ArticleCells2023
SINFONIA: Scalable Identification of Spatially Variable Genes for Deciphering Spatial Domains.
Article in Cells, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Graph neural networks for single-cell omics data: a review of approaches and applications.Briefings in bioinformatics · 2025Pooled it
- D3aist: data-driven detection of atypical interactions in spatial transcriptomics.BMC genomics · 2026Article
- SOFisher: reinforcement learning-guided experiment designs for spatial omics.Nature communications · 2026Article
- Cross-modality representation and multi-sample integration of spatially resolved omics data.Briefings in bioinformatics · 2026Article
- MLN2SVG: domain-aware spatially variable gene detection using contrastive variational autoencoder and multi-level neighbor search.Briefings in bioinformatics · 2026Article
- DuaST: an integrated deep learning framework for spatial transcriptomics with cross-branch interaction.Briefings in bioinformatics · 2026Article
- SpaceBF: spatial coexpression analysis using Bayesian fused approaches in spatial omics datasets.GigaScience · 2026Article
- GATCL: graph attention network meets contrastive learning for spatial domain identification.Briefings in bioinformatics · 2026Article
- JOINT IDENTIFICATION OF SPATIALLY VARIABLE GENES VIA A NETWORK-ASSISTED BAYESIAN REGULARIZATION APPROACH.The annals of applied statistics · 2025Article
- Multitask benchmarking of single-cell multimodal omics integration methods.Nature methods · 2025Article
- Optimizing Xenium In Situ data utility by quality assessment and best-practice analysis workflows.Nature methods · 2025Article
- Categorization of 34 computational methods to detect spatially variable genes from spatially resolved transcriptomics data.Nature communications · 2025Review
- Homebuilt Imaging-Based Spatial Transcriptomics: Tertiary Lymphoid Structures as a Case Example.Methods in molecular biology (Clifton, N.J.) · 2025Article
- A comprehensive overview of graph neural network-based approaches to clustering for spatial transcriptomics.Computational and structural biotechnology journal · 2024Review
- Recent advances in spatially variable gene detection in spatial transcriptomics.Computational and structural biotechnology journal · 2024Review
- Chrysalis: decoding tissue compartments in spatial transcriptomics with archetypal analysis.Communications biology · 2024Article
- Enhancing spatial domain detection in spatial transcriptomics with EnSDD.Communications biology · 2024Article
- Article
- A multi-modality and multi-granularity collaborative learning framework for identifying spatial domains and spatially variable genes.Bioinformatics (Oxford, England) · 2024Article
- PROST: quantitative identification of spatially variable genes and domain detection in spatial transcriptomics.Nature communications · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent advances in spatial transcriptomics have revolutionized the understanding of tissue organization. The identification of spatially variable genes (SVGs) is an essential step for downstream spatial domain characterization. Although several methods have been proposed for identifying SVGs, inadequate ability to decipher spatial domains, poor efficiency, and insufficient interoperability with existing standard analysis workflows still impede the applications of these methods. Here we propose SINFONIA, a scalable method for identifying spatially variable genes via ensemble strategies. Implemented in Python, SINFONIA can be seamlessly integrated into existing analysis workflows. Using 15 spatial transcriptomic datasets generated with different protocols and with different sizes, dimensions and qualities, we show the advantage of SINFONIA over three baseline methods and two variants via systematic evaluation of spatial clustering, domain resolution, latent representation, spatial visualization, and computational efficiency with 21 quantitative metrics. Additionally, SINFONIA is robust relative to the choice of the number of SVGs. We anticipate SINFONIA will facilitate the analysis of spatial transcriptomics.
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