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A statistical framework for differential pseudotime analysis with multiple single-cell RNA-seq samples
View ORCID ProfileWenpin Hou, Zhicheng Ji, Zeyu Chen, E. John Wherry, View ORCID ProfileStephanie C. Hicks, Hongkai Ji
doi: https://doi.org/10.1101/2021.07.10.451910
Wenpin Hou
1Department of Biostatistics, The Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
Zhicheng Ji
2Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC 27710, USA
Zeyu Chen
3Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
4Institute for Immunology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
5Parker Institute for Cancer Immunotherapy at University of Pennsylvania, Philadelphia, PA 19104, USA
E. John Wherry
3Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
4Institute for Immunology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
5Parker Institute for Cancer Immunotherapy at University of Pennsylvania, Philadelphia, PA 19104, USA
Stephanie C. Hicks
1Department of Biostatistics, The Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
Hongkai Ji
1Department of Biostatistics, The Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
Posted July 12, 2021.
A statistical framework for differential pseudotime analysis with multiple single-cell RNA-seq samples
Wenpin Hou, Zhicheng Ji, Zeyu Chen, E. John Wherry, Stephanie C. Hicks, Hongkai Ji
bioRxiv 2021.07.10.451910; doi: https://doi.org/10.1101/2021.07.10.451910
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