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scMultiSim: simulation of single-cell multi-omics and spatial data guided by gene regulatory networks and cell–cell interactions

Nature Methods volume 22, pages 982–993 (2025)Cite this article

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Abstract

Simulated single-cell data are essential for designing and evaluating computational methods in the absence of experimental ground truth. Here we present scMultiSim, a comprehensive simulator that generates multimodal single-cell data encompassing gene expression, chromatin accessibility, RNA velocity and spatial cell locations while accounting for the relationships between modalities. Unlike existing tools that focus on limited biological factors, scMultiSim simultaneously models cell identity, gene regulatory networks, cell–cell interactions and chromatin accessibility while incorporating technical noise. Moreover, it allows users to adjust each factor’s effect easily. Here we show that scMultiSim generates data with expected biological effects, and demonstrate its applications by benchmarking a wide range of computational tasks, including multimodal and multi-batch data integration, RNA velocity estimation, gene regulatory network inference and cell–cell interaction inference using spatially resolved gene expression data. Compared to existing simulators, scMultiSim can benchmark a much broader range of existing computational problems and even new potential tasks.

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Fig. 1: Overview of scMultiSim.
Fig. 2: scMultiSim generates multimodal single-cell data from a predefined cell clustering structure or trajectory.
Fig. 3: scMultiSim generates realistic single-cell gene expression data driven by GRNs and CCI.
Fig. 4: Benchmarking mosaic data integration methods.
Fig. 5: Benchmarking GRN inference methods.
Fig. 6: Benchmarking CCI inference methods.

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Data availability

The simulated datasets are available in Zenodo via https://doi.org/10.5281/zenodo.13119261 (ref. 55). The seqFISH+ data can be downloaded using the GiottoData R package, or on GitHub via https://github.com/drieslab/spatial-datasets/tree/master/data/2019_seqfish_plus_SScortex/. The original data are available at the Gene Expression Omnibus under accession number GSE98674. The 10x Multinome data are available at https://www.10xgenomics.com/resources/datasets/pbmc-from-a-healthy-donor-no-cell-sorting-3-k-1-standard-2-0-0/. The MERFISH data can be obtained using the MouseHypothalamusMoffitt2018 method in R package MerfishData, or originally from Dryad via https://doi.org/10.5061/dryad.8t8s248 (ref. 38). The ISSAAAC-seq data can be obtained from https://www.ebi.ac.uk/biostudies/arrayexpress/studies/E-MTAB-11264/. Source data are provided with this paper.

Code availability

The scMultiSim R package is available at https://github.com/ZhangLabGT/scMultiSim/ and on Zenodo via https://doi.org/10.5281/zenodo.14624601 (ref. 56). scMultiSim is also available on Bioconductor via https://bioconductor.org/packages/release/bioc/html/scMultiSim.html. The code for dataset generation and benchmarking is available at https://github.com/ZhangLabGT/scMultiSim_manuscript/ and on Zenodo via https://doi.org/10.5281/zenodo.13626212 (ref. 57).

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Acknowledgements

This work was supported by grants from the National Institutes of Health (R35GM143070 to H.L., Z.Z. and X.Z.), the National Natural Science Foundation of China (32322019 to X.C.) and Guangdong Basic and Applied Basic Research Foundation (2023A1515011662 and 2022B1515120077 to X.C.).

Author information

Authors and Affiliations

  1. Georgia Institute of Technology, Atlanta, GA, USA

    Hechen Li, Ziqi Zhang, Michael Squires & Xiuwei Zhang

  2. Southern University of Science and Technology, Shenzhen, China

    Xi Chen

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Contributions

X.Z. conceived the idea and X.C. contributed to the design of scMultiSim. H.L., Z.Z. and M.S. implemented the software package. H.L. performed validations and benchmarks. H.L., X.Z. and Z.Z. wrote the manuscript.

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Correspondence to Xiuwei Zhang.

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Nature Methods thanks the anonymous reviewers for their contribution to the peer review of this work. Primary Handling Editor: Lin Tang, in collaboration with the Nature Methods team. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Additional Illustration of scMultiSim’s model.

(a) The CIF matrix of size ncell × ncif. If the total number of genes is larger than the those in the GRN, the remaining free (‘non-GRN’) genes will have their tf and ligand-GIV sampled from the user-controlled Gaussian distribution. (b) The GIV matrix of size ncif × ngene, transposed for clarity. Its rows match the columns in the CIF matrix, representing the effect (weight) of each gene to those factors. (c) We perform the same simulation for nstep steps, adding one new cell in each step. Spatial interactions in each step are incorporated. (d) A cell (black) and its neighbors (white) in the grid. The cells in grey are not neighbors.

Extended Data Fig. 2 scMultiSim simulates batch effects and unspliced counts with RNA velocity.

(a) The observed RNA counts in dataset MD9a with added technical noise and batch effects. (b) The spliced true counts, unspliced true counts, and the RNA velocity ground truth from dataset V. The velocity vectors point to the directions of differentiation indicated by red arrows, from the tree root to leaves.

Extended Data Fig. 3 Additional results on technical variation with different capture efficiency α and batch effect size Eb.

Data was simulated using the tree in Fig. 2b, with σi = 1, rd = 0.2, 500 genes and 1000 cells. From the same true counts, various technical noise was added for both continuous and discrete cell population. We show the t-SNE visualization of the gene expression under four configurations α = {0.1, 0.02}, Eb = {1, 2}, and the chromatin accessibility for Eb = {1, 2}. In each grey box, the left sub-figure is colored by cell population ground truth, and the right is colored by batches. With a lower capture efficiency α, one can easily observe the deterioration of data quality in both discrete or continuous trajectories. For example, cluster 3 in (e) is separated from cluster 4 and 5, while in (f) clusters 3, 4 and 5 cannot be differentiated in the visualization; clusters in (a) also have clearer boundaries than (b). The effect of batch effect Eb is also visible in the visualization, where batches are more separated when Eb= 2 in (c,d,g,h). Same observation also applies to the scATAC-seq data.

Extended Data Fig. 4 Additional results on spatial data simulation by scMultiSim.

(a) scMultiSim provides options to control the the cell layout. We show the results of 1200 cells using same-type probability pn = 1.0 and 0.8, respectively. When pn = 1.0, same-type cells tend to cluster together, while pn = 0.8 introduces more randomness. (b) Demonstration of different spatial layouts provided by scMultiSim. Left: the ‘layers’ layout and five cell types. Right: the ‘islands’ layout and four cell types, while specifying cell type 1 and 2 to be ‘islands’. Both datasets were simulated with 1000 cells. (c) Left to right: cells colored by cell types; cells colored by ground truth spatial domains; cells colored by detected spatial domains by STAGATE; cells colored by detected spatial domains by scHybridNMF. (d) Spatially variable genes generated by scMultiSim (from the same dataset with spatial domains) and SRTsim (relative gene expression from its Shiny application). (e) Long-distance CCI with different σrad for the Gaussian kernel. Left: σrad = 1, right: σrad = 5. With a larger σrad, more long-distance CCI are sampled.

Extended Data Fig. 5 Additional results on generated simulated datasets that resemble real datasets.

For all box/violin plots, centers=medians, boxes=Q1-Q3, whiskers= ± 1.5 IQR. We show the statistical properties of both modalities, scRNA-seq and scATAC-seq, for multi-omics datasets (10x Multiome and ISSAAC-seq). For the MERFISH and SeqFISH+ spatial dataset, we show only the RNA modality as it does not have the scATAC-seq data. For SeqFISH+, n= 523 for cells, n= 200 for genes, n= 3000 for ATAC. For MERFISH, n= 3000 for cells, n= 2000 for genes. For ISSAAC-seq, n= 3000 for cells, n= 1000 for genes, n= 3000 for ATAC.

Source data.

Extended Data Fig. 6 Additional results on benchmarking multimodal GRN inference methods.

N=144. For all box/violin plots, centers=medians, boxes=Q1-Q3, whiskers= ± 1.5 IQR. (a) The results on the main dataset (Fig. 5a), with a uniform y axis. (b) Comparison of CellOracle and scMTNI on the main datasets with different noise levels.

Source data.

Extended Data Fig. 7 Additional results on CCI benchmarking, including SpaTalk.

For all box/violin plots, centers=medians, boxes=Q1-Q3, whiskers= ± 1.5 IQR. (a) The GRN and CCI network used in datasets C. (b) Additional results of benchmarking Giotto, SpaOTsc, and SpaTalk on dataset C (Fig. 6b). First row: ROC curves of Giotto, SpaOT and SpaTalk. Second row: PRC curves of Giotto, SpaOT and SpaTalk.

Extended Data Fig. 8 Additional results on benchmarking CCI inference methods.

For all box/violin plots, centers=medians, boxes=Q1-Q3, whiskers= ± 1.5 IQR. (a) Results on the main dataset (Fig. 6a) for each cell population type with the ROC curves (n= 48). Each curve in the ROC plots corresponds to one dataset. (b) Results of benchmarking single-cell CCI inference (Fig. 6c) with ROC curves (n= 8). Each curve in the ROC plots corresponds to one dataset.

Source data.

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Li, H., Zhang, Z., Squires, M. et al. scMultiSim: simulation of single-cell multi-omics and spatial data guided by gene regulatory networks and cell–cell interactions. Nat Methods 22, 982–993 (2025). https://doi.org/10.1038/s41592-025-02651-0

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