Categorization of 33 computational methods to detect spatially variable genes from spatially resolved transcriptomics data
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909333426536448 |
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| author | Yan, Guanao Hua, Shuo Harper Li, Jingyi Jessica |
| author_facet | Yan, Guanao Hua, Shuo Harper Li, Jingyi Jessica |
| contents | 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 33 state-of-the-art methods, categorizing SVGs into three types: 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 | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_18779 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Categorization of 33 computational methods to detect spatially variable genes from spatially resolved transcriptomics data Yan, Guanao Hua, Shuo Harper Li, Jingyi Jessica Quantitative Methods Applications 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 33 state-of-the-art methods, categorizing SVGs into three types: 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. |
| title | Categorization of 33 computational methods to detect spatially variable genes from spatially resolved transcriptomics data |
| topic | Quantitative Methods Applications |
| url | https://arxiv.org/abs/2405.18779 |