scNAT: a deep learning method for integrating paired single-cell RNA and T cell receptor sequencing profiles.

Journal: Genome biology
PMID: 38111007

Abstract

Many deep learning-based methods have been proposed to handle complex single-cell data. Deep learning approaches may also prove useful to jointly analyze single-cell RNA sequencing (scRNA-seq) and single-cell T cell receptor sequencing (scTCR-seq) data for novel discoveries. We developed scNAT, a deep learning method that integrates paired scRNA-seq and scTCR-seq data to represent data in a unified latent space for downstream analysis. We demonstrate that scNAT is capable of removing batch effects, and identifying cell clusters and a T cell migration trajectory from blood to cerebrospinal fluid in multiple sclerosis.

Authors

  • Biqing Zhu
    Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA.
  • Yuge Wang
    Department of Biostatistics, School of Public Health, Yale University, New Haven, CT, 06511, USA.
  • Li-Ting Ku
    Department of Biostatistics, School of Public Health, Yale University, New Haven, CT, 06511, USA.
  • David van Dijk
    Department of Internal Medicine, Yale University, New Haven, CT, USA; Department of Computer Science, Yale University, New Haven, CT, USA.
  • Le Zhang
    State Key Laboratory of Multiphase Complex Systems, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China; College of Chemical Engineering, University of Chinese Academy of Sciences, Beijing 100049, China; Key Laboratory of Science and Technology on Particle Materials, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 361021, China.
  • David A Hafler
    Department of Neurology, School of Medicine, Yale University, New Haven, CT, 06511, USA.
  • Hongyu Zhao
    SJTU-Yale Joint Center for Biostatistics, Shanghai Jiao Tong University, 800 Dong Chuan Road, Shanghai 200240, China; Department of Biostatistics, Yale University, New Heaven, USA.

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