Clustering groups of cells in single-cell RNA sequencing datasets can produce high-resolution information for complex biological questions. However, it is statistically and computationally challenging due to the low RNA capture rate, which results in a high number of false zero count observations. A deep learning approach called scDeepCluster, which efficiently combines a model for explicitly characterizing missing values with clustering, shows high performance and improved scalability with a computing time increasing linearly with sample size.