Consensus clustering of single-cell RNA-seq data by enhancing network affinity

作     者：Cui, Yaxuan Zhang, Shaoqiang Liang, Ying Wang, Xiangyun Ferraro, Thomas N. Chen, Yong 

作者机构：Tianjin Normal Univ Coll Comp & Informat Engn Tianjin 300387 Peoples R China CMSRU Dept Biomed Sci Camden NJ USA Rowan Univ Dept Mol & Cellular Biosci Camden NJ 08028 USA 

出 版 物：《BRIEFINGS IN BIOINFORMATICS》 (生物信息学简报)

年 卷 期：2021年第22卷第6期

页      面：bbab236-bbab236页

核心收录：

学科分类：0710[理学-生物学] 07[理学] 09[农学] 

基　　金：Natural Science Foundation of Tianjin City [19JCZDJC35100] National Science Foundation of China Rowan University 

主　　题：single-cell RNA-seq clustering algorithm bioinformatics cell typing 

摘      要：Elucidation of cell subpopulations at high resolution is a key and challenging goal of single-cell ribonucleic acid (RNA) sequencing (scRNA-seq) data analysis. Although unsupervised clustering methods have been proposed for de novo identification of cell populations, their performance and robustness suffer from the high variability, low capture efficiency and high dropout rates which are characteristic of scRNA-seq experiments. Here, we present a novel unsupervised method for Single-cell Clustering by Enhancing Network Affinity (SCENA), which mainly employed three strategies: selecting multiple gene sets, enhancing local affinity among cells and clustering of consensus matrices. Large-scale validations on 13 real scRNA-seq datasets show that SCENA has high accuracy in detecting cell populations and is robust against dropout noise. When we applied SCENA to large-scale scRNA-seq data of mouse brain cells, known cell types were successfully detected, and novel cell types of interneurons were identified with differential expression of gamma-aminobutyric acid receptor subunits and transporters. SCENA is equipped with CPU+GPU (Central Processing Units+Graphics Processing Units) heterogeneous parallel computing to achieve high running speed. The high performance and running speed of SCENA combine into a new and efficient platform for biological discoveries in clustering analysis of large and diverse scRNA-seq datasets.