ArticleBiomolecules2024
Accurate Identification of Spatial Domain by Incorporating Global Spatial Proximity and Local Expression Proximity.
Article in Biomolecules, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Spatial transcriptomic alignment, integration, and 3D reconstruction by STAIR.Genome biology · 2025Article
- IAMSAM: image-based analysis of molecular signatures using the Segment Anything Model.Genome biology · 2024Article
- SpatialPrompt: spatially aware scalable and accurate tool for spot deconvolution and domain identification in spatial transcriptomics.Communications biology · 2024Article
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Authors and funding
3 authors.
Funding
Abstract
Accurate identification of spatial domains is essential in the analysis of spatial transcriptomics data in order to elucidate tissue microenvironments and biological functions. However, existing methods only perform domain segmentation based on local or global spatial relationships between spots, resulting in an underutilization of spatial information. To this end, we propose SECE, a deep learning-based method that captures both local and global relationships among spots and aggregates their information using expression similarity and spatial similarity. We benchmarked SECE against eight state-of-the-art methods on six real spatial transcriptomics datasets spanning four different platforms. SECE consistently outperformed other methods in spatial domain identification accuracy. Moreover, SECE produced spatial embeddings that exhibited clearer patterns in low-dimensional visualizations and facilitated a more accurate trajectory inference.
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Registered trials
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