Investigation of cell development and tissue structure network based on natural Language processing of scRNA-seq data
This method offers a computationally efficient approach for analyzing scRNA-seq data by constructing embedding representations similar to those used in large language model pre-training, but without requiring high-performance computing clusters. By generating gene embeddings that capture functional relationships, this method facilitates the study of cell development trajectories, the impact of gene perturbations, cell clustering, and the construction and analysis of tissue networks. This provides a valuable tool for single-cell data analysis.
Authors
- Hai Lin
- Qi Dong Zhao (ORCID: https://orcid.org/0000-0001-9713-1864)
- Peng Wang (ORCID: https://orcid.org/0000-0001-8782-857X)
- Jianwei Shuai (ORCID: https://orcid.org/0000-0002-8712-0544)
- Yuer Lu
- Qing Li (ORCID: https://orcid.org/0000-0002-6464-0977)
- Yuming Peng (ORCID: https://orcid.org/0009-0002-0159-1220)
- Suwen Wei
Institutions
- University of Science and Technology Liaoning (CN)
- Wenzhou Medical University (CN)
- Zhejiang Lab (CN)
- Karamay Central Hospital of Xinjiang (CN)
- University of Chinese Academy of Sciences (CN)
Publication Details
- Journal
- Journal of Translational Medicine
- Published
- 2025-03-04
- DOI
- https://doi.org/10.1186/s12967-025-06263-2
- Citations
- 1
- Primary Topic
- Single-cell and spatial transcriptomics
- Type
- article
- Field-Weighted Citation Impact
- 0.81
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Liaoning Province