ArticleNature methods2024
Benchmarking spatial clustering methods with spatially resolved transcriptomics data.
Article in Nature methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 130 papers, 1 of them a synthesis that pooled it.
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Who cites it
130 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Society for Immunotherapy of Cancer: updates and best practices for multiplex immunohistochemistry (IHC) and immunofluorescence (IF) image analysis and data sharing.Journal for immunotherapy of cancer · 2025Guideline
- Asynchronous Evolution of Epithelium and Stroma Differentiates Precursor Lesions from Pancreatic Cancer.Cancer discovery · 2026Article
- Deep Learning-Assisted Prioritization of Candidate Drug Targets in Tumors Using Spatial Multi-Omics.Biology · 2026Review
- Beyond benchmarking: an expert-guided consensus approach to spatially aware clustering.Nature methods · 2026Article
- spMosaic: multi-sample integration for scalable spatial domain discovery based on spatial transcriptomics.Briefings in bioinformatics · 2026Article
- A multiperspective evaluation framework of spatial transcriptomics clustering methods.NAR genomics and bioinformatics · 2026Article
- Scalable, generalizable and uncertainty-aware integration of spatial multiomics across diverse modalities and platforms with SCIGMA.Nature genetics · 2026Article
- HESTIA: scalable multimodal integration of histology and high-resolution spatial Transcriptomics for robust spatial domain identification.Briefings in bioinformatics · 2026Article
- MuSpAn: a toolbox for multiscale spatial analysis.Nature communications · 2026Article
- SRLST: a unified multimodal representation learning framework for spatial transcriptomics analysis.Bioinformatics (Oxford, England) · 2026Article
- Leveraging multi-modal foundation models for analysing spatial multi-omic and histopathology data.Nature biomedical engineering · 2026Article
- Cross-Propagative Graph Learning Reveals Spatial Tissue Domains in Multi-Modal Spatial Transcriptomics.Small methods · 2026Article
- NicheTrans: spatial-aware cross-omics translation.Nature methods · 2026Article
- A systematic benchmarking framework and dual-view optimization strategy for single-cell DNA methylation imputation.Briefings in bioinformatics · 2026Article
- Scalable multi-group nonnegative spatial factorization for spatial genomics data with cell-type heterogeneity.bioRxiv : the preprint server for biology · 2026Article
- Structural-information guided fusion for spatial domain identification from spatial transcriptomics.Bioinformatics (Oxford, England) · 2026Article
- Diffusion-based representation integration for foundation models improves spatial transcriptomics analysis.Bioinformatics (Oxford, England) · 2026Article
- SPEAK: Spatial Prompting with Expert Aligned Knowledge for Tissue Domain Identification in Spatial Transcriptomics.bioRxiv : the preprint server for biology · 2026Article
- GenOT: generative optimal transport enables spatiotemporal interpolation and generation in cross-platform spatial transcriptomics.Genome biology · 2026Article
- SECTOR: structural entropy-based learning of spatiotemporal organisation in spatial transcriptomics.Bioinformatics (Oxford, England) · 2026Article
70 more citing papers are in PubMed but not listed here.
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8 authors.
Funding
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Abstract
Spatial clustering, which shares an analogy with single-cell clustering, has expanded the scope of tissue physiology studies from cell-centroid to structure-centroid with spatially resolved transcriptomics (SRT) data. Computational methods have undergone remarkable development in recent years, but a comprehensive benchmark study is still lacking. Here we present a benchmark study of 13 computational methods on 34 SRT data (7 datasets). The performance was evaluated on the basis of accuracy, spatial continuity, marker genes detection, scalability, and robustness. We found existing methods were complementary in terms of their performance and functionality, and we provide guidance for selecting appropriate methods for given scenarios. On testing additional 22 challenging datasets, we identified challenges in identifying noncontinuous spatial domains and limitations of existing methods, highlighting their inadequacies in handling recent large-scale tasks. Furthermore, with 145 simulated data, we examined the robustness of these methods against four different factors, and assessed the impact of pre- and postprocessing approaches. Our study offers a comprehensive evaluation of existing spatial clustering methods with SRT data, paving the way for future advancements in this rapidly evolving field.
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