scSMD: a deep learning method for accurate clustering of single cells based on auto-encoder.

Journal: BMC bioinformatics
PMID: 39881248

Abstract

BACKGROUND: Single-cell RNA sequencing (scRNA-seq) has transformed biological research by offering new insights into cellular heterogeneity, developmental processes, and disease mechanisms. As scRNA-seq technology advances, its role in modern biology has become increasingly vital. This study explores the application of deep learning to single-cell data clustering, with a particular focus on managing sparse, high-dimensional data.

Authors

  • Xiaoxu Cui
    School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China.
  • Renkai Wu
    School of Microelectronics, Shanghai University, Shanghai, China.
  • Yinghao Liu
    School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China.
  • Peizhan Chen
    Clinical Research Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Qing Chang
    Department of Ultrasound, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
  • Pengchen Liang
    School of Microelectronics, Shanghai University, Shanghai, China.
  • Changyu He
    Department of Surgery, Shanghai Key Laboratory of Gastric Neoplasms, Shanghai Institute of Digestive Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. hechangyu.2008@163.com.

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