Extended Data Fig. 2: Benchmarking scDesign3 against four existing scRNA-seq simulators (scGAN, muscat, SPARSim, and ZINB-WaVE) for generating scRNA-seq data from bifurcating trajectories (myeloid progenitors in mouse bone marrow; dataset MARROW in Supplementary Table 2).

a, Distributions of eight summary statistics in the test data and the synthetic data generated by scDesign3 and the four simulators. Each number on top of a violin plot (the distribution of a summary statistic in a synthetic dataset) is the Kolmogorov-Smirnov (KS) distance between the synthetic data distribution (indicated by that violin plot) and the test data distribution. A smaller number indicates better agreement between the synthetic data and the test data in terms of that summary statistic’s distribution. b, Heatmaps of the gene-gene correlation matrices (showing top 100 highly expressed genes) in the test data and the synthetic data generated by scDesign3 and the four simulators. Pearson’s correlation coefficient r measures the similarity between two correlation matrices, one from the test data and the other from the synthetic data. c, PCA visualization (top two PCs) of the test data and the synthetic data generated by scDesign3 and the four simulators. Colors label cells’ pseudotime values in two trajectories; note that only the synthetic data generated by scDesign3 contain the pseudotime truths. An mLISI value close to 2 means that the synthetic data resemble the real data well in the low-dimensional space. d, UMAP visualization of the real data and the synthetic data generated by scDesign3 and the four simulators.