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Airpart: Interpretable statistical models for analyzing allelic imbalance in single-cell datasets

Wancen Mu, Hirak Sarkar, Avi Srivastava, Kwangbom Choi, Rob Patro, Michael I. Love
doi: https://doi.org/10.1101/2021.10.15.464546

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doi 
https://doi.org/10.1101/2021.10.15.464546
History 
  • October 16, 2021.
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 4.0 International license.

Author Information

  1. Wancen Mu1,†,
  2. Hirak Sarkar2,
  3. Avi Srivastava3,
  4. Kwangbom Choi4,
  5. Rob Patro2 and
  6. Michael I. Love*,1,5
  1. 1Department of Biostatistics, University of North Carolina-Chapel Hill
  2. 2Department of Computer Science, University of Maryland, College Park, MD
  3. 3New York Genome Center, New York, NY 10013, USA
  4. 4The Jackson Laboratory, Bar Harbor, ME 04609, USA
  5. 5Department of Genetics, University of North Carolina-Chapel Hill
  1. ↵† Corresponding author; email: wancenmu{at}gmail.com
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Posted October 16, 2021.
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Airpart: Interpretable statistical models for analyzing allelic imbalance in single-cell datasets
Wancen Mu, Hirak Sarkar, Avi Srivastava, Kwangbom Choi, Rob Patro, Michael I. Love
bioRxiv 2021.10.15.464546; doi: https://doi.org/10.1101/2021.10.15.464546
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