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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
Hao Zhang
2Division of Health Informatics, Department of Population Health Sciences, Weill Cornell Medicine
Zilong Bai
2Division of Health Informatics, Department of Population Health Sciences, Weill Cornell Medicine
Dylan Mann-Krzisnik
3Quantitative Life Science, McGill University
Fei Wang
2Division of Health Informatics, Department of Population Health Sciences, Weill Cornell Medicine
Yue Li
3Quantitative Life Science, McGill University
4School of Computer Science, McGill University
5Mila - Quebec AI Institute

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Posted January 31, 2023.
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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