Abstract
Single-cell RNA-seq (scRNA-seq) data simulation is critical for evaluating computational methods for analysing scRNA-seq data especially when ground truth is experimentally unattainable. The reliability of evaluation depends on the ability of simulation methods to capture properties of experimental data. However, while many scRNA-seq data simulation methods have been proposed, a systematic evaluation of these methods is lacking. We developed a comprehensive evaluation framework, SimBench, including a novel kernel density estimation measure to benchmark 12 simulation methods through 36 scRNA-seq experimental datasets. We evaluated the simulation methods on a panel of data properties, ability to maintain biological signals and computational scalability. Our benchmark uncovered performance differences among the methods and highlighted the varying difficulties in simulating data characteristics. Furthermore, we identified several limitations including maintaining heterogeneity of distribution. These results, together with the framework and datasets made publicly available as R packages, will guide simulation methods selection and their future development.
Competing Interest Statement
The authors have declared no competing interest.
