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

TL;DR

This paper introduces SGEN, a graph convolutional network-based method for effectively reducing dimensionality of single-cell sequencing data, preserving biological structure and revealing cell development trajectories in 2D visualizations.

Contribution

SGEN is the first to apply GCNs for single-cell data visualization, capturing data structure and developmental trajectories more accurately than traditional methods.

Findings

SGEN produces clearer 2D distributions of cells.

It preserves high-dimensional relationships between cells.

It reflects cell development trajectories in the scatter plot.

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.