PMID- 35048116 OWN - NLM STAT- Publisher TI - Accurate and fast cell marker gene identification with COSG. CI - © The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com. LA - eng PT - Journal Article PL - England TA - Brief Bioinform JT - Briefings in bioinformatics JID - 100912837 IS - 1477-4054 (Electronic) LID - bbab579 [pii] LID - 10.1093/bib/bbab579 [doi] FAU - Dai, Min AU - Dai M AUID- ORCID: 0000-0001-7584-5014 FAU - Pei, Xiaobing AU - Pei X FAU - Wang, Xiu-Jie AU - Wang XJ AUID- ORCID: 0000-0001-7865-0204 IS - 1467-5463 (Linking) SB - IM OTO - NOTNLM OT - cell marker gene OT - cosine similarity OT - single-cell ATAC-seq OT - single-cell RNA-seq OT - spatially resolved transcriptomics LR - 20220120 DP - 2022 Jan 19 DEP - 20220119 AB - 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.