Integrating multiple references for single-cell assignment

作者：Bin Duan, Shaoqi Chen, Xiaohan Chen, Chenyu Zhu, Chen Tang, Shuguang Wang, Yicheng Gao, Shaliu Fu, Qi Liu · 发表于：Nucleic Acids Research · 年份：2021 · DOI：10.1093/nar/gkab380 · 被引用次数：30 · 研究领域：Single-cell and spatial transcriptomics、Cancer Genomics and Diagnostics、Extracellular vesicles in disease

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.