ArticleNucleic acids research2025
Benchmarking computational methods for detecting spatial domains and domain-specific spatially variable genes from spatial transcriptomics data.
Article in Nucleic acids research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- spMosaic: multi-sample integration for scalable spatial domain discovery based on spatial transcriptomics.Briefings in bioinformatics · 2026Article
- STcompare: comparative spatial transcriptomics data analysis of structurally matched tissues to characterize differentially spatially patterned genes.Bioinformatics (Oxford, England) · 2026Article
- DSSMST: A Deterministic State Space Model for Self-Supervised Spatial Domain Identification in Spatial Transcriptomics.Biochemical genetics · 2026Article
- Computational analysis in spatial transcriptomics: methods and perspectives.Briefings in bioinformatics · 2026Review
- Benchmarking Spatial Clustering Methods for Mass Spectrometry-Based Spatial Metabolomics.Metabolites · 2026Article
- Benchmarking computational methods for identifying and quantifying polyadenylation sites from 3' tag-based single-cell RNA-seq data.Nucleic acids research · 2026Article
- Biased multi-view contrastive learning with attentive masking for spatial transcriptomic analysis.Briefings in bioinformatics · 2026Article
- mosna Reveals Different Types of Cellular Interactions Predictive of Response to Immunotherapies and Survival in Cancer.Molecular & cellular proteomics : MCP · 2026Article
- SpatialQuery: scalable discovery and molecular characterization of multicellular motifs from spatial omics data.bioRxiv : the preprint server for biology · 2026Article
- Spatial instruction of tissue immunity.ImmunoHorizons · 2026Review
- The Evolution of Spatial Omics Technologies Introduces A Novel Avenue for Lung Cancer Research.Genomics, proteomics & bioinformatics · 2026Review
- ChatSpatial: Schema-Enforced Agentic Orchestration for Reproducible and Cross-Platform Spatial Transcriptomics.bioRxiv : the preprint server for biology · 2026Article
- Benchmarking cell-type-specific spatially variable gene detection methods.Briefings in bioinformatics · 2026Article
- GALA: a unified landmark-free framework for coarse-to-fine spatial alignment across resolutions and modalities in spatial transcriptomics.Briefings in bioinformatics · 2026Article
- Spatiotemporal transcriptomic mapping reveals region-specific glial activation and astrocyte shifts in epileptogenesis beyond the hippocampus.Acta neuropathologica communications · 2026Article
- Cellular neighborhoods in cancer.Nature cancer · 2026Article
- SPACE: Spatially variable gene clustering adjusting for cell type effect for improved spatial domain detection.Nucleic acids research · 2025Article
- SpaICL: image-guided curriculum strategy-based graph contrastive learning for spatial transcriptomics clustering.Briefings in bioinformatics · 2025Article
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5 authors.
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Abstract
Advances in spatially resolved transcriptomics (SRT) have led to the emergence of numerous computational methods for identifying spatial domains and spatially variable genes (SVGs); however, a comprehensive assessment of existing methods is lacking. We comprehensively benchmarked 19 methods for detecting spatial domains and domain-specific SVGs from SRT data, using 30 real-world datasets covering six SRT technologies and 27 synthetic datasets. We first evaluated the performance of these methods on spatial domain identification in terms of accuracy, stability, generalizability, and scalability. Results reveal that there is no single method that works best for all datasets, and the optimal method depends on the data, especially the SRT platform. Further, we proposed a quantitative strategy to evaluate domain-specific SVG recognition results and assessed the impact of spatial domains on SVG detection. We found that SVG detection based on spatial domains identified by different GNN methods have high accuracy but low concordance. Generally, the more accurate the recognized spatial domains, the higher the number and accuracy of domain-specific SVGs detected. Moreover, integrating spatial clustering results from different methods can lead to more robust and better clustering and SVG results. Practical guidelines were provided for choosing appropriate methods for spatial domain and domain-specific SVG identification.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.