DeepPNCC: reconstructing pseudo-spatial cell-cell interaction landscapes from single-cell data to decipher breast cancer pathogenesis
DeepPNCC provides an efficient tool for spatially informed analysis of scRNA-seq data, enabling the reconstruction of pseudo-spatial cell-cell interaction networks from datasets lacking explicit spatial information. This approach expands the spatial analytical scope of scRNA-seq and facilitates mechanistic dissection of cellular ecosystems in both health and disease.
Authors
- D. Guo (ORCID: https://orcid.org/0000-0003-0099-1628)
- Z.G. Wang (ORCID: https://orcid.org/0000-0002-1229-0071)
- Xuhua Li (ORCID: https://orcid.org/0000-0003-3253-0746)
- J. Liang (ORCID: https://orcid.org/0000-0002-8872-1376)
- Hong-jiu Wang
- Xiaoling Gao (ORCID: https://orcid.org/0000-0002-5466-3200)
- Na Wang (ORCID: https://orcid.org/0000-0001-6667-5957)
- Xiaoling Wen
- Sirui Li (ORCID: https://orcid.org/0000-0002-2267-4335)
- Jie Shen
Institutions
- University of Science and Technology of China (CN)
- Harbin Medical University (CN)
- Hefei Institutes of Physical Science (CN)
- Hainan General Hospital (CN)
- Hainan Medical College Hospital (CN)
- Hainan Medical University (CN)
Publication Details
- Journal
- Journal of Translational Medicine
- Published
- 2025-12-17
- DOI
- https://doi.org/10.1186/s12967-025-07578-w
- Primary Topic
- Single-cell and spatial transcriptomics
- Type
- article
- Field-Weighted Citation Impact
- 0.00