ArticleNature communications2025
Characterizing cell-type spatial relationships across length scales in spatially resolved omics data.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Multimodal spatial omics: From data acquisition to computational integration.Patterns (New York, N.Y.) · 2026Review
- Spatial transcriptomics identifies immune-stromal niches associated with cancer in adult dermatomyositis.Nature communications · 2026Article
- Spatially resolved single-cell analyses of human meningioma identify novel cell states influencing tumor microenvironment and progression.Nature genetics · 2026Article
- Spatiotemporal mapping of CD112 unveils its dichotomous role in T-cell exhaustion and ferroptosis crosstalk in cervical cancer.Cell death & disease · 2026Article
- Cell Type Populations for 3D Anatomical Structures of the Human Reference Atlas.Scientific data · 2026Article
- Basophil-specific deletion of interleukin 15 impairs memory CD8Frontiers in immunology · 2026Article
- Kontextual reframes analysis of spatial omics data and reveals consistent cell relationships across images.Cell reports methods · 2025Article
- Challenges and Opportunities in Analyzing Cancer-Associated Microbiomes.Cancer research · 2025Review
- Exploring endothelial cell environments across organs in spatially resolved omics data.bioRxiv : the preprint server for biology · 2025Article
- Generalized cell phenotyping for spatial proteomics with language-informed vision models.bioRxiv : the preprint server for biology · 2025Article
- Constructing and Using Cell Type Populations of the Human Reference Atlas.bioRxiv : the preprint server for biology · 2025Article
- Spatial Transcriptomics Identifies Immune-Stromal Niches Associated with Cancer in Adult Dermatomyositis.bioRxiv : the preprint server for biology · 2025Article
- Characterizing cell-type spatial relationships across length scales in spatially resolved omics data.Nature communications · 2025Article
- SEraster: a rasterization preprocessing framework for scalable spatial omics data analysis.Bioinformatics (Oxford, England) · 2024Article
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10 authors.
Funding
Abstract
Spatially resolved omics (SRO) technologies enable the identification of cell types while preserving their organization within tissues. Application of such technologies offers the opportunity to delineate cell-type spatial relationships, particularly across different length scales, and enhance our understanding of tissue organization and function. To quantify such multi-scale cell-type spatial relationships, we present CRAWDAD, Cell-type Relationship Analysis Workflow Done Across Distances, as an open-source R package. To demonstrate the utility of such multi-scale characterization, recapitulate expected cell-type spatial relationships, and evaluate against other cell-type spatial analyses, we apply CRAWDAD to various simulated and real SRO datasets of diverse tissues assayed by diverse SRO technologies. We further demonstrate how such multi-scale characterization enabled by CRAWDAD can be used to compare cell-type spatial relationships across multiple samples. Finally, we apply CRAWDAD to SRO datasets of the human spleen to identify consistent as well as patient and sample-specific cell-type spatial relationships. In general, we anticipate such multi-scale analysis of SRO data enabled by CRAWDAD will provide useful quantitative metrics to facilitate the identification, characterization, and comparison of cell-type spatial relationships across axes of interest.
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