scASGC: An adaptive simplified graph convolution model for clustering single-cell RNA-seq data

Shudong Wang, Yu Zhang, Yulin Zhang*, Wenhao Wu, Lan Ye, Yun Yin Li, Jionglong Su, Shanchen Pang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


Single-cell RNA sequencing (scRNA-seq) is now a successful technique for identifying cellular heterogeneity, revealing novel cell subpopulations, and forecasting developmental trajectories. A crucial component of the processing of scRNA-seq data is the precise identification of cell subpopulations. Although many unsupervised clustering methods have been developed to cluster cell subpopulations, the performance of these methods is vulnerable to dropouts and high dimensionality. In addition, most existing methods are time-consuming and fail to adequately account for potential associations between cells. In the manuscript, we present an unsupervised clustering method based on an adaptive simplified graph convolution model called scASGC. The proposed method builds plausible cell graphs, aggregates neighbor information using a simplified graph convolution model, and adaptively determines the most optimal number of convolution layers for various graphs. Experiments on 12 public datasets show that scASGC outperforms both classical and state-of-the-art clustering methods. In addition, in a study of mouse intestinal muscle containing 15,983 cells, we identified distinct marker genes based on the clustering results of scASGC. The source code of scASGC is available at

Original languageEnglish
Article number107152
JournalComputers in Biology and Medicine
Publication statusPublished - Sept 2023


  • Bioinformatics
  • Clustering
  • Computational biology
  • Graph convolution
  • Machine learning
  • ScRNA-seq


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