SCRIP: an accurate simulator for single-cell RNA sequencing data

Abstract

Abstract Motivation Recent advancements in single-cell RNA sequencing (scRNA-seq) have enabled time-efficient transcriptome profiling in individual cells. To optimize sequencing protocols and develop reliable analysis methods for various application scenarios, solid simulation methods for scRNA-seq data are required. However, due to the noisy nature of scRNA-seq data, currently available simulation methods cannot sufficiently capture and simulate important properties of real data, especially the biological variation. In this study, we developed scRNA-seq information producer (SCRIP), a novel simulator for scRNA-seq that is accurate and enables simulation of bursting kinetics. Results Compared to existing simulators, SCRIP showed a significantly higher accuracy of stimulating key data features, including mean–variance dependency in all experiments. SCRIP also outperformed other methods in recovering cell–cell distances. The application of SCRIP in evaluating differential expression analysis methods showed that edgeR outperformed other examined methods in differential expression analyses, and ZINB-WaVE improved the AUC at high dropout rates. Collectively, this study provides the research community with a rigorous tool for scRNA-seq data simulation. Availability and implementation https://CRAN.R-project.org/package=SCRIP. Supplementary information Supplementary data are available at Bioinformatics online.