ArticleCell reports methods2026
Φ-Space ST: A platform-agnostic method to identify cell states in spatial transcriptomics studies.
Article in Cell reports methods, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Spatial Topology Reveals Biologically Distinct Recurrent Motifs in Colorectal Cancer.bioRxiv : the preprint server for biology · 2026Article
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3 authors.
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Abstract
We introduce Φ-Space ST, a platform-agnostic method to identify continuous cell states in spatial transcriptomics (ST) data using multiple scRNA-seq references. For ST with supercellular resolution, Φ-Space ST achieves interpretable cell-type deconvolution with significantly faster computation. For subcellular resolution, Φ-Space ST annotates cell states without cell segmentation, leading to highly insightful spatial niche identification. Φ-Space ST harmonizes annotations derived from multiple scRNA-seq references and provides interpretable characterizations of disease cell states by leveraging healthy references. We validate Φ-Space ST in four case studies involving CosMx, Visium, Xenium, and Stereo-seq platforms for various cancer tissues. Our method revealed niche-specific enriched cell types and distinct cell-type co-presence patterns that distinguish tumor from non-tumor tissue regions. These findings highlight the potential of Φ-Space ST as a robust and scalable tool for ST data analysis for understanding complex tissues and pathologies.
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