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[2602.12651] Uncovering spatial tissue domains and cell types in spatial omics through cross-scale profiling of cellular and genomic interactions
Summary
This article presents CellScape, a deep learning framework for analyzing spatial transcriptomics data, addressing the challenges of noise and complexity in existing methods.
Why It Matters
Understanding cellular interactions and genomic relationships in spatial omics is crucial for advancing biomedical research. CellScape enhances data analysis, potentially leading to breakthroughs in disease understanding and treatment by revealing spatial patterns in tissues.
Key Takeaways
- CellScape effectively integrates spatial signals with genomic data.
- The framework addresses noise and complexity in spatial transcriptomics data.
- Improves spatial domain segmentation and cellular analysis.
- Offers a versatile tool for diverse transcriptomics datasets.
- Enhances understanding of cellular identity and function in tissues.
Computer Science > Machine Learning arXiv:2602.12651 (cs) [Submitted on 13 Feb 2026] Title:Uncovering spatial tissue domains and cell types in spatial omics through cross-scale profiling of cellular and genomic interactions Authors:Rui Yan, Xiaohan Xing, Xun Wang, Zixia Zhou, Md Tauhidul Islam, Lei Xing View a PDF of the paper titled Uncovering spatial tissue domains and cell types in spatial omics through cross-scale profiling of cellular and genomic interactions, by Rui Yan and 5 other authors View PDF HTML (experimental) Abstract:Cellular identity and function are linked to both their intrinsic genomic makeup and extrinsic spatial context within the tissue microenvironment. Spatial transcriptomics (ST) offers an unprecedented opportunity to study this, providing in situ gene expression profiles at single-cell resolution and illuminating the spatial and functional organization of cells within tissues. However, a significant hurdle remains: ST data is inherently noisy, large, and structurally complex. This complexity makes it intractable for existing computational methods to effectively capture the interplay between spatial interactions and intrinsic genomic relationships, thus limiting our ability to discern critical biological patterns. Here, we present CellScape, a deep learning framework designed to overcome these limitations for high-performance ST data analysis and pattern discovery. CellScape jointly models cellular interactions in tissue space and genomic relation...