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Single-cell multi-omic topic embedding reveals cell-type-specific and COVID-19 severity-related immune signatures

Manqi Zhou, Hao Zhang, Zilong Bai, Dylan Mann-Krzisnik, Fei Wang, View ORCID ProfileYue Li
doi: https://doi.org/10.1101/2023.01.31.526312
Manqi Zhou
1Department of Computational Biology, Cornell University
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Hao Zhang
2Division of Health Informatics, Department of Population Health Sciences, Weill Cornell Medicine
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Zilong Bai
2Division of Health Informatics, Department of Population Health Sciences, Weill Cornell Medicine
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Dylan Mann-Krzisnik
3Quantitative Life Science, McGill University
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Fei Wang
2Division of Health Informatics, Department of Population Health Sciences, Weill Cornell Medicine
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  • For correspondence: few2001@med.cornell.edu yueli@cs.mcgill.ca
Yue Li
3Quantitative Life Science, McGill University
4School of Computer Science, McGill University
5Mila - Quebec AI Institute
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  • ORCID record for Yue Li
  • For correspondence: few2001@med.cornell.edu yueli@cs.mcgill.ca
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Posted January 31, 2023.
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Single-cell multi-omic topic embedding reveals cell-type-specific and COVID-19 severity-related immune signatures
Manqi Zhou, Hao Zhang, Zilong Bai, Dylan Mann-Krzisnik, Fei Wang, Yue Li
bioRxiv 2023.01.31.526312; doi: https://doi.org/10.1101/2023.01.31.526312
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Single-cell multi-omic topic embedding reveals cell-type-specific and COVID-19 severity-related immune signatures
Manqi Zhou, Hao Zhang, Zilong Bai, Dylan Mann-Krzisnik, Fei Wang, Yue Li
bioRxiv 2023.01.31.526312; doi: https://doi.org/10.1101/2023.01.31.526312

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