1. Getting Started

scMultiSim is a simulation tool for single-cell multi-omics data. It can simulate RNA counts, ATAC-seq data, RNA velocity, and spatial locations of continuous or discrete cell populations. It can model the effects of gene regulatory networks (GRN), chromatin accessibility, and cell-cell interactions on the simulated data.

This article introduces the basic workflow of scMultiSim.

Installation

It is recommended to install scMultiSim from Bioconductor with:

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("scMultiSim")

You can also install the development version of scMultiSim from GitHub with:

devtools::install_github("ZhangLabGT/scMultiSim@main")

Running Simulation

Once installed, you can load the package with:

library(scMultiSim)

A typical workflow consists two main steps:

  1. Simulate the true counts;
  2. Add technical noise and batch to the dataset.

The sim_true_counts function generates the true counts. It accepts a list of options as input. You are able to control most of the simulated effects here.

data(GRN_params_100)

results <- sim_true_counts(list(
  # required options
  GRN = GRN_params_100,
  tree = Phyla3(),
  num.cells = 500,
  # optional options
  num.cif = 20,
  discrete.cif = F,
  cif.sigma = 0.1
  # ... other options
))
#> Time spent: 0.06 mins

scMultiSim requires users to provide the following options:

  • GRN: The Gene Regulatory Network.
  • tree: The cell differential tree.

Typically, you may also want to adjust the following options to control other important factors:

  • num.cells: Specify the number of cells.
  • unregulated.gene.ratio or num.genes: Control the total number of genes.
  • discrete.cif: Whether generating discrete or continuous cell population.
  • diff.cif.fraction: Control the contribution of the trajectory/cluster specified by the tree.
  • cif.sigma: Control the variation of cells along the trajectory.

The Simulating Multimodal Single-Cell Data tutorial will introduce these functions in more detail, including how to simulate RNA velocity data and ATAC-seq data. The Simulating Spatial Cell-Cell Interactions tutorial will focus on simulating spatial cell locations and cell-cell interactions. You may also want to check the Parameter Guide or running the scmultisim_help() function for a complete list of options.

The Shiny app

Don’t forget that scMultiSim provides a Shiny app to help you explore the options interactively. Simply run run_shiny() to start the app.

run_shiny()

Add technical noise and batch effect

You can use add_expr_noise to add technical noise to the dataset, and divide_batches to add batch effects.

add_expr_noise(results)
#> Adding experimental noise...
#> 50..100..150..200..250..300..350..400..450..500..Using atac_counts
#> Time spent: 0.11 mins
divide_batches(results, nbatch = 2)
#> Adding batch effects...

Visualize the results

scMultiSim provides various visualization functions to help you understand the simulated data.

For example, plot_tsne() visualizes the cells using t-SNE.

plot_tsne(results$counts, results$cell_meta$pop)