ArticleNature communications2023
Spatial transcriptomics deconvolution at single-cell resolution using Redeconve.
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 55 papers.
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Who cites it
55 citing papers in PubMed.
- Integrated Transcriptomic Analysis ofInternational journal of molecular sciences · 2026Article
- Review
- CODAvision: best practices and a user-friendly interface for rapid, customizable segmentation of medical images.Nature protocols · 2026Review
- Computational analysis in spatial transcriptomics: methods and perspectives.Briefings in bioinformatics · 2026Review
- Human neural organoid modeling of diffuse midline glioma captures the complexity of patient tumors.Journal of neuro-oncology · 2026Article
- DiSCO: deconvoluting spatial transcriptomics via combinatorial optimization with a foundational diffusion model.Briefings in bioinformatics · 2026Article
- CSsingle: a unified tool for robust decomposition of bulk and spatial transcriptomic data across diverse single-cell references.Nucleic acids research · 2026Article
- RESCUE: recovery of unattributed expression patterns in spatial transcriptomics.Nature communications · 2026Article
- SA2E: spatial-aware auto-encoder for cell type deconvolution of spatial transcriptomics data.Bioinformatics (Oxford, England) · 2026Article
- PD-1 blockade plus tyrosine kinase inhibitor remodels the tumor microenvironment in advanced renal cell carcinoma.Nature communications · 2026Article
- GALA: a unified landmark-free framework for coarse-to-fine spatial alignment across resolutions and modalities in spatial transcriptomics.Briefings in bioinformatics · 2026Article
- SpaJoint: a transfer learning method for spatial transcriptomics deconvolution.Briefings in bioinformatics · 2026Article
- Attention-guided enhanced deconvolution enables reference-free cell type estimation in spatial transcriptomics.Scientific reports · 2026Article
- HIDF: Integrating Tree-Structured scRNA-seq Heterogeneity for Hierarchical Deconvolution of Spatial Transcriptomics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Advances in the pathophysiological study of brain development: application of cerebral organoid combined with Spatial omics technology.Stem cell research & therapy · 2026Review
- Advances in Spatial Transcriptomics in Bone.Current osteoporosis reports · 2026Review
- CellMap: precision mapping of cellular landscape in spatial transcriptomics.Nucleic acids research · 2026Article
- A deep learning representation and spatial Bayesian cell-type deconvolution for spatial transcriptomics.PeerJ · 2026Article
- Mapping safety in space: the emerging role of spatial transcriptomics in safe drug development.Frontiers in toxicology · 2026Review
- Single-cell spatiotemporal mapping reveals a spleen-to-lung neutrophil axis in antiviral defense.Immunity & inflammation · 2026Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Computational deconvolution with single-cell RNA sequencing data as reference is pivotal to interpreting spatial transcriptomics data, but the current methods are limited to cell-type resolution. Here we present Redeconve, an algorithm to deconvolute spatial transcriptomics data at single-cell resolution, enabling interpretation of spatial transcriptomics data with thousands of nuanced cell states. We benchmark Redeconve with the state-of-the-art algorithms on diverse spatial transcriptomics platforms and datasets and demonstrate the superiority of Redeconve in terms of accuracy, resolution, robustness, and speed. Application to a human pancreatic cancer dataset reveals cancer-clone-specific T cell infiltration, and application to lymph node samples identifies differential cytotoxic T cells between IgA+ and IgG+ spots, providing novel insights into tumor immunology and the regulatory mechanisms underlying antibody class switch.
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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.