Categorization of 34 computational methods to detect spatially variable genes from spatially resolved transcriptomics data
Abstract In the analysis of spatially resolved transcriptomics data, detecting spatially variable genes (SVGs) is crucial. Numerous computational methods exist, but varying SVG definitions and methodologies lead to incomparable results. We review 34 state-of-the-art methods, classifying SVGs into th...
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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/s41467-025-56080-w |
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| _version_ | 1832571505331929088 |
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| author | Guanao Yan Shuo Harper Hua Jingyi Jessica Li |
| author_facet | Guanao Yan Shuo Harper Hua Jingyi Jessica Li |
| author_sort | Guanao Yan |
| collection | DOAJ |
| description | Abstract In the analysis of spatially resolved transcriptomics data, detecting spatially variable genes (SVGs) is crucial. Numerous computational methods exist, but varying SVG definitions and methodologies lead to incomparable results. We review 34 state-of-the-art methods, classifying SVGs into three categories: overall, cell-type-specific, and spatial-domain-marker SVGs. Our review explains the intuitions underlying these methods, summarizes their applications, and categorizes the hypothesis tests they use in the trade-off between generality and specificity for SVG detection. We discuss challenges in SVG detection and propose future directions for improvement. Our review offers insights for method developers and users, advocating for category-specific benchmarking. |
| format | Article |
| id | doaj-art-3a1946791146450082ba1d6d98218fe5 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-3a1946791146450082ba1d6d98218fe52025-02-02T12:32:09ZengNature PortfolioNature Communications2041-17232025-01-0116112110.1038/s41467-025-56080-wCategorization of 34 computational methods to detect spatially variable genes from spatially resolved transcriptomics dataGuanao Yan0Shuo Harper Hua1Jingyi Jessica Li2Department of Statistics and Data Science, University of CaliforniaDepartment of Biomedical Data Science, Stanford UniversityDepartment of Statistics and Data Science, University of CaliforniaAbstract In the analysis of spatially resolved transcriptomics data, detecting spatially variable genes (SVGs) is crucial. Numerous computational methods exist, but varying SVG definitions and methodologies lead to incomparable results. We review 34 state-of-the-art methods, classifying SVGs into three categories: overall, cell-type-specific, and spatial-domain-marker SVGs. Our review explains the intuitions underlying these methods, summarizes their applications, and categorizes the hypothesis tests they use in the trade-off between generality and specificity for SVG detection. We discuss challenges in SVG detection and propose future directions for improvement. Our review offers insights for method developers and users, advocating for category-specific benchmarking.https://doi.org/10.1038/s41467-025-56080-w |
| spellingShingle | Guanao Yan Shuo Harper Hua Jingyi Jessica Li Categorization of 34 computational methods to detect spatially variable genes from spatially resolved transcriptomics data Nature Communications |
| title | Categorization of 34 computational methods to detect spatially variable genes from spatially resolved transcriptomics data |
| title_full | Categorization of 34 computational methods to detect spatially variable genes from spatially resolved transcriptomics data |
| title_fullStr | Categorization of 34 computational methods to detect spatially variable genes from spatially resolved transcriptomics data |
| title_full_unstemmed | Categorization of 34 computational methods to detect spatially variable genes from spatially resolved transcriptomics data |
| title_short | Categorization of 34 computational methods to detect spatially variable genes from spatially resolved transcriptomics data |
| title_sort | categorization of 34 computational methods to detect spatially variable genes from spatially resolved transcriptomics data |
| url | https://doi.org/10.1038/s41467-025-56080-w |
| work_keys_str_mv | AT guanaoyan categorizationof34computationalmethodstodetectspatiallyvariablegenesfromspatiallyresolvedtranscriptomicsdata AT shuoharperhua categorizationof34computationalmethodstodetectspatiallyvariablegenesfromspatiallyresolvedtranscriptomicsdata AT jingyijessicali categorizationof34computationalmethodstodetectspatiallyvariablegenesfromspatiallyresolvedtranscriptomicsdata |