SGEN: Single-cell Sequencing Graph Self-supervised Embedding Network
- URL: http://arxiv.org/abs/2110.09413v1
- Date: Fri, 15 Oct 2021 13:59:58 GMT
- Title: SGEN: Single-cell Sequencing Graph Self-supervised Embedding Network
- Authors: Ziyi Liu, Minghui Liao, Fulin luo, Bo Du
- Abstract summary: We develop a 2D feature representation method based on graph convolutional networks (GCN) for the visualization of single-cell data.
The results show SGEN achieves obvious 2D distribution and preserves the high-dimensional relationship of different cells.
- Score: 35.11562214480459
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Single-cell sequencing has a significant role to explore biological processes
such as embryonic development, cancer evolution, and cell differentiation.
These biological properties can be presented by a two-dimensional scatter plot.
However, single-cell 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 reduction methods, which do not
consider the structure characteristics of single-cell sequencing data, are
difficult to reveal the data structure in the 2D representation. In this paper,
we develop a 2D feature representation method based on graph convolutional
networks (GCN) for the visualization of single-cell data, termed single-cell
sequencing graph embedding networks (SGEN). This method constructs the graph by
the similarity relationship between cells and adopts GCN to analyze the
neighbor embedding information of samples, which makes the similar cell closer
to each other on the 2D scatter plot. The results show SGEN achieves obvious 2D
distribution and preserves the high-dimensional relationship of different
cells. Meanwhile, similar cell clusters have spatial continuity rather than
relying heavily on random initialization, which can reflect the trajectory of
cell development in this scatter plot.
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