ReviewNature communications2025
Categorization of 34 computational methods to detect spatially variable genes from spatially resolved transcriptomics data.
Review in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- Unveiling the role of spatial transcriptomics in the analysis of the tumor immune microenvironment (Review).International journal of molecular medicine · 2026Review
- RKMR: A Rapid Kernel Machine Regression Framework for Optimal Marker Detection in Spatial Omics Data.bioRxiv : the preprint server for biology · 2026Article
- spMosaic: multi-sample integration for scalable spatial domain discovery based on spatial transcriptomics.Briefings in bioinformatics · 2026Article
- Accurate prediction in reconstructed spatial transcriptomes does not ensure valid biological discovery.bioRxiv : the preprint server for biology · 2026Article
- Interpretable and scalable spatial gene set activity analysis with GESSO uncovers functional tissue architecture.bioRxiv : the preprint server for biology · 2026Article
- Investigating Spatial Dynamics in Spatial Omics Data with StarTrail.Journal of the American Statistical Association · 2026Article
- Computational analysis in spatial transcriptomics: methods and perspectives.Briefings in bioinformatics · 2026Review
- BatchSVG: identifying batch-biased genes in the application of spatially variable gene detection.Bioinformatics (Oxford, England) · 2026Article
- Flexible and scalable inference of spatially varying correlation in spatial transcriptomics with spCorr.Genome research · 2026Article
- Spatial Gene Set Enrichment Analysis with Applications to Spatially Resolved Transcriptomic Data.bioRxiv : the preprint server for biology · 2026Article
- Review
- MLN2SVG: domain-aware spatially variable gene detection using contrastive variational autoencoder and multi-level neighbor search.Briefings in bioinformatics · 2026Article
- SpatialQuery: scalable discovery and molecular characterization of multicellular motifs from spatial omics data.bioRxiv : the preprint server for biology · 2026Article
- HarveST uses a heterogeneous graph learning framework to reveal spatial transcriptomics patterns.Communications biology · 2026Article
- Exploring the human brain: spatial transcriptomics challenges and approaches in post-mortem analysis.Brain : a journal of neurology · 2026Review
- Benchmarking cell-type-specific spatially variable gene detection methods.Briefings in bioinformatics · 2026Article
- DuaST: an integrated deep learning framework for spatial transcriptomics with cross-branch interaction.Briefings in bioinformatics · 2026Article
- A Multimodal Single-Cell Epigenomic and 3D Genome Atlas of the Human Basal Ganglia.bioRxiv : the preprint server for biology · 2026Article
- SpaceBF: spatial coexpression analysis using Bayesian fused approaches in spatial omics datasets.GigaScience · 2026Article
- Mapping safety in space: the emerging role of spatial transcriptomics in safe drug development.Frontiers in toxicology · 2026Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
In the analysis of spatially resolved transcriptomics data, detecting spatially variable genes (SVGs) is crucial. Numerous computational methods exist, but varying SVG definitions and methodologies lead to incomparable results. We review 34 state-of-the-art methods, classifying SVGs into three categories: overall, cell-type-specific, and spatial-domain-marker SVGs. Our review explains the intuitions underlying these methods, summarizes their applications, and categorizes the hypothesis tests they use in the trade-off between generality and specificity for SVG detection. We discuss challenges in SVG detection and propose future directions for improvement. Our review offers insights for method developers and users, advocating for category-specific benchmarking.
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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.