Deep Spectral Clustering of Single-Cell RNA-seq Data

In recent years, Single cell RNA sequencing (scRNA-Seq) has become widely popular in bioinformatics. Single cell RNA-seq clustering is critical for determining cell type heterogenesity at single cell level and aims to assign cells that have similar transcriptomes into the same group. Since single cell RNA sequencing data are very complex and high dimensional classical unsupervised clustering techniques may not present satisfactory biological clustering performance. In this study, we propose to use deep spectral clustering method on three publicly available scRNA datasets and compare the clustering performance of the obtained model with different classical clustering algorithms. By using Normalized Mutual Information (NMI) evaluation metric, results show that deep spectral clustering method provides accurate and improved clustering performance.