COME: contrastive mapping learning for spatial reconstruction of single-cell RNA sequencing data.

Journal: Bioinformatics (Oxford, England)
PMID: 39992219

Abstract

MOTIVATION: Single-cell RNA sequencing (scRNA-seq) enables high-throughput transcriptomic profiling at single-cell resolution. The inherent spatial location is crucial for understanding how single cells orchestrate multicellular functions and drive diseases. However, spatial information is often lost during tissue dissociation. Spatial transcriptomic (ST) technologies can provide precise spatial gene expression atlas, while their practicality is constrained by the number of genes they can assay or the associated costs at a larger scale and the fine-grained cell-type annotation. By transferring knowledge between scRNA-seq and ST data through cell correspondence learning, it is possible to recover the spatial properties inherent in scRNA-seq datasets.

Authors

  • Xindian Wei
    Department of Computer Science, City University of Hong Kong, Kowloon 999077, Hong Kong.
  • Tianyi Chen
    Department of Computer Science, City University of Hong Kong, Kowloon 999077, Hong Kong.
  • Xibiao Wang
    Department of Computer Science, Shantou University, Shantou 515063, China.
  • Wenjun Shen
    Department of Bioinformatics, Shantou University Medical College, Shantou 515041, China.
  • Cheng Liu
    Key Lab of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China; School of Earth and Space Sciences, University of Science and Technology of China, Hefei 230026, China; Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; Anhui Province Key Laboratory of Polar Environment and Global Change, University of Science and Technology of China, Hefei 230026, China. Electronic address: chliu81@ustc.edu.cn.
  • Si Wu
    State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China.
  • Hau-San Wong

Keywords

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