ArticleNature communications2023
Dimension-agnostic and granularity-based spatially variable gene identification using BSP.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed, 31 citations in OpenAlex.
- Computational analysis in spatial transcriptomics: methods and perspectives.Briefings in bioinformatics · 2026Review
- The interpretable multimodal dimension reduction framework SpaHDmap enhances resolution in spatial transcriptomics.Nature cell biology · 2026Article
- scBSP: a fast and accurate tool for identifying spatially variable features from high-resolution spatial omics data.Bioinformatics (Oxford, England) · 2025Article
- Mapping disease critical spatially variable gene programs by integrating spatial transcriptomics with human genetics.bioRxiv : the preprint server for biology · 2025Article
- FUSION: a web-based application for in-depth exploration of multi-omics data with brightfield histology.Nature communications · 2025Article
- BISON: bi-clustering of spatial omics data with feature selection.Bioinformatics (Oxford, England) · 2025Article
- Rotation-invariance is essential for accurate detection of spatially variable genes in spatial transcriptomics.Nature communications · 2025Article
- Prioritizing perturbation-responsive gene patterns using interpretable deep learning.Nature communications · 2025Article
- Relation equivariant graph neural networks to explore the mosaic-like tissue architecture of kidney diseases on spatially resolved transcriptomics.Bioinformatics (Oxford, England) · 2025Article
- Unravelling tumour spatiotemporal heterogeneity using spatial multimodal data.Clinical and translational medicine · 2025Review
- Benchmarking algorithms for spatially variable gene identification in spatial transcriptomics.Bioinformatics (Oxford, England) · 2025Article
- scBSP: A fast and accurate tool for identifying spatially variable features from high-resolution spatial omics data.bioRxiv : the preprint server for biology · 2025Article
- Categorization of 34 computational methods to detect spatially variable genes from spatially resolved transcriptomics data.Nature communications · 2025Review
- Spatial Transcriptomics: Integrating Morphology and Molecular Mechanisms of Kidney Diseases.The American journal of pathology · 2025Review
- Recent advances in spatially variable gene detection in spatial transcriptomics.Computational and structural biotechnology journal · 2024Review
- Computational methods for alignment and integration of spatially resolved transcriptomics data.Computational and structural biotechnology journal · 2024Review
- GAADE: identification spatially variable genes based on adaptive graph attention network.Briefings in bioinformatics · 2024Article
- Chrysalis: decoding tissue compartments in spatial transcriptomics with archetypal analysis.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
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Authors and funding
10 authors at 4 institutions in 1 country.
Funding
Abstract
Identifying spatially variable genes (SVGs) is critical in linking molecular cell functions with tissue phenotypes. Spatially resolved transcriptomics captures cellular-level gene expression with corresponding spatial coordinates in two or three dimensions and can be used to infer SVGs effectively. However, current computational methods may not achieve reliable results and often cannot handle three-dimensional spatial transcriptomic data. Here we introduce BSP (big-small patch), a non-parametric model by comparing gene expression pattens at two spatial granularities to identify SVGs from two or three-dimensional spatial transcriptomics data in a fast and robust manner. This method has been extensively tested in simulations, demonstrating superior accuracy, robustness, and high efficiency. BSP is further validated by substantiated biological discoveries in cancer, neural science, rheumatoid arthritis, and kidney studies with various types of spatial transcriptomics technologies.
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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.