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[02330] Representation Learning for Continuous Single-cell Biology with Graph Neural Networks

  • Session Time & Room : 4E (Aug.24, 17:40-19:20) @D505
  • Type : Contributed Talk
  • Abstract : Single-cell RNA sequencing provides high-resolution transcriptomics to study cellular dynamic processes, yet its high-dimensionality, sparsity, and noises undermine the performance of downstream analysis. We propose a deep learning framework based on Variational Graph AutoEncoder to learn a low-dimensional representation that preserves global information and local continuity. By applying pseudotemporal ordering to the extracted features, we show that the model accurately preserves the dynamic cell trajectories of real and synthetic scRNA-seq datasets.
  • Classification : 92B20, 68T05, Machine Learning, Bioinformatics
  • Format : Talk at Waseda University
  • Author(s) :
    • Chengkai Yang (The University of Tokyo)