Statistical identification of cell type-specific spatially variable genes in spatial transcriptomics
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 typ...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-56280-4 |
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| _version_ | 1832571526902185984 |
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| author | Lulu Shang Peijun Wu Xiang Zhou |
| author_facet | Lulu Shang Peijun Wu Xiang Zhou |
| author_sort | Lulu Shang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-8202caba33414ff99fcd7ac80ef82816 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-8202caba33414ff99fcd7ac80ef828162025-02-02T12:31:24ZengNature PortfolioNature Communications2041-17232025-01-0116112010.1038/s41467-025-56280-4Statistical identification of cell type-specific spatially variable genes in spatial transcriptomicsLulu Shang0Peijun Wu1Xiang Zhou2Department of Biostatistics, The University of Texas MD Anderson Cancer CenterDepartment of Biostatistics, University of MichiganDepartment of Biostatistics, University of MichiganAbstract 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.https://doi.org/10.1038/s41467-025-56280-4 |
| spellingShingle | Lulu Shang Peijun Wu Xiang Zhou Statistical identification of cell type-specific spatially variable genes in spatial transcriptomics Nature Communications |
| title | Statistical identification of cell type-specific spatially variable genes in spatial transcriptomics |
| title_full | Statistical identification of cell type-specific spatially variable genes in spatial transcriptomics |
| title_fullStr | Statistical identification of cell type-specific spatially variable genes in spatial transcriptomics |
| title_full_unstemmed | Statistical identification of cell type-specific spatially variable genes in spatial transcriptomics |
| title_short | Statistical identification of cell type-specific spatially variable genes in spatial transcriptomics |
| title_sort | statistical identification of cell type specific spatially variable genes in spatial transcriptomics |
| url | https://doi.org/10.1038/s41467-025-56280-4 |
| work_keys_str_mv | AT lulushang statisticalidentificationofcelltypespecificspatiallyvariablegenesinspatialtranscriptomics AT peijunwu statisticalidentificationofcelltypespecificspatiallyvariablegenesinspatialtranscriptomics AT xiangzhou statisticalidentificationofcelltypespecificspatiallyvariablegenesinspatialtranscriptomics |