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Deep learning enables accurate clustering and batch effect removal in single-cell RNA-seq analysis
Xiangjie Li, Yafei Lyu, Jihwan Park, Jingxiao Zhang, Dwight Stambolian, Katalin Susztak, Gang Hu, Mingyao Li
doi: https://doi.org/10.1101/530378
Xiangjie Li
1Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
2Center for Applied Statistics, School of Statistics, Renmin University, Beijing, China
Yafei Lyu
1Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
Jihwan Park
3Departments of Medicine and Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
Jingxiao Zhang
2Center for Applied Statistics, School of Statistics, Renmin University, Beijing, China
Dwight Stambolian
4Department of Ophthalmology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
Katalin Susztak
3Departments of Medicine and Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
Gang Hu
1Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
5Department of Information Theory and Data Science, School of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, China
Mingyao Li
1Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
Posted January 25, 2019.
Deep learning enables accurate clustering and batch effect removal in single-cell RNA-seq analysis
Xiangjie Li, Yafei Lyu, Jihwan Park, Jingxiao Zhang, Dwight Stambolian, Katalin Susztak, Gang Hu, Mingyao Li
bioRxiv 530378; doi: https://doi.org/10.1101/530378
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