scGCRC: Graph and Contrastive-Based Representation Learning for Single-Cell RNA-Seq Data Clustering

Abstract

The advent of single-cell RNA-sequencing (scRNA-seq) technology promotes biological analysis at the cellular level. Clustering cells to identify the type of cell is an important step in scRNA-seq analysis. Most of the existing clustering methods based on deep learning technology first adopt an autoencoder-decoder module to learn the low-dimensional features of cells and then apply other modules to learn the clustering relationship features of cells. However, the two-stage learning process makes the model training more difficult. Here we propose a novel cell representation learning method that is based on a local self-attention network and contrastive learning for scRNA-seq clustering. In particular, a local self-attention network automatically aggregates potential information of cells based on a cell relationship graph, and a dual contrastive learning module simultaneously optimizes the cell representation in cell- and cluster-level. The cell-level module makes related cells similar at the feature level, whereas the cluster-level module enables cells to form clusters at the cluster level. Finally, the powerful Leiden community discovery algorithm is used for clustering based on learned representation. In brief, we construct cell pairs through cell relationships and utilize contrastive learning to directly learn cell representations in a low-dimensional space while preserving their local structural relationships without pretraining an autoencoder-decoder module. Three benchmark experiments on 160 subsample datasets with different numbers of cell types, 3 datasets of different protocols, and 9 real public datasets demonstrate the superior performance of the proposed method compared with baseline methods.