Single-cell RNA sequencing (scRNA-seq) promises to provide higher resolution of cellular differences than bulk RNA sequencing. Clustering transcriptomes profiled by scRNA-seq has been routinely conducted to reveal cell heterogeneity and diversity. However, clustering analysis of scRNA-seq data remains a statistical and computational challenge, due to the pervasive dropout events obscuring the data matrix with prevailing ‘false’ zero count observations. Here, we have developed scDeepCluster, a single-cell model-based deep embedded clustering method, which simultaneously learns feature representation and clustering via explicit modelling of scRNA-seq data generation. Based on testing extensive simulated data and real datasets from four representative single-cell sequencing platforms, scDeepCluster outperformed state-of-the-art methods under various clustering performance metrics and exhibited improved scalability, with running time increasing linearly with sample size. Its accuracy and efficiency make scDeepCluster a promising algorithm for clustering large-scale scRNA-seq data.
All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Computer Vision and Pattern Recognition
- Computer Networks and Communications
- Human-Computer Interaction