TransAnno-Net: A Deep Learning Framework for Accurate Cell Type Annotation of Mouse Lung Tissue Using Self-supervised Pretraining.

Journal: Computer methods and programs in biomedicine
Published Date: Apr 24, 2025

Abstract

BACKGROUND: Single-cell RNA sequencing (scRNA-seq) has become a significant tool for addressing complex issuess in the field of biology. In the context of scRNA-seq analysis, it is imperative to accurately determine the type of each cell. However, conventional supervised or semi-supervised methodologies are contingent on expert labels and incur substantial labeling costs, In contrast self-supervised pre-training strategies leverage unlabeled data during the pre-training phase and utilise a limited amount of labeled data in the fine-tuning phase, thereby greatly reducing labor costs. Furthermore, the fine-tuning does not need to learn the feature representations from scratch, enhancing the efficiency and transferability of the model.

Authors

  • Qing Zhang
    Department of Respiratory Medicine, Affiliated Zhongshan Hospital of Dalian University, Dalian, China.
  • Xiaoxiao Wu
    Department of Neurosurgery & Brain and Nerve Research Laboratory, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province 215006, China; Suzhou Medical College of Soochow University, Suzhou, Jiangsu Province 215002, China.
  • Xiang Li
    Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States.
  • Wei Ma
    Institute of Urban Agriculture, Chinese Academy of Agricultural Sciences, Chengdu, China.
  • Tongquan Wu
    School of Mechanical Engineering, Hefei University of Technology, Hefei, PR China.
  • Liuyue Li
    Faculty of Applied Science and Engineering, University of Toronto, Canada.
  • Fan Hu
    Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
  • Yicheng Xie
    Department of Dermatology, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, 310052, Zhejiang Province, China. Electronic address: ycxie@zju.edu.cn.
  • Xinglong Wu
    School of Computer Science & Engineering, Wuhan Institute of Technology, Wuhan, Hubei, 430205, China; Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China.

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