scGASI: A Graph Autoencoder-Based Single-Cell Integration Clustering Method

Abstract

Single-cell RNA sequencing (scRNA-seq) technology offers the opportunity to study biological issues at the cellular level. The identification of single-cell types by unsupervised clustering is a basic goal of scRNA-seq data analysis. Although there have been a number of recent proposals for single-cell clustering methods, only a few of these have considered both shallow and deep potential information. Therefore, we propose a graph autoencoder-based single-cell integration clustering method, scGASI. Based on multiple feature sets, scGASI unifies deep feature embedding and data affinity recovery in a uniform framework to learn a consensus affinity matrix between cells. scGASI first constructs multiple feature sets. Then, to extract the deep potential information embedded in the data, scGASI uses a graph autoencoder (GAEs) to learn the low-dimensional latent representation of the data. Next, to effectively fuse the deep potential information in the embedding space and the shallow information in the raw space, we design a multi-layer kernel self-expression integration strategy. This strategy uses a kernel self-expression model with multi-layer similarity fusion to learn a similarity matrix shared by the raw and embedding spaces of a given feature set, and a consensus learning mechanism to learn a consensus affinity matrix across all feature sets. Finally, the consensus affinity matrix is used for spectral clustering, visualization, and identification of gene markers. Large-scale validation on real datasets shows that scGASI has higher clustering accuracy than many popular clustering methods.