DeepST: identifying spatial domains in spatial transcriptomics by deep learning.

Journal: Nucleic acids research
Published Date: Dec 9, 2022

Abstract

Recent advances in spatial transcriptomics (ST) have brought unprecedented opportunities to understand tissue organization and function in spatial context. However, it is still challenging to precisely dissect spatial domains with similar gene expression and histology in situ. Here, we present DeepST, an accurate and universal deep learning framework to identify spatial domains, which performs better than the existing state-of-the-art methods on benchmarking datasets of the human dorsolateral prefrontal cortex. Further testing on a breast cancer ST dataset, we showed that DeepST can dissect spatial domains in cancer tissue at a finer scale. Moreover, DeepST can achieve not only effective batch integration of ST data generated from multiple batches or different technologies, but also expandable capabilities for processing other spatial omics data. Together, our results demonstrate that DeepST has the exceptional capacity for identifying spatial domains, making it a desirable tool to gain novel insights from ST studies.

Authors

  • Chang Xu
    Institute of Cardio-Cerebrovascular Medicine, Central Hospital of Dalian University of Technology, Dalian 116089, China.
  • Xiyun Jin
    Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150000, China.
  • Songren Wei
    Department of Neuropharmacology and Novel Drug Discovery, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou 510515, China.
  • Pingping Wang
    School of Electrical and Information Engineering, Tianjin University, 300072, Tianjin, China.
  • Meng Luo
    Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, China; State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center for Biotherapy, Chengdu, China.
  • Zhaochun Xu
    Computer Department, Jingdezhen Ceramic University, Jingdezhen, China.
  • Wenyi Yang
    School of Software, Central South University, Changsha, 410075, China.
  • Yideng Cai
    School of Computer Science and Engineering, Central South University, Changsha 410083, China.
  • Lixing Xiao
    Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150000, China.
  • Xiaoyu Lin
    Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150000, China.
  • Hongxin Liu
    School of Science and Information Science, Qingdao Agricultural University, Qingdao, 266109, China.
  • Rui Cheng
    Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, China.
  • Fenglan Pang
    School of Life Science and Technology, Harbin Institute of Technology, Harbin 150000, China.
  • Rui Chen
    College of Food Science and Engineering, Northwest A&F University, Yangling 712100, Shanxi, China.
  • Xi Su
    Foshan Maternal and Child Health Hospital, Foshan, Guangdong, China.
  • Ying Hu
    Department of Ultrasonography, The First Affiliated Hospital, College of Medicine, Zhejiang University, Qingchun Road No. 79, Hangzhou, Zhejiang 310003, China.
  • Guohua Wang
    School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China.
  • Qinghua Jiang
    School of Life Science and Technology, Harbin Institute of Technology, Harbin, China.

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