• Corpus ID: 239015980

SGEN: Single-cell Sequencing Graph Self-supervised Embedding Network

@article{Liu2021SGENSS,
  title={SGEN: Single-cell Sequencing Graph Self-supervised Embedding Network},
  author={Ziyi Liu and Minghui Liao and Fulin Luo and Bo Du},
  journal={ArXiv},
  year={2021},
  volume={abs/2110.09413}
}
Single-cell sequencing has significant role to explore biological processes such as embryonic development, cancer evolution and cell differentiation. These biological properties can be presented by two-dimensional scatter plot. However, singlecell sequencing data generally has very high dimensionality. Therefore, dimensionality reduction should be used to process the high dimensional sequencing data for 2D visualization and subsequent biological analysis. The traditional dimensionality… 

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