Accurate and fast cell marker gene identification with COSG

• Published in: Briefings in bioinformatics Vol. 23; no. 2
• Main Authors: Dai, Min, Pei, Xiaobing, Wang, Xiu-Jie
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 10.03.2022; Oxford Publishing Limited (England)
• Subjects: Biomarkers; Datasets; Gene Expression Profiling; Gene sequencing; Genes; Sequence Analysis, RNA; Similarity; Single-Cell Analysis - methods; Transcriptome; Transcriptomes; Whole Exome Sequencing; single-cell RNA-seq; single-cell ATAC-seq; spatially resolved transcriptomics; cosine similarity; cell marker gene
• ISSN: 1467-5463; 1477-4054; 1477-4054
• DOI: 10.1093/bib/bbab579

Abstract Accurate cell classification is the groundwork for downstream analysis of single-cell sequencing data, yet how to identify true marker genes for different cell types still remains a big challenge. Here, we report COSine similarity-based marker Gene identification (COSG) as a cosine similarity-based method for more accurate and scalable marker gene identification. COSG is applicable to single-cell RNA sequencing data, single-cell ATAC sequencing data and spatially resolved transcriptome data. COSG is fast and scalable for ultra-large datasets of million-scale cells. Application on both simulated and real experimental datasets showed that the marker genes or genomic regions identified by COSG have greater cell-type specificity, demonstrating the superior performance of COSG in terms of both accuracy and efficiency as compared with other available methods.