SINFONIA: Scalable Identification of Spatially Variable Genes for Deciphering Spatial Domains
Recent advances in spatial transcriptomics have revolutionized the understanding of tissue organization. The identification of spatially variable genes (SVGs) is an essential step for downstream spatial domain characterization. Although several methods have been proposed for identifying SVGs, inadeq...
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| Format: | Article |
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MDPI AG
2023-02-01
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| Series: | Cells |
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| Online Access: | https://www.mdpi.com/2073-4409/12/4/604 |
| _version_ | 1797621813755248640 |
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| author | Rui Jiang Zhen Li Yuhang Jia Siyu Li Shengquan Chen |
| author_facet | Rui Jiang Zhen Li Yuhang Jia Siyu Li Shengquan Chen |
| author_sort | Rui Jiang |
| collection | DOAJ |
| description | Recent advances in spatial transcriptomics have revolutionized the understanding of tissue organization. The identification of spatially variable genes (SVGs) is an essential step for downstream spatial domain characterization. Although several methods have been proposed for identifying SVGs, inadequate ability to decipher spatial domains, poor efficiency, and insufficient interoperability with existing standard analysis workflows still impede the applications of these methods. Here we propose SINFONIA, a scalable method for identifying spatially variable genes via ensemble strategies. Implemented in Python, SINFONIA can be seamlessly integrated into existing analysis workflows. Using 15 spatial transcriptomic datasets generated with different protocols and with different sizes, dimensions and qualities, we show the advantage of SINFONIA over three baseline methods and two variants via systematic evaluation of spatial clustering, domain resolution, latent representation, spatial visualization, and computational efficiency with 21 quantitative metrics. Additionally, SINFONIA is robust relative to the choice of the number of SVGs. We anticipate SINFONIA will facilitate the analysis of spatial transcriptomics. |
| first_indexed | 2024-03-11T09:01:19Z |
| format | Article |
| id | doaj.art-d30b783155814defa3e175695768c848 |
| institution | Directory Open Access Journal |
| issn | 2073-4409 |
| language | English |
| last_indexed | 2024-03-11T09:01:19Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cells |
| spelling | doaj.art-d30b783155814defa3e175695768c8482023-11-16T19:44:39ZengMDPI AGCells2073-44092023-02-0112460410.3390/cells12040604SINFONIA: Scalable Identification of Spatially Variable Genes for Deciphering Spatial DomainsRui Jiang0Zhen Li1Yuhang Jia2Siyu Li3Shengquan Chen4MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST/Department of Automation, Tsinghua University, Beijing 100084, ChinaMOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST/Department of Automation, Tsinghua University, Beijing 100084, ChinaSchool of Statistics and Data Science, Nankai University, Tianjin 300071, ChinaSchool of Statistics and Data Science, Nankai University, Tianjin 300071, ChinaSchool of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, ChinaRecent advances in spatial transcriptomics have revolutionized the understanding of tissue organization. The identification of spatially variable genes (SVGs) is an essential step for downstream spatial domain characterization. Although several methods have been proposed for identifying SVGs, inadequate ability to decipher spatial domains, poor efficiency, and insufficient interoperability with existing standard analysis workflows still impede the applications of these methods. Here we propose SINFONIA, a scalable method for identifying spatially variable genes via ensemble strategies. Implemented in Python, SINFONIA can be seamlessly integrated into existing analysis workflows. Using 15 spatial transcriptomic datasets generated with different protocols and with different sizes, dimensions and qualities, we show the advantage of SINFONIA over three baseline methods and two variants via systematic evaluation of spatial clustering, domain resolution, latent representation, spatial visualization, and computational efficiency with 21 quantitative metrics. Additionally, SINFONIA is robust relative to the choice of the number of SVGs. We anticipate SINFONIA will facilitate the analysis of spatial transcriptomics.https://www.mdpi.com/2073-4409/12/4/604spatial transcriptomicsspatial autocorrelationspatially variable genesspatial domains |
| spellingShingle | Rui Jiang Zhen Li Yuhang Jia Siyu Li Shengquan Chen SINFONIA: Scalable Identification of Spatially Variable Genes for Deciphering Spatial Domains Cells spatial transcriptomics spatial autocorrelation spatially variable genes spatial domains |
| title | SINFONIA: Scalable Identification of Spatially Variable Genes for Deciphering Spatial Domains |
| title_full | SINFONIA: Scalable Identification of Spatially Variable Genes for Deciphering Spatial Domains |
| title_fullStr | SINFONIA: Scalable Identification of Spatially Variable Genes for Deciphering Spatial Domains |
| title_full_unstemmed | SINFONIA: Scalable Identification of Spatially Variable Genes for Deciphering Spatial Domains |
| title_short | SINFONIA: Scalable Identification of Spatially Variable Genes for Deciphering Spatial Domains |
| title_sort | sinfonia scalable identification of spatially variable genes for deciphering spatial domains |
| topic | spatial transcriptomics spatial autocorrelation spatially variable genes spatial domains |
| url | https://www.mdpi.com/2073-4409/12/4/604 |
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