ArticleGenome biology2023
Disparities in spatially variable gene calling highlight the need for benchmarking spatial transcriptomics methods.
Article in Genome biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Topological Data Analysis for Unsupervised Feature Selection in Large Scale Spatial Omics Data Sets.Bulletin of mathematical biology · 2026Article
- Castl: robust identification of spatially variable genes in spatial transcriptomics via an ensemble-based framework.Briefings in bioinformatics · 2026Article
- Benchmarking cell-type-specific spatially variable gene detection methods.Briefings in bioinformatics · 2026Article
- PreTSA: computationally efficient modeling of temporal and spatial gene expression patterns.Genome biology · 2026Article
- JOINT IDENTIFICATION OF SPATIALLY VARIABLE GENES VIA A NETWORK-ASSISTED BAYESIAN REGULARIZATION APPROACH.The annals of applied statistics · 2025Article
- Systematic benchmarking of computational methods to identify spatially variable genes.Genome biology · 2025Article
- Benchmarking computational methods for detecting spatial domains and domain-specific spatially variable genes from spatial transcriptomics data.Nucleic acids research · 2025Article
- Quantifying and interpreting biologically meaningful spatial signatures within tumor microenvironments.NPJ precision oncology · 2025Review
- 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
- Descart: a method for detecting spatial chromatin accessibility patterns with inter-cellular correlations.Genome biology · 2024Article
- Article
- HEARTSVG: a fast and accurate method for identifying spatially variable genes in large-scale spatial transcriptomics.Nature communications · 2024Article
- Cellular neighborhood analysis in spatial omics reveals new tissue domains and cell subtypes.Nature genetics · 2024Article
- Adipose tissue macrophage heterogeneity in the single-cell genomics era.Molecules and cells · 2024Review
- Deep learning in spatially resolved transcriptfomics: a comprehensive technical viewBriefings in bioinformatics · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identifying spatially variable genes (SVGs) is a key step in the analysis of spatially resolved transcriptomics data. SVGs provide biological insights by defining transcriptomic differences within tissues, which was previously unachievable using RNA-sequencing technologies. However, the increasing number of published tools designed to define SVG sets currently lack benchmarking methods to accurately assess performance. This study compares results of 6 purpose-built packages for SVG identification across 9 public and 5 simulated datasets and highlights discrepancies between results. Additional tools for generation of simulated data and development of benchmarking methods are required to improve methods for identifying SVGs.
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