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Rapid Communication | Open Access

Performance analysis of markers for prostate cell typing in single-cell data

Yanting Shena,b,1Xiawei Feie,1Junyan XufRui Yangd( )Qinyu Gec( )Zhong Wangb( )
Department of Urology, Shanghai Ninth People's Hospital, Shanghai Jiaotong University School of Medicine, Shanghai 200011, China
Department of Urology and Andrology, Gongli Hospital, The Second Military Medical University, Shanghai 200135, China
State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, Jiangsu 210096, China
Wuxi Maternal and Child Health Hospital, Wuxi School of Medicine, Jiangnan University, Jiangsu 214002, China
Department of Urology, Qingpu Branch of Zhongshan Hospital Affiliated to Fudan University, Shanghai 201799, China
University of Shanghai for Science and Technology, Shanghai 200093, China

1 These authors contributed equally to this work and shared first authorship.

Peer review under responsibility of Chongqing Medical University.

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Nichols L, Taverner T, Crowe F, et al. In simulated data and health records, latent class analysis was the optimum multimorbidity clustering algorithm. J Clin Epidemiol. 2022;152:164-175.

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Genes & Diseases
Article number: 101157
Cite this article:
Shen Y, Fei X, Xu J, et al. Performance analysis of markers for prostate cell typing in single-cell data. Genes & Diseases, 2024, 11(6): 101157. https://doi.org/10.1016/j.gendis.2023.101157

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Received: 05 July 2023
Published: 26 October 2023
© 2023 The Authors.

This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

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