MOSim is an R package for the simulation of multi-omic bulk and single cell experiments that mimic regulatory mechanisms within the cell.
Gene expression (RNA-seq count data) is the central data type simulated by MOSim, while the rest of available omic data types
provide gene regulation information. For bulk simulation, regulators include ATAC-seq (DNase-seq), ChIP-seq, miRNA-seq and Methyl-seq. In addition to these omics,
regulation by transcription factors (TFs) can also be modeled. While for single-cell simulation, the regulators included are scATAC-seq and TFs.
MOSim has great flexibility in defining experimental designs, DEGs, and active regulators making it a versatile tool for a variety of different applications:
i) validating methods aimed at modelling complex, multi-layered regulatory networks, ii) benchmarking multi-omics data integration pipelines,
iii) benchmarking GRN inference tools, iv) evaluating differential expression and accessibility analysis tools, v) testing single-cell data clustering methods,
vi) evaluating multi-omics visualization tools etc.
MOSim is a Bioconductor R package, and we strongly recommend that it is installed from the Bioconductor repository. To install MOSim, open the R console and run:
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("MOSim")
The developer version (which now includes the sc_mosim functionalities) can be installed from GitHub using the devtools R package:
install.packages("devtools")
devtools::install_github("ConesaLab/MOSim")
Vignettes and documentation can be accessed from MOSim's Bioconductor site, or by running the following line in the R console:
browseVignettes("MOSim")
If you used MOSim for your research, please cite:
- Monzó C, Martínez C, Arzalluz-Luque A, Conesa A, Tarazona S (2024). MOSim: bulk and single-cell multi-layer regulatory network simulator. BioRxiv. DOI: 10.1101/421834
sc_mosim strongly relies on functionality from SPARSim. If you use the sc_mosim module to simulate multi-omics single cell data, please also cite:
- Baruzzo G, Patuzzi I, Di Camillo B (2020). SPARSim single cell: a count data simulator for scRNA-seq data. Bioinformatics, Volume 36, Issue 5, Pages 1468-1475. DOI: 10.1093/bioinformatics/btz752