ArticleNature communications2022
Identification of spatially variable genes with graph cuts.
Article in Nature communications, 2022. 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.
- MLN2SVG: domain-aware spatially variable gene detection using contrastive variational autoencoder and multi-level neighbor search.Briefings in bioinformatics · 2026Article
- Benchmarking cell-type-specific spatially variable gene detection methods.Briefings in bioinformatics · 2026Article
- Castl: robust identification of spatially variable genes in spatial transcriptomics via an ensemble-based framework.Briefings in bioinformatics · 2026Article
- The future of mathematical oncology in the age of AI.NPJ systems biology and applications · 2026Review
- SpaceBF: spatial coexpression analysis using Bayesian fused approaches in spatial omics datasets.GigaScience · 2026Article
- AI-Based Prediction of Gene Expression in Single-Cell and Multiscale Genomics and Transcriptomics.International journal of molecular sciences · 2026Review
- Spider: a flexible and unified framework for simulating spatial transcriptomics data.Bioinformatics (Oxford, England) · 2026Article
- Single-Cell Sequencing Unravels Pancreatic Cancer: Novel Technologies Reveal Novel Aspects of Cellular Heterogeneity and Inform Therapeutic Strategies.Biomedicines · 2025Review
- Antibody-oligonucleotide conjugates for spatial proteomics: principles, applications, and challenges.Acta biochimica et biophysica Sinica · 2025Article
- Systematic benchmarking of computational methods to identify spatially variable genes.Genome biology · 2025Article
- Finding spatially variable ligand-receptor interactions with functional support from downstream genes.Nature communications · 2025Article
- Prioritizing perturbation-responsive gene patterns using interpretable deep learning.Nature communications · 2025Article
- A unified analysis of atlas single-cell data.Genome research · 2025Article
- InSituCor: exploring spatially correlated genes conditional on the cell type landscape.Genome biology · 2025Article
- Benchmarking algorithms for spatially variable gene identification in spatial transcriptomics.Bioinformatics (Oxford, England) · 2025Article
- STMiner: Gene-centric spatial transcriptomics for deciphering tumor tissues.Cell genomics · 2025Article
- Categorization of 34 computational methods to detect spatially variable genes from spatially resolved transcriptomics data.Nature communications · 2025Review
- Multimodal integration strategies for clinical application in oncology.Frontiers in pharmacology · 2025Review
- Single-cell omics: experimental workflow, data analyses and applications.Science China. Life sciences · 2025Review
- Descart: a method for detecting spatial chromatin accessibility patterns with inter-cellular correlations.Genome biology · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Single-cell gene expression data with positional information is critical to dissect mechanisms and architectures of multicellular organisms, but the potential is limited by the scalability of current data analysis strategies. Here, we present scGCO, a method based on fast optimization of hidden Markov Random Fields with graph cuts to identify spatially variable genes. Comparing to existing methods, scGCO delivers a superior performance with lower false positive rate and improved specificity, while demonstrates a more robust performance in the presence of noises. Critically, scGCO scales near linearly with inputs and demonstrates orders of magnitude better running time and memory requirement than existing methods, and could represent a valuable solution when spatial transcriptomics data grows into millions of data points and beyond.
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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.