scRGCL: a cell type annotation method for single-cell RNA-seq data using residual graph convolutional neural network with contrastive learning
Lin Yuan, Shengguo Sun, Yufeng Jiang, Qinhu Zhang, Lan Ye, Chun-Hou Zheng, De-Shuang Huang · 2025
Read scRGCL: a cell type annotation method for single-cell RNA-seq data using residual graph convolutional neural network with contrastive learning by Lin Yuan, Shengguo Sun, Yufeng Jiang, Qinhu Zhang, Lan Ye, Chun-Hou Zheng, De-Shuang Huang (2025) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-11663015.txt.
Description
Cell type annotation is a critical step in analyzing single-cell RNA sequencing (scRNA-seq) data. A large number of deep learning (DL)–based methods have been proposed to annotate cell types of scRNA-seq data and have achieved impressive results. However, there are several limitations to these methods. First, they do not fully exploit cell-to-cell differential features.
Catalog record
| CREATOR | Lin Yuan, Shengguo Sun, Yufeng Jiang, Qinhu Zhang, Lan Ye, Chun-Hou Zheng, De-Shuang Huang |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 57 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |