ArticleBriefings in bioinformatics2023
Benchmarking cell-type clustering methods for spatially resolved transcriptomics data.
Article in Briefings in bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
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Who cites it
43 citing papers in PubMed, 80 citations in OpenAlex.
- spMosaic: multi-sample integration for scalable spatial domain discovery based on spatial transcriptomics.Briefings in bioinformatics · 2026Article
- Beyond benchmarking: an expert-guided consensus approach to spatially aware clustering.Nature methods · 2026Article
- PromptSTG: prototype-guided prompting for few-shot spatial transcriptomics annotation.Briefings in bioinformatics · 2026Article
- RAS pathway activity subtypes identified by machine learning define prognostic and immune microenvironment characteristics in lung adenocarcinoma.Discover oncology · 2026Article
- Benchmarking Spatial Clustering Methods for Mass Spectrometry-Based Spatial Metabolomics.Metabolites · 2026Article
- ModelistsGCN: a multimodal graph convolutional network framework for single-cell spatial transcriptomic cell typing.Briefings in bioinformatics · 2026Article
- SpatialESD: Spatial Ensemble Domain Detection in Spatial Transcriptomics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- An explainable imaging-clinical biomarker for non-small cell lung cancer prognostication based on normalised hotspot to centroid distance and [European journal of nuclear medicine and molecular imaging · 2026Article
- PathCLAST: pathway-augmented contrastive learning with attention for interpretable spatial transcriptomics.Briefings in bioinformatics · 2026Article
- Spatial omics for profiling the dynamic tumor microenvironment.Clinical & translational immunology · 2026Review
- Spatial transcriptomics iterative hierarchical clustering (stIHC): A novel method for identifying spatial gene co-expression modules.Quantitative biology (Beijing, China) · 2025Article
- Cell-type deconvolution methods for spatial transcriptomics.Nature reviews. Genetics · 2025Review
- Beyond benchmarking: an expert-guided consensus approach to spatially aware clustering.bioRxiv : the preprint server for biology · 2025Article
- Spatial Transcriptomics of Adipose Tissue: Technologies, Applications, and Challenges.Journal of obesity & metabolic syndrome · 2025Review
- On metrics for subpopulation detection in single-cell and spatial omics data.Nucleic acids research · 2025Article
- ST-deconv: an accurate deconvolution approach for spatial transcriptome data utilizing self-encoding and contrastive learning.NAR genomics and bioinformatics · 2025Article
- Review
- spCLUE: a contrastive learning approach to unified spatial transcriptomics analysis across single-slice and multi-slice data.Genome biology · 2025Article
- m2ST: dual multi-scale graph clustering for spatially resolved transcriptomics.Bioinformatics (Oxford, England) · 2025Article
- Learning tissue representation by identification of persistent local patterns in spatial omics data.Nature communications · 2025Article
Corrections and comments
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Authors and funding
3 authors at 3 institutions in 1 country.
Funding
Abstract
Spatially resolved transcriptomics technologies enable the measurement of transcriptome information while retaining the spatial context at the regional, cellular or sub-cellular level. While previous computational methods have relied on gene expression information alone for clustering single-cell populations, more recent methods have begun to leverage spatial location and histology information to improve cell clustering and cell-type identification. In this study, using seven semi-synthetic datasets with real spatial locations, simulated gene expression and histology images as well as ground truth cell-type labels, we evaluate 15 clustering methods based on clustering accuracy, robustness to data variation and input parameters, computational efficiency, and software usability. Our analysis demonstrates that even though incorporating the additional spatial and histology information leads to increased accuracy in some datasets, it does not consistently improve clustering compared with using only gene expression data. Our results indicate that for the clustering of spatial transcriptomics data, there are still opportunities to enhance the overall accuracy and robustness by improving information extraction and feature selection from spatial and histology data.
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Registered trials
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.