ReviewComputational and structural biotechnology journal2024
A comprehensive overview of graph neural network-based approaches to clustering for spatial transcriptomics.
Review in Computational and structural biotechnology journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 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
25 citing papers in PubMed, 36 citations in OpenAlex.
- Beyond benchmarking: an expert-guided consensus approach to spatially aware clustering.Nature methods · 2026Article
- GenOT: generative optimal transport enables spatiotemporal interpolation and generation in cross-platform spatial transcriptomics.Genome biology · 2026Article
- Advancing single-cell omics and cell-based therapeutics with quantum computing.Nature reviews. Molecular cell biology · 2026Review
- SpatialESD: Spatial Ensemble Domain Detection in Spatial Transcriptomics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- UGP system: A deep learning-driven platform for automated identification of ultrafine granular powders using chromatographic fingerprinting.Journal of pharmaceutical analysis · 2026Article
- CEMUSA: a graph-based integrative metric for evaluating clusters in spatial transcriptomics.Bioinformatics (Oxford, England) · 2026Article
- SCALE: unsupervised multiscale domain identification in spatial omics data.Nucleic acids research · 2026Article
- The Heterogeneity and Function of Stromal Cells in the Tumor Microenvironment.Research (Washington, D.C.) · 2026Review
- 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
- Benchmarking computational methods for detecting spatial domains and domain-specific spatially variable genes from spatial transcriptomics data.Nucleic acids research · 2025Article
- SpaMask: Dual masking graph autoencoder with contrastive learning for spatial transcriptomics.PLoS computational biology · 2025Article
- Emerging AI approaches for cancer spatial omics.GigaScience · 2025Review
- Pan-cancer single-cell transcriptomic analysis reveals CD83 as a hallmark of tumor-associated neutrophils with senescent and pro-tumor properties.Computational and structural biotechnology journal · 2025Article
- Pruning-Assisted Modeling of Network Graph Connectivity from Spatial Transcriptomic Data.Methods in molecular biology (Clifton, N.J.) · 2025Article
- PCA-based spatial domain identification with state-of-the-art performance.Bioinformatics (Oxford, England) · 2024Article
- Comprehensive evaluation and practical guideline of gating methods for high-dimensional cytometry data: manual gating, unsupervised clustering, and auto-gating.Briefings in bioinformatics · 2024Article
- Deep learning in integrating spatial transcriptomics with other modalities.Briefings in bioinformatics · 2024Review
- Enhancing spatial domain detection in spatial transcriptomics with EnSDD.Communications biology · 2024Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Spatial transcriptomics technologies enable researchers to accurately quantify and localize messenger ribonucleic acid (mRNA) transcripts at a high resolution while preserving their spatial context. The identification of spatial domains, or the task of spatial clustering, plays a crucial role in investigating data on spatial transcriptomes. One promising approach for classifying spatial domains involves the use of graph neural networks (GNNs) by leveraging gene expressions, spatial locations, and histological images. This study provided a comprehensive overview of the most recent GNN-based methods of spatial clustering methods for the analysis of data on spatial transcriptomics. We extensively evaluated the performance of current methods on prevalent datasets of spatial transcriptomics by considering their accuracy of clustering, robustness, data stabilization, relevant requirements, computational efficiency, and memory use. To this end, we explored 60 clustering scenarios by extending the essential frameworks of spatial clustering for the selection of the GNNs, algorithms of downstream clustering, principal component analysis (PCA)-based reduction, and refined methods of correction. We comparatively analyzed the performance of the methods in terms of spatial clustering to identify their limitations and outline future directions of research in the field. Our survey yielded novel insights, and provided motivation for further investigating spatial transcriptomics.
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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.