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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
doi: https://doi.org/10.1101/2023.01.31.526312

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doi 
https://doi.org/10.1101/2023.01.31.526312
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  • January 31, 2023.

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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-NC-ND 4.0 International license.

Author Information

  1. Manqi Zhou1,†,
  2. Hao Zhang2,†,
  3. Zilong Bai2,
  4. Dylan Mann-Krzisnik3,
  5. Fei Wang2,* and
  6. Yue Li3,4,5,*
  1. 1Department of Computational Biology, Cornell University
  2. 2Division of Health Informatics, Department of Population Health Sciences, Weill Cornell Medicine
  3. 3Quantitative Life Science, McGill University
  4. 4School of Computer Science, McGill University
  5. 5Mila - Quebec AI Institute
  1. ↵*Correspondence to few2001{at}med.cornell.edu, yueli{at}cs.mcgill.ca

  1. ↵† Equal contribution

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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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