Dissecting tumor microenvironment from spatially resolved transcriptomics data by heterogeneous graph learning
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
1 Online-Ressource.
- Language
-
Englisch
- Bibliographic citation
-
Dissecting tumor microenvironment from spatially resolved transcriptomics data by heterogeneous graph learning ; volume:15 ; number:1 ; day:13 ; month:6 ; year:2024 ; pages:1-18 ; date:12.2024
Nature Communications ; 15, Heft 1 (13.6.2024), 1-18, 12.2024
- Creator
-
Zuo, Chunman
Xia, Junjie
Chen, Luonan
- Contributor
-
SpringerLink (Online service)
- DOI
-
10.1038/s41467-024-49171-7
- URN
-
urn:nbn:de:101:1-2408200855493.311647397578
- Rights
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
14.08.2025, 10:47 AM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Zuo, Chunman
- Xia, Junjie
- Chen, Luonan
- SpringerLink (Online service)
Other Objects (12)
Spatially resolved transcriptomics in immersive environments
Spatially resolved transcriptomics in immersive environments
Spatially Resolved Transcriptomics Technology Facilitates Cancer Research
Technique integration of single-cell RNA sequencing with spatially resolved transcriptomics in the tumor microenvironment
Spatially resolved transcriptomics reveals plant host responses to pathogens
SRTsim: spatial pattern preserving simulations for spatially resolved transcriptomics
Deciphering spatial domains from spatially resolved transcriptomics through spatially regularized deep graph networks
Spatially resolved transcriptomics provide a new method for cancer research
Simulating multiple variability in spatially resolved transcriptomics with scCube
Gene count normalization in single-cell imaging-based spatially resolved transcriptomics
Statistical and machine learning methods for spatially resolved transcriptomics data analysis
Integrating Spatially‐Resolved Transcriptomics Data Across Tissues and Individuals: Challenges and Opportunities
Spatially resolved transcriptomics in immersive environments
Spatially resolved transcriptomics in immersive environments
Spatially Resolved Transcriptomics Technology Facilitates Cancer Research
Technique integration of single-cell RNA sequencing with spatially resolved transcriptomics in the tumor microenvironment
Spatially resolved transcriptomics reveals plant host responses to pathogens
SRTsim: spatial pattern preserving simulations for spatially resolved transcriptomics
Deciphering spatial domains from spatially resolved transcriptomics through spatially regularized deep graph networks
Spatially resolved transcriptomics provide a new method for cancer research
Simulating multiple variability in spatially resolved transcriptomics with scCube
Gene count normalization in single-cell imaging-based spatially resolved transcriptomics
Statistical and machine learning methods for spatially resolved transcriptomics data analysis
Integrating Spatially‐Resolved Transcriptomics Data Across Tissues and Individuals: Challenges and Opportunities
Spatially resolved transcriptomics in immersive environments
Spatially resolved transcriptomics in immersive environments
Spatially Resolved Transcriptomics Technology Facilitates Cancer Research
Technique integration of single-cell RNA sequencing with spatially resolved transcriptomics in the tumor microenvironment
Spatially resolved transcriptomics reveals plant host responses to pathogens
SRTsim: spatial pattern preserving simulations for spatially resolved transcriptomics
Deciphering spatial domains from spatially resolved transcriptomics through spatially regularized deep graph networks
Spatially resolved transcriptomics provide a new method for cancer research
Simulating multiple variability in spatially resolved transcriptomics with scCube
Gene count normalization in single-cell imaging-based spatially resolved transcriptomics
Statistical and machine learning methods for spatially resolved transcriptomics data analysis