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A robust statistical framework to detect multiple sources of hidden variation in single-cell transcriptomes

Donghyung Lee, Anthony Cheng, Duygu Ucar
doi: https://doi.org/10.1101/151217
Donghyung Lee
1 The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, Unites States of America,
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  • For correspondence: donghyung.lee@jax.org duygu.ucar@jax.org
Anthony Cheng
1 The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, Unites States of America,
2 University of Connecticut Health Center, Farmington, Connecticut, Unites States of America
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Duygu Ucar
1 The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, Unites States of America,
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  • For correspondence: donghyung.lee@jax.org duygu.ucar@jax.org
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Article Information

doi 
https://doi.org/10.1101/151217
History 
  • June 18, 2017.

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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. Donghyung Lee1,*,
  2. Anthony Cheng1,2 and
  3. Duygu Ucar1,*
  1. 1 The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, Unites States of America,
  2. 2 University of Connecticut Health Center, Farmington, Connecticut, Unites States of America
  1. ↵*Correspondence: donghyung.lee{at}jax.org and duygu.ucar{at}jax.org
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Posted June 18, 2017.
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A robust statistical framework to detect multiple sources of hidden variation in single-cell transcriptomes
Donghyung Lee, Anthony Cheng, Duygu Ucar
bioRxiv 151217; doi: https://doi.org/10.1101/151217
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A robust statistical framework to detect multiple sources of hidden variation in single-cell transcriptomes
Donghyung Lee, Anthony Cheng, Duygu Ucar
bioRxiv 151217; doi: https://doi.org/10.1101/151217

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