Deciphering spatial domains from spatially resolved transcriptomics through spatially regularized deep graph networks
Experimental results demonstrate that SR-DGN outperforms state-of-the-art methods in spatial domain identification across SRT data from different sequencing platforms. Moreover, SR-DGN is capable of recovering known microanatomical structures, yielding clearer low-dimensional visualizations and more accurate spatial trajectory inferences.
Authors
- Haoyang Li (ORCID: https://orcid.org/0000-0002-4854-3317)
- Na Yu (ORCID: https://orcid.org/0000-0002-1154-3348)
- Daoliang Zhang (ORCID: https://orcid.org/0009-0009-7682-6187)
- Wei Zhang (ORCID: https://orcid.org/0000-0003-1868-4810)
- Xu Qiao
- Wenjing Zhang (ORCID: https://orcid.org/0000-0003-0891-2752)
- Rui Gao (ORCID: https://orcid.org/0000-0001-7628-9128)
- Xue Sun
Institutions
- Shandong University (CN)
- University of Jinan (CN)
Publication Details
- Journal
- BMC Genomics
- Published
- 2024-11-29
- DOI
- https://doi.org/10.1186/s12864-024-11072-w
- Citations
- 1
- Primary Topic
- Single-cell and spatial transcriptomics
- Type
- article
- Field-Weighted Citation Impact
- 0.23
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Shandong Province