ArticleNature communications2023
SONAR enables cell type deconvolution with spatially weighted Poisson-Gamma model for spatial transcriptomics.
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 33 papers.
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33 citing papers in PubMed, 46 citations in OpenAlex.
- Unlocking the full potential of spatial omics in plants: practical challenges, solutions, and a path forward.The Plant cell · 2026Review
- SlotDeconv: spatial transcriptomics deconvolution via diversity-constrained prototype learning and spatial refinement.Bioinformatics (Oxford, England) · 2026Article
- STAID: A Self-Refining Deep Learning Framework for Spatial Cell-Type Deconvolution with Biologically Informed Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- RETROFIT: Reference-free deconvolution of cell-type mixtures in spatial transcriptomics.Nature communications · 2026Article
- Spatially informed reference-free cell-type deconvolution for spatial transcriptomics with SpatialCD.Genome research · 2026Article
- T-cell distribution in the dorsal root ganglion across species, sex, and age.bioRxiv : the preprint server for biology · 2026Article
- Logistic regression for estimating functional effects with spatial transcriptomics.Nucleic acids research · 2026Article
- Estimating tumour immune infiltration: methodological convergence across histology and spatial technologies.Briefings in bioinformatics · 2026Review
- RESCUE: recovery of unattributed expression patterns in spatial transcriptomics.Nature communications · 2026Article
- DANST enables cell-type deconvolution in spatial transcriptomics using deep domain adversarial neural networks.Communications biology · 2026Article
- Discovering paracrine regulators of cell type composition from spatial transcriptomics using SPER.Bioinformatics advances · 2026Article
- JOINT IDENTIFICATION OF SPATIALLY VARIABLE GENES VIA A NETWORK-ASSISTED BAYESIAN REGULARIZATION APPROACH.The annals of applied statistics · 2025Article
- Cell-type deconvolution methods for spatial transcriptomics.Nature reviews. Genetics · 2025Review
- Inference of cell-type composition and single-cell spatial maps from spatial transcriptomics data with SWOT.Communications biology · 2025Article
- SURF: A Self-Supervised Deep Learning Method for Reference-Free Deconvolution in Spatial Transcriptomics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- gwSPADE: gene frequency-weighted reference-free deconvolution in spatial transcriptomics.Nucleic acids research · 2025Article
- High-resolution mapping of single cells in spatial context.Nature communications · 2025Article
- Spatial transcriptomics deconvolution methods generalize well to spatial chromatin accessibility data.Bioinformatics (Oxford, England) · 2025Article
- Nageotte nodules in human dorsal root ganglia reveal neurodegeneration in diabetic peripheral neuropathy.Nature communications · 2025Article
- Robust Spatial Cell-Type Deconvolution with Qualitative Reference for Spatial Transcriptomics.Small methods · 2025Article
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent advancements in spatial transcriptomic technologies have enabled the measurement of whole transcriptome profiles with preserved spatial context. However, limited by spatial resolution, the measured expressions at each spot are often from a mixture of multiple cells. Computational deconvolution methods designed for spatial transcriptomic data rarely make use of the valuable spatial information as well as the neighboring similarity information. Here, we propose SONAR, a Spatially weighted pOissoN-gAmma Regression model for cell-type deconvolution with spatial transcriptomic data. SONAR directly models the raw counts of spatial transcriptomic data and applies a geographically weighted regression framework that incorporates neighboring information to enhance local estimation of regional cell type composition. In addition, SONAR applies an additional elastic weighting step to adaptively filter dissimilar neighbors, which effectively prevents the introduction of local estimation bias in transition regions with sharp boundaries. We demonstrate the performance of SONAR over other state-of-the-art methods on synthetic data with various spatial patterns. We find that SONAR can accurately map region-specific cell types in real spatial transcriptomic data including mouse brain, human heart and human pancreatic ductal adenocarcinoma. We further show that SONAR can reveal the detailed distributions and fine-grained co-localization of immune cells within the microenvironment at the tumor-normal tissue margin in human liver cancer.
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