ArticleGenome biology2023
Celloscope: a probabilistic model for marker-gene-driven cell type deconvolution in spatial transcriptomics data.
Article in Genome biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Benchmarking cell-type deconvolution in cross-platform transcriptomic data.Genome biology · 2026Article
- RETROFIT: Reference-free deconvolution of cell-type mixtures in spatial transcriptomics.Nature communications · 2026Article
- Estimating tumour immune infiltration: methodological convergence across histology and spatial technologies.Briefings in bioinformatics · 2026Review
- A guide to transcriptomic deconvolution in cancer.Nature reviews. Cancer · 2026Review
- Spatial omics at the forefront: emerging technologies, analytical innovations, and clinical applications.Cancer cell · 2026Review
- Decrypting cancer's spatial code: from single cells to tissue niches.Molecular oncology · 2025Review
- Cell-type deconvolution methods for spatial transcriptomics.Nature reviews. Genetics · 2025Review
- Spatial pattern enhanced cellular and tissue recognition for spatial transcriptomics.NAR genomics and bioinformatics · 2025Article
- STHD: probabilistic cell typing of single spots in whole transcriptome spatial data with high definition.Genome biology · 2025Article
- Integrating transcriptomics and hybrid machine learning enables high-accuracy diagnostic modeling for nasopharyngeal carcinoma.Discover oncology · 2025Article
- SMART: spatial transcriptomics deconvolution using marker-gene-assisted topic model.Genome biology · 2024Article
- Integrative spatial and genomic analysis of tumor heterogeneity with Tumoroscope.Nature communications · 2024Article
- STASCAN deciphers fine-resolution cell distribution maps in spatial transcriptomics by deep learning.Genome biology · 2024Article
- Implementing distinct spatial proteogenomic technologies: opportunities, challenges, and key considerations.Clinical and experimental immunology · 2024Review
- Computational Strategies and Algorithms for Inferring Cellular Composition of Spatial Transcriptomics Data.Genomics, proteomics & bioinformatics · 2024Review
- Pianno: a probabilistic framework automating semantic annotation for spatial transcriptomics.Nature communications · 2024Article
- Challenges and perspectives in computational deconvolution of genomics data.Nature methods · 2024Review
- StereoSiTE: a framework to spatially and quantitatively profile the cellular neighborhood organized iTME.GigaScience · 2024Article
- Spatial transcriptomics deconvolution at single-cell resolution using Redeconve.Nature communications · 2023Article
- Celloscope: a probabilistic model for marker-gene-driven cell type deconvolution in spatial transcriptomics data.Genome biology · 2023Article
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Authors and funding
12 authors.
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Abstract
Spatial transcriptomics maps gene expression across tissues, posing the challenge of determining the spatial arrangement of different cell types. However, spatial transcriptomics spots contain multiple cells. Therefore, the observed signal comes from mixtures of cells of different types. Here, we propose an innovative probabilistic model, Celloscope, that utilizes established prior knowledge on marker genes for cell type deconvolution from spatial transcriptomics data. Celloscope outperforms other methods on simulated data, successfully indicates known brain structures and spatially distinguishes between inhibitory and excitatory neuron types based in mouse brain tissue, and dissects large heterogeneity of immune infiltrate composition in prostate gland tissue.
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