analysis of complex biological activities. To bridge this gap, we introduce CellContrast, a computational method for reconstructing spatial relationships among SC cells from spatial transcriptomics (ST) reference. By adopting a contrastive learning framework and training with ST data, CellContrast projects gene expressions into a hidden space where proximate cells share similar representation values. We performed extensive benchmarking on diverse platforms, including SeqFISH, Stereo-seq, 10X Visium, and MERSCOPE, on mouse embryo and human breast cells. The results reveal that CellContrast substantially outperforms other related methods, facilitating accurate spatial reconstruction of SC. We further demonstrate CellContrast's utility by applying it to cell-type co-localization and cell-cell communication analysis with real-world SC samples, proving the recovered cell locations empower more discoveries and mitigate potential false positives.
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