ArticleNature communications2022
Advances in mixed cell deconvolution enable quantification of cell types in spatial transcriptomic 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 191 papers.
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191 citing papers in PubMed.
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- Bexmarilimab-induced macrophage activation leads to treatment benefit in solid tumors: The phase I/II first-in-human MATINS trial.Cell reports. Medicine · 2023Trial
- Spatial multi-omics revealed the impact of tumor ecosystem heterogeneity on immunotherapy efficacy in patients with advanced non-small cell lung cancer treated with bispecific antibody.Journal for immunotherapy of cancer · 2023Trial
- Benchmarking cell-type deconvolution in cross-platform transcriptomic data.Genome biology · 2026Article
- Same-Slide Spatial Multiomics Integration with IN-DEPTH Reveals Tumor Virus-Linked Spatial Reorganization of the Tumor Microenvironment.Cancer discovery · 2026Article
- AddaGCN: Spatial transcriptomics deconvolution using graph convolutional networks with adversarial discriminative domain adaptation.PLoS computational biology · 2026Article
- Heterogeneity characterization and therapeutic marker discovery of endometrial carcinoma using GeoMXT™ digital spatial profiling.Journal of translational medicine · 2026Article
- Impact of vitamin D on the colon cancer immune microenvironment: results of a randomized clinical trial of preoperative vitamin D supplementation in patients with stage I-III colon cancer.Cancer discovery · 2026Article
- Patient-derived three-dimensional lung tumor models to evaluate response to therapy.NPJ precision oncology · 2026Article
- Natural killer cell-mediated immunosurveillance modulates liver cancer evolution through cancer stemness enhancement and lipid metabolism reprogramming.Nature communications · 2026Article
- Spatiotemporal cell type deconvolution leveraging tissue structure.Research square · 2026Article
- Integrated multi-omics identifies distinct macrophage alterations during progression of metabolic dysfunction-associated steatohepatitis.Nature genetics · 2026Article
- Spatial transcriptomics reveals influence of microenvironment on intrinsic fates in melanoma therapy resistance.Genome biology · 2026Article
- Spatial characterization of skin lesions in discoid and systemic lupus erythematosus.Nature communications · 2026Article
- Human neural organoid modeling of diffuse midline glioma captures the complexity of patient tumors.Journal of neuro-oncology · 2026Article
- Estimating tumour immune infiltration: methodological convergence across histology and spatial technologies.Briefings in bioinformatics · 2026Review
- TNBC Spatial Transcriptomic Analysis across Clinical States Reveals Subtype-Specific Networks and Immunosuppressive Niches.Cancer research communications · 2026Article
- Spatial predictors of response to chemo-immunotherapy in microsatellite stable metastatic colorectal cancer.Nature communications · 2026Article
- CSsingle: a unified tool for robust decomposition of bulk and spatial transcriptomic data across diverse single-cell references.Nucleic acids research · 2026Article
131 more citing papers are in PubMed but not listed here.
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7 authors.
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Abstract
Mapping cell types across a tissue is a central concern of spatial biology, but cell type abundance is difficult to extract from spatial gene expression data. We introduce SpatialDecon, an algorithm for quantifying cell populations defined by single cell sequencing within the regions of spatial gene expression studies. SpatialDecon incorporates several advancements in gene expression deconvolution. We propose an algorithm harnessing log-normal regression and modelling background, outperforming classical least-squares methods. We compile cell profile matrices for 75 tissue types. We identify genes whose minimal expression by cancer cells makes them suitable for immune deconvolution in tumors. Using lung tumors, we create a dataset for benchmarking deconvolution methods against marker proteins. SpatialDecon is a simple and flexible tool for mapping cell types in spatial gene expression studies. It obtains cell abundance estimates that are spatially resolved, granular, and paired with highly multiplexed gene expression data.
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