Integrating multiple references for single-cell assignment. Issue 14 (25th May 2021)

• Record Type: Journal Article
• Authors: Duan, Bin; Chen, Shaoqi; Chen, Xiaohan; Zhu, Chenyu; Tang, Chen; Wang, Shuguang; Gao, Yicheng; Fu, Shaliu; Liu, Qi
• Is Part Of: Nucleic acids research. Volume 49:Issue 14(2021)
• Page Start: e80
• Page End: e80
• Publication Date: 2021-05-25
• Subjects: Nucleic acids -- Periodicals; Molecular biology -- Periodicals; 572.805
• DOI: 10.1093/nar/gkab380
• Languages: English
• ISSNs: 0305-1048

Abstract

Abstract: Efficient single-cell assignment is essential for single-cell sequencing data analysis. With the explosive growth of single-cell sequencing data, multiple single-cell sequencing data sources are available for the same kind of tissue, which can be integrated to further improve single-cell assignment; however, an efficient integration strategy is still lacking due to the great challenges of data heterogeneity existing in multiple references. To this end, we present mtSC, a flexible single-cell assignment framework that integrates multiple references based on multitask deep metric learning designed specifically for cell type identification within tissues with multiple single-cell sequencing data as references. We evaluated mtSC on a comprehensive set of publicly available benchmark datasets and demonstrated its state-of-the-art effectiveness for integrative single-cell assignment with multiple references.