ArticleNature methods2025
scMultiSim: simulation of single-cell multi-omics and spatial data guided by gene regulatory networks and cell-cell interactions.
Article in Nature methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- SpaMOAL is a deep learning method that enables accurate spatial domain identification from multi-omics data.PLoS biology · 2026Article
- SignifiKANTE: efficient P-value computation for gene regulatory networks.Bioinformatics (Oxford, England) · 2026Article
- Unified representation learning for spatial multi-omics.Bioinformatics (Oxford, England) · 2026Article
- Integrating Multi-View Features via Deep Generalized Canonical Correlation Analysis for Single-Cell Clustering.International journal of molecular sciences · 2026Article
- CeSpGRN: inferring cell-specific gene regulatory networks from single-cell multi-omics and spatial data.Bioinformatics (Oxford, England) · 2026Article
- Spatially multimodal and multiscale network for representation learning from spatial multi-omics.BMC genomics · 2026Article
- SCMBench: benchmarking domain-specific and foundation models for single-cell multi-omics data integration.Nature communications · 2026Article
- SCOPE: Localizing fate-decision states and their regulatory drivers in single-cell differentiation.bioRxiv : the preprint server for biology · 2026Article
- scSCCNIA: similarity matrix based contrastive clustering with neighbor information aggregation for single-cell RNA sequencing data.Briefings in bioinformatics · 2026Article
- BEASTsim-a benchmarking and analysis platform for spatial transcriptomics simulations.Briefings in bioinformatics · 2026Article
- Novel African American Colorectal CancerHuman mutation · 2026Article
- Cognitive impairment after cerebral ischemia-reperfusion injury: a neuroecosystem perspective.Frontiers in neurology · 2026Review
- GeneSNAKE: a Python package for simulation of gene regulatory networks and perturbation-induced expression data.Bioinformatics advances · 2026Article
- Spatially varying gene regulation network inference from spatial transcriptomics.Bioinformatics advances · 2026Article
- Quantum Generative Modeling of Single-Cell transcriptomes: Capturing Gene-Gene and Cell-Cell Interactions.ArXiv · 2025Article
- From features to slice: parameter-cloud modeling of spatial transcriptomics for simulation and 3D interpolatory augmentation.bioRxiv : the preprint server for biology · 2025Article
- Single-cell multi-omics in biliary tract cancers: decoding heterogeneity, microenvironment, and treatment strategies.Molecular biomedicine · 2025Review
- sCCIgen: a high-fidelity spatially resolved transcriptomics data simulator for cell-cell interaction studies.Genome biology · 2025Article
- simPIC:flexible simulation of paired-insertion counts for single-cell ATAC sequencing data.bioRxiv : the preprint server for biology · 2025Article
- Paradigms, innovations, and biological applications of RNA velocity: a comprehensive review.Briefings in bioinformatics · 2025Review
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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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.