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

Yi-Xuan Xiong, Meng-Guo Wang, Luonan Chen, Xiao-Fei Zhang
doi: https://doi.org/10.1101/2022.11.17.516980
Yi-Xuan Xiong
1School of Mathematics and Statistics, Central China Normal University, Wuhan, 430079, China
2Hubei Key Laboratory of Mathematical Sciences, Central China Normal University, Wuhan, 430079, China
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Meng-Guo Wang
1School of Mathematics and Statistics, Central China Normal University, Wuhan, 430079, China
2Hubei Key Laboratory of Mathematical Sciences, Central China Normal University, Wuhan, 430079, China
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Luonan Chen
3State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, 200031, China
4School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China
5Key Laboratory of Systems Health Science of Zhejiang Province, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou, 310024, China
6Guangdong Institute of Intelligence Science and Technology, Hengqin, Zhuhai, Guangdong, 519031, China
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  • For correspondence: zhangxf@mail.ccnu.edu.cn
Xiao-Fei Zhang
1School of Mathematics and Statistics, Central China Normal University, Wuhan, 430079, China
2Hubei Key Laboratory of Mathematical Sciences, Central China Normal University, Wuhan, 430079, China
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  • For correspondence: zhangxf@mail.ccnu.edu.cn
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Abstract

Automated cell-type annotation using a well-annotated single-cell RNA-sequencing (scRNA-seq) reference relies on the diversity of cell types in the reference. However, for technical and biological reasons, new query data of interest may contain unseen cell types that are missing from the reference. When annotating new query data, identifying unseen cell types is fundamental not only to improve annotation accuracy but also to new biological discoveries. Here, we propose mtANN (multiple-reference-based scRNA-seq data annotation), a new method to automatically annotate query data while accurately identifying unseen cell types with the help of multiple references. Key innovations of mtANN include the integration of deep learning and ensemble learning to improve prediction accuracy, and the introduction of a new metric defined from three complementary aspects to identify unseen cell types. We demonstrate the advantages of mtANN over state-of-the-art methods for cell-type annotation and unseen cell-type identification on two benchmark dataset collections, as well as its predictive power on a collection of COVID-19 datasets.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • ↵† The authors wish it to be known that, in their opinion, the first two authors should be regarded as Joint First Authors.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted November 18, 2022.
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Cell-type annotation with accurate unseen cell-type identification using multiple references
Yi-Xuan Xiong, Meng-Guo Wang, Luonan Chen, Xiao-Fei Zhang
bioRxiv 2022.11.17.516980; doi: https://doi.org/10.1101/2022.11.17.516980
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Cell-type annotation with accurate unseen cell-type identification using multiple references
Yi-Xuan Xiong, Meng-Guo Wang, Luonan Chen, Xiao-Fei Zhang
bioRxiv 2022.11.17.516980; doi: https://doi.org/10.1101/2022.11.17.516980

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