ArticleNature communications2022
Reference-free cell type deconvolution of multi-cellular pixel-resolution spatially resolved transcriptomics data.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 147 papers.
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Who cites it
147 citing papers in PubMed.
- Inferring gene expression profiles of tumor clones.Bioinformatics (Oxford, England) · 2026Article
- CoexpressDeconvolve enables reference-free single-cell-resolution deconvolution from spot-based spatial transcriptomics.iScience · 2026Article
- WDGTA: a wavelet-denoised graph attention framework for spatial transcriptomics deconvolution.Briefings in bioinformatics · 2026Article
- Benchmarking cell type annotation in spatial transcriptomics: resolving cellular hierarchies, biological fidelity, and dynamic cell states.Research square · 2026Article
- RETROFIT: Reference-free deconvolution of cell-type mixtures in spatial transcriptomics.Nature communications · 2026Article
- Ten years of mapping gene expression in tissues.Nature · 2026Article
- Benchmarking cell type annotation in spatial transcriptomics: resolving cellular hierarchies, biological fidelity, and dynamic cell states.bioRxiv : the preprint server for biology · 2026Article
- Spatially informed reference-free cell-type deconvolution for spatial transcriptomics with SpatialCD.Genome research · 2026Article
- Spatiotemporal transcriptomic analysis during cold ischemic injury to the murine kidney reveals compartment-specific changes.Genome biology · 2026Article
- ST-LDAW: A Topic-Model and Damped Weighted Least-Squares Method for Integrative Deconvolution of Single-Cell and Spatial Transcriptomics.Interdisciplinary sciences, computational life sciences · 2026Article
- Multi-omics and artificial intelligence for precision drug discovery and potential clinical applications.Signal transduction and targeted therapy · 2026Review
- Spatiotemporal cell type deconvolution leveraging tissue structure.Research square · 2026Article
- Review
- Longitudinal multiomic and spatial transcriptomic profiling of lupus nephritis progression in a murine model.Journal of immunology (Baltimore, Md. : 1950) · 2026Article
- Gpnmb defines a phagocytic state of microglia linked to cell death in prion disease mouse model.Nature communications · 2026Article
- Review
- Estimating tumour immune infiltration: methodological convergence across histology and spatial technologies.Briefings in bioinformatics · 2026Review
- Tissue-specific fibroblast lipid cues impose the rate of epithelial cancer invasion.Nature metabolism · 2026Article
- Article
- Spatial transcriptomics of en bloc transurethral resection of bladder tumor specimens defines an invasive-front gene expression signature.NPJ precision oncology · 2026Article
87 more citing papers are in PubMed but not listed here.
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5 authors.
Funding
Abstract
Recent technological advancements have enabled spatially resolved transcriptomic profiling but at multi-cellular pixel resolution, thereby hindering the identification of cell-type-specific spatial patterns and gene expression variation. To address this challenge, we develop STdeconvolve as a reference-free approach to deconvolve underlying cell types comprising such multi-cellular pixel resolution spatial transcriptomics (ST) datasets. Using simulated as well as real ST datasets from diverse spatial transcriptomics technologies comprising a variety of spatial resolutions such as Spatial Transcriptomics, 10X Visium, DBiT-seq, and Slide-seq, we show that STdeconvolve can effectively recover cell-type transcriptional profiles and their proportional representation within pixels without reliance on external single-cell transcriptomics references. STdeconvolve provides comparable performance to existing reference-based methods when suitable single-cell references are available, as well as potentially superior performance when suitable single-cell references are not available. STdeconvolve is available as an open-source R software package with the source code available at https://github.com/JEFworks-Lab/STdeconvolve .
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