spatiAlign: an unsupervised contrastive learning model for data integration of spatially resolved transcriptomics.

Journal: GigaScience
Published Date: Jan 2, 2024

Abstract

BACKGROUND: Integrative analysis of spatially resolved transcriptomics datasets empowers a deeper understanding of complex biological systems. However, integrating multiple tissue sections presents challenges for batch effect removal, particularly when the sections are measured by various technologies or collected at different times.

Authors

  • Chao Zhang
    School of Information Engineering, Suqian University, Suqian, Jiangsu, China.
  • Lin Liu
    Institute of Natural Sciences, MOE-LSC, School of Mathematical Sciences, CMA-Shanghai, SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University; Shanghai Artificial Intelligence Laboratory.
  • Ying Zhang
    Department of Nephrology, Nanchong Central Hospital Affiliated to North Sichuan Medical College, Nanchong, China.
  • Mei Li
    Department of Laboratory Medicine, Med+X Center for Manufacturing, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, China.
  • Shuangsang Fang
    BGI Research, Shenzhen 518083, China.
  • Qiang Kang
    School of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning, 116023, China. Electronic address: kangqiang@mail.dlut.edu.cn.
  • Ao Chen
    BGI Research, Shenzhen 518083, China.
  • Xun Xu
    BGI-Shenzhen, Shenzhen 518083, China.
  • Yong Zhang
    Outpatient Department of Hepatitis, The Sixth Affiliated People's Hospital of Dalian Medical University, Dalian, Liaoning, China.
  • Yuxiang Li
    BGI Research, Shenzhen 518083, China.

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