Single-cell RNA sequencing data clustering using graph convolutional networks

Abstract

Single-cell RNA sequencing (scRNAseq) makes it possible to analyze gene expression profiles at the individual cell scale and to discover intrinsic and extrinsic cellular processes in biological research. Cell clustering is one of the most important steps in analyzing scRNAseq data. With rapid developments of single cell sequencing technologies, scRNAseq data grow in size and heterogeneity. However, traditional clustering methods like Kmeans with or without dimension reduction methods, cannot handle high sparse and massive scRNAseq data. Although some deep learning based methods have been proposed to denoise the data and cluster cells simultaneously, learning informative representations of cells for accurate cell clustering is still a challenging problem to be solved. In this work, we propose a deep learning model that combines a deep graph convolutional network (GCN) and a self-supervised mechanism. The GCN considers not only the gene expressions but also the relationship between cells to represent cells. The self-supervised mechanism is employed to provide the clustering assignments of cells. Moreover, we utilize the negative log-likelihood of the negative binomial (NB) function as loss in the data reconstruction due to the assumption that genes expression values can be represented by the NB model. We compared the performance of our proposed method with those of the existing clustering methods for scRNAseq data and conventional clustering methods. Results show that our method achieves better performance in terms of accuracy, adjusted random index (ARI), and normalized mutual information (NMI).