ArticleBioinformatics (Oxford, England)2025
Benchmarking algorithms for spatially variable gene identification in spatial transcriptomics.
Article in Bioinformatics (Oxford, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- A multiperspective evaluation framework of spatial transcriptomics clustering methods.NAR genomics and bioinformatics · 2026Article
- BatchSVG: identifying batch-biased genes in the application of spatially variable gene detection.Bioinformatics (Oxford, England) · 2026Article
- Standardized metrics for assessment and reproducibility of imaging-based spatial transcriptomics datasets.Nature biotechnology · 2026Article
- HisCMCL: cross-modal contrastive learning with hierarchical multi-scale fusion for spatial expression prediction.Bioinformatics (Oxford, England) · 2026Article
- Single-Cell and Spatial Omics: Methods and Applications.MedComm · 2026Review
- The Evolution of Spatial Omics Technologies Introduces A Novel Avenue for Lung Cancer Research.Genomics, proteomics & bioinformatics · 2026Review
- Benchmarking cell-type-specific spatially variable gene detection methods.Briefings in bioinformatics · 2026Article
- DeepSpaceDB: a spatial transcriptomics atlas for interactive in-depth analysis of tissues and tissue microenvironments.Nucleic acids research · 2026Article
- Artificial Intelligence Revolution in Transcriptomics: From Single Cells to Spatial Atlases.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Addressing the mean-variance relationship in spatially resolved transcriptomics data with spoon.Biostatistics (Oxford, England) · 2025Article
- BatchSVG: identifying batch-biased genes in the application of spatially variable gene detection.bioRxiv : the preprint server for biology · 2025Article
- scBSP: a fast and accurate tool for identifying spatially variable features from high-resolution spatial omics data.Bioinformatics (Oxford, England) · 2025Article
- Addressing the mean-variance relationship in spatially resolved transcriptomics data withbioRxiv : the preprint server for biology · 2024Article
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Authors and funding
7 authors.
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Abstract
motivationThe rapid development of spatial transcriptomics has underscored the importance of identifying spatially variable genes. As a fundamental task in spatial transcriptomic data analysis, spatially variable gene identification has been extensively studied. However, the lack of comprehensive benchmark makes it difficult to validate the effectiveness of various algorithms scattered across a large number of studies with real-world datasets.
resultsIn response, this article proposes a benchmark framework to evaluate algorithms for identifying spatially variable genes through the analysis of 30 synthesized and 74 real-world datasets, aiming to identify the best algorithms and their corresponding application scenarios. This framework can assist medical and life scientists in selecting suitable algorithms for their research, while also aid bioinformatics scientists in developing more powerful and efficient computational methods in spatial transcriptomic research. AVAILABILITY AND IMPLEMENTATION: The source code of this benchmarking framework is available at both Github (https://github.com/XiDsLab/svg-benchmark) and Zenodo (https://doi.org/10.5281/zenodo.15031083). In addition, all real and synthetic datasets considered in this study are also publicly available at Zenodo (https://doi.org/10.5281/zenodo.7227771).
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