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TWO-SIGMA: a novel TWO-component SInGle cell Model-based Association method for single-cell RNA-seq data

Eric Van Buren, View ORCID ProfileMing Hu, Chen Weng, Fulai Jin, Yan Li, Di Wu, View ORCID ProfileYun Li
doi: https://doi.org/10.1101/709238
Eric Van Buren
Department of Biostatistics, The University of North Carolina at Chapel Hill
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Ming Hu
Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation
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  • ORCID record for Ming Hu
Chen Weng
Department of Genetics, School of Medicine, Case Western Reserve University
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Fulai Jin
Department of Genetics, School of Medicine, Case Western Reserve University
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Yan Li
Department of Genetics, School of Medicine, Case Western Reserve University
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Di Wu
Department of Biostatistics, The University of North Carolina at Chapel HillDepartment of Oral and Craniofacial Health Sciences, Adams School of Dentistry, The University of North Carolina at Chapel Hill
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  • For correspondence: yun_li@med.unc.edu did@email.unc.edu
Yun Li
Department of Biostatistics, The University of North Carolina at Chapel HillDepartment of Genetics, The University of North Carolina at Chapel HillDepartment of Computer Science, The University of North Carolina at Chapel Hill
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  • ORCID record for Yun Li
  • For correspondence: yun_li@med.unc.edu did@email.unc.edu
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Posted July 22, 2019.
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TWO-SIGMA: a novel TWO-component SInGle cell Model-based Association method for single-cell RNA-seq data
Eric Van Buren, Ming Hu, Chen Weng, Fulai Jin, Yan Li, Di Wu, Yun Li
bioRxiv 709238; doi: https://doi.org/10.1101/709238
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TWO-SIGMA: a novel TWO-component SInGle cell Model-based Association method for single-cell RNA-seq data
Eric Van Buren, Ming Hu, Chen Weng, Fulai Jin, Yan Li, Di Wu, Yun Li
bioRxiv 709238; doi: https://doi.org/10.1101/709238

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