ArticleNature communications2025
STANCE: a unified statistical model to detect cell-type-specific spatially variable genes in spatial transcriptomics.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- RKMR: A Rapid Kernel Machine Regression Framework for Optimal Marker Detection in Spatial Omics Data.bioRxiv : the preprint server for biology · 2026Article
- Computational analysis in spatial transcriptomics: methods and perspectives.Briefings in bioinformatics · 2026Review
- Benchmarking cell-type-specific spatially variable gene detection methods.Briefings in bioinformatics · 2026Article
- A spatially informed matrix normal model for gene co-expression analysis in spatial transcriptomics studies.Nucleic acids research · 2025Article
- A unified framework for identification of cell-type-specific spatially variable genes in spatial transcriptomic studies.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- High-precision cell-type mapping and annotation of single-cell spatial transcriptomics with STAMapper.Genome biology · 2025Article
- Mapping disease critical spatially variable gene programs by integrating spatial transcriptomics with human genetics.bioRxiv : the preprint server for biology · 2025Article
- SPACE: Spatially variable gene clustering adjusting for cell type effect for improved spatial domain detection.Nucleic acids research · 2025Article
- SIGEL: a context-aware genomic representation learning framework for spatial genomics analysis.Genome biology · 2025Article
- Rotation-invariance is essential for accurate detection of spatially variable genes in spatial transcriptomics.Nature communications · 2025Article
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4 authors.
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Abstract
One of the major challenges in spatial transcriptomics is to detect spatially variable genes (SVGs), whose expression patterns are non-random across tissue locations. Many SVGs correlate with cell type compositions, introducing the concept of cell type-specific SVGs (ctSVGs). Existing ctSVG detection methods treat cell type-specific spatial effects as fixed effects, leading to tissue spatial rotation-dependent results. Moreover, SVGs may exhibit random spatial patterns within cell types, meaning an SVG is not always a ctSVG, and vice versa, further complicating detection. We propose STANCE, a unified statistical model for both SVGs and ctSVGs detection under a linear mixed-effect model framework that integrates gene expression, spatial location, and cell type composition information. STANCE ensures tissue rotation-invariant results, with a two-stage approach: initial SVG/ctSVG detection followed by ctSVG-specific testing. We demonstrate its performance through extensive simulations and analyses of public datasets. Downstream analyses reveal STANCE's potential in spatial transcriptomics analysis.
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