Evaluating spatially variable gene detection methods for spatial transcriptomics data
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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1 Online-Ressource.
- Language
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Englisch
- Bibliographic citation
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Evaluating spatially variable gene detection methods for spatial transcriptomics data ; volume:25 ; number:1 ; day:15 ; month:1 ; year:2024 ; pages:1-21 ; date:12.2024
Genome biology ; 25, Heft 1 (15.1.2024), 1-21, 12.2024
- Creator
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Chen, Carissa
Kim, Hani Jieun
Yang, Pengyi
- Contributor
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SpringerLink (Online service)
- DOI
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10.1186/s13059-023-03145-y
- URN
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urn:nbn:de:101:1-2024032621420760202658
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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14.08.2025, 11:02 AM CEST
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Associated
- Chen, Carissa
- Kim, Hani Jieun
- Yang, Pengyi
- SpringerLink (Online service)
Other Objects (12)
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PROST: quantitative identification of spatially variable genes and domain detection in spatial transcriptomics
Unsupervised spatially embedded deep representation of spatial transcriptomics
Disparities in spatially variable gene calling highlight the need for benchmarking spatial transcriptomics methods
SRTsim: spatial pattern preserving simulations for spatially resolved transcriptomics
Deciphering spatial domains from spatially resolved transcriptomics through spatially regularized deep graph networks
HEARTSVG: a fast and accurate method for identifying spatially variable genes in large-scale spatial transcriptomics
Interpretable spatially aware dimension reduction of spatial transcriptomics with STAMP
Spatially Informed Graph Structure Learning Extracts Insights from Spatial Transcriptomics
spatialLIBD: an R/Bioconductor package to visualize spatially-resolved transcriptomics data
Spatially resolved transcriptomics in immersive environments
Spatially resolved transcriptomics in immersive environments
Spatially aware dimension reduction for spatial transcriptomics
PROST: quantitative identification of spatially variable genes and domain detection in spatial transcriptomics
Unsupervised spatially embedded deep representation of spatial transcriptomics
Disparities in spatially variable gene calling highlight the need for benchmarking spatial transcriptomics methods
SRTsim: spatial pattern preserving simulations for spatially resolved transcriptomics
Deciphering spatial domains from spatially resolved transcriptomics through spatially regularized deep graph networks
HEARTSVG: a fast and accurate method for identifying spatially variable genes in large-scale spatial transcriptomics
Interpretable spatially aware dimension reduction of spatial transcriptomics with STAMP
Spatially Informed Graph Structure Learning Extracts Insights from Spatial Transcriptomics
spatialLIBD: an R/Bioconductor package to visualize spatially-resolved transcriptomics data
Spatially resolved transcriptomics in immersive environments
Spatially resolved transcriptomics in immersive environments
Spatially aware dimension reduction for spatial transcriptomics
PROST: quantitative identification of spatially variable genes and domain detection in spatial transcriptomics
Unsupervised spatially embedded deep representation of spatial transcriptomics
Disparities in spatially variable gene calling highlight the need for benchmarking spatial transcriptomics methods
SRTsim: spatial pattern preserving simulations for spatially resolved transcriptomics
Deciphering spatial domains from spatially resolved transcriptomics through spatially regularized deep graph networks
HEARTSVG: a fast and accurate method for identifying spatially variable genes in large-scale spatial transcriptomics
Interpretable spatially aware dimension reduction of spatial transcriptomics with STAMP
Spatially Informed Graph Structure Learning Extracts Insights from Spatial Transcriptomics
spatialLIBD: an R/Bioconductor package to visualize spatially-resolved transcriptomics data
Spatially resolved transcriptomics in immersive environments