ScGSLC: An unsupervised graph similarity learning framework for single-cell RNA-seq data clustering. (February 2021)

• Record Type: Journal Article
• Authors: Li, Junyi; Jiang, Wei; Han, Henry; Liu, Jing; Liu, Bo; Wang, Yadong
• Is Part Of: Computational biology and chemistry. Volume 90(2021)
• Publication Date: 2021-02
• Subjects: Single-cell RNA sequencing data -- Unsupervised clustering -- Graph similarity -- Graph embedding -- Graph convolution network; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2020.107415
• Languages: English
• ISSNs: 1476-9271

Abstract

Abstract: Accurate clustering of cells from single-cell RNA sequencing (scRNA-seq) data is an essential step for biological analysis such as putative cell type identification. However, scRNA-seq data has high dimension and high sparsity, which makes traditional clustering methods less effective to reflect the similarity between cells. Since genetic network fundamentally defines the functions of cell and deep learning shows strong advantages in network representation learning, we propose a novel scRNA-seq clustering framework ScGSLC based on graph similarity learning. ScGSLC effectively integrates scRNA-seq data and protein-protein interaction network to a graph. Then graph convolution network is employed by ScGSLC to embedding graph and clustering the cells by the calculated similarity between graphs. Unsupervised clustering results of nine public data sets demonstrate that ScGSLC shows better performance than the state-of-the-art methods.