Systematic benchmarking of computational methods to identify spatially variable genes
Our benchmarking provides a detailed comparison of SVG detection methods and serves as a reference for both users and method developers.
Authors
- Robrecht Cannoodt (ORCID: https://orcid.org/0000-0003-3641-729X)
- Dongyuan Song (ORCID: https://orcid.org/0000-0003-1114-1215)
- Jingyi Jessica Li (ORCID: https://orcid.org/0000-0002-9288-5648)
- Guanao Yan (ORCID: https://orcid.org/0000-0002-6861-1882)
- Luca Pinello (ORCID: https://orcid.org/0000-0003-1109-3823)
- Sai Nirmayi Yasa
- Zhijian Li (ORCID: https://orcid.org/0000-0002-1523-1333)
- Z. Patel (ORCID: https://orcid.org/0000-0002-4387-9946)
Institutions
- Broad Institute (US)
- University of California, Los Angeles (US)
- Ghent University (BE)
- Massachusetts General Hospital (US)
- Fred Hutch Cancer Center (US)
- VIB-UGent Center for Inflammation Research (BE)
- Center for Cancer Research (US)
- UConn Health (US)
- Michigan State University (US)
Publication Details
- Journal
- Genome biology
- Published
- 2025-09-18
- DOI
- https://doi.org/10.1186/s13059-025-03731-2
- Citations
- 6
- Primary Topic
- Single-cell and spatial transcriptomics
- Type
- article
- Field-Weighted Citation Impact
- 4.84
Funders
- National Science Foundation
- National Institutes of Health
- National Human Genome Research Institute
- National Institute of General Medical Sciences