ArticleBMC bioinformatics2025
Benchmarking cell type annotation methods for 10x Xenium spatial transcriptomics data.
Article in BMC bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- STAT3D as a new image-tailored Xenium-based spatial transcriptomics tool for accurately dissecting the tumor microenvironment.Nucleic acids research · 2026Article
- Analysis of multicellular anatomical structures from spatial omics data using sosta.PLoS computational biology · 2026Article
- Benchmarking cell type annotation in spatial transcriptomics: resolving cellular hierarchies, biological fidelity, and dynamic cell states.Research square · 2026Article
- Computational analysis in spatial transcriptomics: methods and perspectives.Briefings in bioinformatics · 2026Review
- Benchmarking cell type annotation in spatial transcriptomics: resolving cellular hierarchies, biological fidelity, and dynamic cell states.bioRxiv : the preprint server for biology · 2026Article
- SpatialCell AI achieves reference-free single-cell resolution from spot-based spatial transcriptomics through morphology-guided enhancement.Scientific reports · 2026Article
- SpNeigh: spatial neighborhood and differential expression analysis for high-resolution spatial transcriptomics.NAR genomics and bioinformatics · 2026Article
- Resolving sensitivity, specificity and signal contamination in Xenium spatial transcriptomics.Nature methods · 2026Article
- Spatial Multiomics Reveal Insights Into ADC Efficacy.European journal of immunology · 2026Review
- STHELAR, a multi-tissue dataset linking spatial transcriptomics and histology for cell type annotation.Scientific data · 2026Article
- Injured epithelial cell states impact kidney allograft survival after T-cell-mediated rejection.Nature communications · 2026Article
- Spatially Resolved Profiling of Compartmentalized Muscle and Brain Inflammation.European journal of immunology · 2025Review
- An integrated approach for analyzing spatially resolved multi-omics datasets from the same tissue section.Frontiers in molecular biosciences · 2025Article
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4 authors.
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Abstract
backgroundImaging-based spatial transcriptomics technologies allow us to explore spatial gene expression profiles at the cellular level. Cell type annotation of imaging-based spatial data is challenging due to the small gene panel, but it is a crucial step for downstream analyses. Many good reference-based cell type annotation tools have been developed for single-cell RNA sequencing and sequencing-based spatial transcriptomics data. However, the performance of the reference-based cell type annotation tools on imaging-based spatial transcriptomics data has not been well studied yet.
resultsWe compared performance of five reference-based methods (SingleR, Azimuth, RCTD, scPred and scmapCell) with the marker-gene-based manual annotation method on an imaging-based Xenium data of human breast cancer. A practical workflow has been demonstrated for preparing a high-quality single-cell RNA reference, evaluating the accuracy, and estimating the running time for reference-based cell type annotation tools.
conclusionsSingleR was the best performing reference-based cell type annotation tool for the Xenium platform, being fast, accurate and easy to use, with results closely matching those of manual annotation.
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