SCMcluster: a high-precision cell clustering algorithm integrating marker gene set with single-cell RNA sequencing data

• Published in: Briefings in functional genomics Vol. 22; no. 4; pp. 329 - 340
• Main Authors: Wu, Hao, Zhou, Haoru, Zhou, Bing, Wang, Meili
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 17.07.2023
• Subjects: Algorithms; Animals; Cluster Analysis; Gene Expression Profiling - methods; Humans; Mice; Sequence Analysis, RNA - methods; Single-Cell Analysis - methods; scRNA-seq; ensemble clustering; marker genes; single-cell clustering
• ISSN: 2041-2649; 2041-2657; 2041-2657
• DOI: 10.1093/bfgp/elad004

Abstract Single-cell clustering is the most significant part of single-cell RNA sequencing (scRNA-seq) data analysis. One main issue facing the scRNA-seq data is noise and sparsity, which poses a great challenge for the advance of high-precision clustering algorithms. This study adopts cellular markers to identify differences between cells, which contributes to feature extraction of single cells. In this work, we propose a high-precision single-cell clustering algorithm-SCMcluster (single-cell cluster using marker genes). This algorithm integrates two cell marker databases(CellMarker database and PanglaoDB database) with scRNA-seq data for feature extraction and constructs an ensemble clustering model based on the consensus matrix. We test the efficiency of this algorithm and compare it with other eight popular clustering algorithms on two scRNA-seq datasets derived from human and mouse tissues, respectively. The experimental results show that SCMcluster outperforms the existing methods in both feature extraction and clustering performance. The source code of SCMcluster is available for free at https://github.com/HaoWuLab-Bioinformatics/SCMcluster.