SimCH: simulation of single -cell RNA sequencing data by modeling cellular heterogeneity at gene expression level

Single -cell ribonucleic acid (RNA) sequencing (scRNA-seq) has been a powerful technology for transcriptome analysis. However, the systematic validation of diverse computational tools used in scRNA-seq analysis remains challenging. Here, we propose a novel simulation tool, termed as Simulation of Cellular Heterogeneity (SimCH), for the flexible and comprehensive assessment of scRNAseq computational methods. The Gaussian Copula framework is recruited to retain gene coexpression of experimental data shown to be associated with cellular heterogeneity. The synthetic count matrices generated by suitable SimCH modes closely match experimental data originating from either homogeneous or heterogeneous cell populations and either unique molecular identifier (UMI)-based or non-UMI-based techniques. We demonstrate how SimCH can benchmark several types of computational methods, including cell clustering, discovery of differentially expressed genes, trajectory inference, batch correction and imputation. Moreover, we show how SimCH can be used to conduct power evaluation of cell clustering methods. Given these merits, we believe that SimCH can accelerate single -cell research.

Automated summary

Single-cell RNA sequencing (scRNA-seq) is a powerful technology for studying gene expression at the single-cell level. However, the evaluation of computational methods used in scRNA-seq analysis is challenging. In this study, we propose a simulation tool called Simulation of Cellular Heterogeneity (SimCH) that can accurately simulate scRNA-seq data and be used to benchmark computational methods. We demonstrate the utility of SimCH in evaluating various computational methods and conducting power evaluation of cell clustering methods.