ArticleNature communications2025
Statistical identification of 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 25 papers.
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Who cites it
25 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
- Halo: a pretrained model for whole-cell segmentation from nuclei images in spatial transcriptomics.Briefings in bioinformatics · 2026Article
- Computational analysis in spatial transcriptomics: methods and perspectives.Briefings in bioinformatics · 2026Review
- Biased multi-view contrastive learning with attentive masking for spatial transcriptomic analysis.Briefings in bioinformatics · 2026Article
- UNLOCKING MULTI-SAMPLE DIFFERENTIAL EXPRESSION FOR SPATIAL TRANSCRIPTOMICS DATA WITH TESSERA.bioRxiv : the preprint server for biology · 2026Article
- SpatialQuery: scalable discovery and molecular characterization of multicellular motifs from spatial omics data.bioRxiv : the preprint server for biology · 2026Article
- Benchmarking cell-type-specific spatially variable gene detection methods.Briefings in bioinformatics · 2026Article
- S3RL: Enhancing Spatial Single-Cell Transcriptomics With Separable Representation Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- SAGE: Spatially Aware Gene Selection and Dual-View Embedding Fusion for Domain Identification in Spatial Transcriptomics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A complete spatial map of mouse retinal ganglion cells reveals density and gene expression specializations.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- JOINT IDENTIFICATION OF SPATIALLY VARIABLE GENES VIA A NETWORK-ASSISTED BAYESIAN REGULARIZATION APPROACH.The annals of applied statistics · 2025Article
- Development and Validation of a Prognostic Model for Lung Cancer Based on Machine Learning and Immune Microenvironment Analysis.Journal of cellular and molecular medicine · 2025Article
- 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
- DiSTect: a Bayesian model for disease-associated gene discovery and prediction in spatial transcriptomics.Bioinformatics (Oxford, England) · 2025Article
- Challenges and Opportunities in Analyzing Cancer-Associated Microbiomes.Cancer research · 2025Review
- Super-Resolved Spatial Transcriptomics Reveals Early Changes in RNA Localization in the 5xFAD Hippocampus.bioRxiv : the preprint server for 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
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3 authors.
Funding
Abstract
An essential task in spatial transcriptomics is identifying spatially variable genes (SVGs). Here, we present Celina, a statistical method for systematically detecting cell type-specific SVGs (ct-SVGs)-a subset of SVGs exhibiting distinct spatial expression patterns within specific cell types. Celina utilizes a spatially varying coefficient model to accurately capture each gene's spatial expression pattern in relation to the distribution of cell types across tissue locations, ensuring effective type I error control and high power. Celina proves powerful compared to existing methods in single-cell resolution spatial transcriptomics and stands as the only effective solution for spot-resolution spatial transcriptomics. Applied to five real datasets, Celina uncovers ct-SVGs associated with tumor progression and patient survival in lung cancer, identifies metagenes with unique spatial patterns linked to cell proliferation and immune response in kidney cancer, and detects genes preferentially expressed near amyloid-β plaques in an Alzheimer's model.
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