A single-cell expression simulator guided by gene regulatory networks
A common approach to benchmarking of single-cell transcriptomics tools is to generate synthetic data sets that resemble experimental data in their statistical properties. However, existing single-cell simulators do not incorporate known principles of transcription factor-gene regulatory interactions that underlie expression dynamics. Here we present SERGIO, a simulator of single-cell gene expression data that models the stochastic nature of transcription as well as linear and non-linear influences of multiple transcription factors on genes according to a user-provided gene regulatory network. SERGIO is capable of simulating any number of cell types in steady-state or cells differentiating to multiple fates according to a provided trajectory, reporting both unspliced and spliced transcript counts in single-cells. We show that data sets generated by SERGIO are comparable with experimental data in terms of multiple statistical measures. We also illustrate the use of SERGIO to benchmark several popular single-cell analysis tools, including GRN inference methods.