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Cell-type annotation with accurate unseen cell-type identification using multiple references

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成果类型:
期刊论文
作者:
Xiong, Yi-Xuan;Wang, Meng-Guo;Chen, Luonan;Zhang, Xiao-Fei
通讯作者:
Luonan Chen ,<&wdkj&>Xiao-Fei Zhang
作者机构:
[Zhang, Xiao-Fei; Xiong, Yi-Xuan; Wang, Meng-Guo] School of Mathematics and Statistics, Central China Normal University, Wuhan, China
[Zhang, Xiao-Fei; Xiong, Yi-Xuan; Wang, Meng-Guo] Key Laboratory of Nonlinear Analysis & Applications (Ministry of Education), Central China Normal University, Wuhan, China
[Chen, Luonan] State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China
[Chen, Luonan] School of Life Science and Technology, ShanghaiTech University, Shanghai, China
[Chen, Luonan] Key Laboratory of Systems Health Science of Zhejiang Province, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou, China
通讯机构:
[Luonan Chen ,; Xiao-Fei Zhang]
State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China, School of Life Science and Technology, ShanghaiTech University, Shanghai, China, Key Laboratory of Systems Health Science of Zhejiang Province, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou, China, Guangdong Institute of Intelligence Science and Technology, Hengqin, Zhuhai, Guangdong, China<&wdkj&> School of Mathematics and Statistics, Central China Normal University, Wuhan, China, Key Laboratory of Nonlinear Analysis & Applications (Ministry of Education), Central China Normal University, Wuhan, China
语种:
英文
关键词:
Pancreas;COVID 19;Entropy;Gene expression;Dendritic cells;B cells;Virus testing;Monocytes
期刊:
PLOS COMPUTATIONAL BIOLOGY
ISSN:
1553-734X
年:
2023
卷:
19
期:
6
页码:
e1011261
机构署名:
本校为第一机构
院系归属:
数学与统计学学院
摘要:
The recent advances in single-cell RNA sequencing (scRNA-seq) techniques have stimulated efforts to identify and characterize the cellular composition of complex tissues. With the advent of various sequencing techniques, automated cell-type annotation using a well-annotated scRNA-seq reference becomes popular. But it relies on the diversity of cell types in the reference, which may not capture all the cell types present in the query data of interest. There are generally unseen cell types in the query data of interest because most data atlases are obtained for different purposes and techniques....

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