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Integrating multiple references for single-cell assignment.

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.

SUBMITTER: Duan B

PROVIDER: S-EPMC8373058 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature

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Integrating multiple references for single-cell assignment.

Duan Bin B Chen Shaoqi S Chen Xiaohan X Zhu Chenyu C Tang Chen C Wang Shuguang S Gao Yicheng Y Fu Shaliu S Liu Qi Q

Nucleic acids research 20210801 14

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 ass ...[more]

PMID: 34037791

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