ArticleNature communications2023
Reconstruction of the cell pseudo-space from single-cell RNA sequencing data with scSpace.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed, 34 citations in OpenAlex.
- Stepwise multi-scale reconstruction of cell spatial organization from single-cell RNA sequencing data with Cell2space.Briefings in bioinformatics · 2026Article
- Toward simultaneous pseudo-space reconstruction and cell-type deconvolution of single-cell spatial transcriptome using SpaDicer.Cell reports methods · 2026Article
- HistoMap: Reconstructing Spatially Resolved Single-Cell Profiles from Bulk RNA-Seq to Decipher the Immune-Excluded Microenvironment in Colon Cancer.International journal of molecular sciences · 2026Article
- stSCI: A multi-task learning framework for integrative analysis of single-cell and spatial transcriptomics data.Innovation (Cambridge (Mass.)) · 2026Article
- Polyomino reconstructs spatial transcriptomic profiles with single-cell resolution via a region-allocation method.Genome research · 2025Article
- Advances and applications in single-cell and spatial genomics.Science China. Life sciences · 2025Review
- Transfer learning of multicellular organization via single-cell and spatial transcriptomics.PLoS computational biology · 2025Article
- Identification and characterization of cell niches in tissue from spatial omics data at single-cell resolution.Nature communications · 2025Article
- Elucidating cardiomyocyte heterogeneity and maturation dynamics through integrated single-cell and spatial transcriptomics.iScience · 2025Article
- Single-cell genomics and spatial transcriptomics in islet transplantation for diabetes treatment: advancing towards personalized therapies.Frontiers in immunology · 2025Review
- Spatial Transcriptomics: Integrating Morphology and Molecular Mechanisms of Kidney Diseases.The American journal of pathology · 2025Review
- Single-cell and spatial transcriptomics integration: new frontiers in tumor microenvironment and cellular communication.Frontiers in immunology · 2025Review
- Spatially Informed Graph Structure Learning Extracts Insights from Spatial Transcriptomics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Deep learning in integrating spatial transcriptomics with other modalities.Briefings in bioinformatics · 2024Review
- Recovering single-cell expression profiles from spatial transcriptomics with scResolve.Cell reports methods · 2024Article
- Spatial transcriptomics in health and disease.Nature reviews. Nephrology · 2024Review
- SpaTopic: A statistical learning framework for exploring tumor spatial architecture from spatially resolved transcriptomic data.Science advances · 2024Article
- Simulating multiple variability in spatially resolved transcriptomics with scCube.Nature communications · 2024Article
- Comprehensive single-cell RNA-seq analysis using deep interpretable generative modeling guided by biological hierarchy knowledge.Briefings in bioinformatics · 2024Article
- eMCI: An Explainable Multimodal Correlation Integration Model for Unveiling Spatial Transcriptomics and Intercellular Signaling.Research (Washington, D.C.) · 2024Article
Corrections and comments
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Authors and funding
17 authors at 7 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Tissues are highly complicated with spatial heterogeneity in gene expression. However, the cutting-edge single-cell RNA-seq technology eliminates the spatial information of individual cells, which contributes to the characterization of cell identities. Herein, we propose single-cell spatial position associated co-embeddings (scSpace), an integrative method to identify spatially variable cell subpopulations by reconstructing cells onto a pseudo-space with spatial transcriptome references (Visium, STARmap, Slide-seq, etc.). We benchmark scSpace with both simulated and biological datasets, and demonstrate that scSpace can accurately and robustly identify spatially variated cell subpopulations. When employed to reconstruct the spatial architectures of complex tissue such as the brain cortex, the small intestinal villus, the liver lobule, the kidney, the embryonic heart, and others, scSpace shows promising performance on revealing the pairwise cellular spatial association within single-cell data. The application of scSpace in melanoma and COVID-19 exhibits a broad prospect in the discovery of spatial therapeutic markers.
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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.