DANCE: a deep learning library and benchmark platform for single-cell analysis.

Journal: Genome biology
Published Date: Mar 19, 2024

Abstract

DANCE is the first standard, generic, and extensible benchmark platform for accessing and evaluating computational methods across the spectrum of benchmark datasets for numerous single-cell analysis tasks. Currently, DANCE supports 3 modules and 8 popular tasks with 32 state-of-art methods on 21 benchmark datasets. People can easily reproduce the results of supported algorithms across major benchmark datasets via minimal efforts, such as using only one command line. In addition, DANCE provides an ecosystem of deep learning architectures and tools for researchers to facilitate their own model development. DANCE is an open-source Python package that welcomes all kinds of contributions.

Authors

  • Jiayuan Ding
    Department of Computer Science and Engineering, Michigan State University, East Lansing, USA. dingjia5@msu.edu.
  • Renming Liu
    Department of Computational Mathematics, Science & Engineering, Michigan State University, East Lansing, MI 48824, USA.
  • Hongzhi Wen
    Department of Computer Science and Engineering, Michigan State University, East Lansing, USA.
  • Wenzhuo Tang
    Department of Statistics and Probability, Michigan State University, East Lansing, USA.
  • Zhaoheng Li
    Department of Biostatistics, University of Washington, Seattle, USA.
  • Julian Venegas
    Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, USA.
  • Runze Su
    Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, USA.
  • Dylan Molho
    Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, USA.
  • Wei Jin
    Department of Cardiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin 2nd Road, Shanghai, 200025, PR China; Institute of Cardiovascular Diseases, Shanghai Jiao Tong University School of Medicine, 197 Ruijin 2nd Road, Shanghai, 200025, PR China. Electronic address: jinwei1125@126.com.
  • Yixin Wang
    Structural Biophysics Group, School of Optometry and Vision Sciences, Cardiff University, Cardiff, Wales, UK.
  • Qiaolin Lu
    School of Artificial Intelligence, Jilin University, Jilin, China.
  • Lingxiao Li
    Department of Computer Science, Boston University, Boston, USA.
  • Wangyang Zuo
    Department of Computer Science, Zhejiang University of Technology, Zhejiang, China.
  • Yi Chang
  • Yuying Xie
    Department of Computational Mathematics, Science, and Engineering, Michigan State University, East Lansing, MI 48823.
  • Jiliang Tang
    Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA.

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