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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
Ming Hu
Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation
Chen Weng
Department of Genetics, School of Medicine, Case Western Reserve University
Fulai Jin
Department of Genetics, School of Medicine, Case Western Reserve University
Yan Li
Department of Genetics, School of Medicine, Case Western Reserve University
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
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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Posted July 22, 2019.
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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