The large-scale, high-dimensional, and sparse single-cell RNA sequencing (scRNA-seq) data have raised great challenges in the pipeline of data analysis. A large number of statistical and machine learning methods have been developed to analyze scRNA-seq data and answer related scientific questions. Although different methods claim advantages in certain circumstances, it is difficult for users to select appropriate methods for their analysis tasks. Benchmark studies aim to provide recommendations for method selection based on an objective, accurate, and comprehensive comparison among cutting-edge methods. They can also offer suggestions for further methodological development through massive evaluations conducted on real data.