Introduction

Welcome to the MOSim package, a versatile tool for simulating bulk and single-cell multi-omics data. In this vignette, we will explore how to create synthetic single-cell data, focusing on single-cell RNA-seq (scRNA-seq) and single-cell ATAC-seq (scATAC-seq) data. Using the MOSim package, you can generate custom multi-omics datasets for various experimental conditions, making it an essential resource for testing and validating analysis methods, or creating benchmark datasets.

Installation

Before we dive into the exciting world of data simulation, you’ll need to install the MOSim package. You can easily obtain it from CRAN using the following commands:

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

BiocManager::install("MOSim")

# For the latest development version
install.packages("devtools")
devtools::install_github("ConesaLab/MOSim")

Simulating Single-Cell Multi-Omics Data

The core of data simulation lies in the scMOSim function, which allows you to create synthetic single-cell multi-omics data. Let’s explore a typical example of its usage, using the default dataset loaded in the package:

library(MOSim)

# Create a list of omics data types (e.g., scRNA-seq and scATAC-seq)
omicsList <- sc_omicData(list("scRNA-seq", "scATAC-seq"), 
                         data = NULL)

# Define cell types for your experiment
cell_types <- list('Treg' = c(1:10),'cDC' = c(11:20),'CD4_TEM' = c(21:30),
      'Memory_B' = c(31:40))

# Load an association list containing peak IDs related to gene names
associationList <- data(associationList)

# Simulate multi-omics data with specific parameters
testing_groups <- scMOSim(
  omicsList,
  cell_types,
  numberReps = 2,
  numberGroups = 3,
  diffGenes = list(c(0.2, 0.3), c(0.2, 0.3)),
  minFC = 0.25,
  maxFC = 4,
  numberCells = NULL,
  mean = NULL,
  sd = NULL,
  regulatorEffect = list(c(0.1, 0.2), c(0.1, 0.2), c(0.1, 0.2)),
  associationList = associationList
)

In the example above, we load omics data types, specify experimental conditions and cell types, and load an association list. The scMOSim function lets us simulate multi-omics data with various parameters, such as the number of replicates, differentially expressed genes, and regulatory effects.

Data Preparation

Before diving into simulation, it’s essential to have your data ready. The sc_omicData function aids in preparing your data for simulation. It accepts the following inputs:

• omics_types: A list of omics data types, which can be “scRNA-seq” or “scATAC-seq.”

• data (optional): A user-inputted list of matrices with features as rows and cells as columns. If data is NULL, the default data from 10 cells for 4 celltypes is loaded.

Providing Custom Data

scMOSim also allows you to simulate data resembling characteristics of a dataset of your choice. To do so, you need to format your data using the sc_omicData function. Supported input formats include:

• Count matrix
• Seurat array
  For example, to format your data extracted from the original Seurat object,
  you can follow these steps:

# This is done to get a dataset to extract a matrix from (for example purposes)
scRNA <- MOSim::sc_omicData("scRNA-seq", data = NULL)
count <- scRNA[["scRNA-seq"]]
options(Seurat.object.assay.version = "v3")
Seurat_obj <- Seurat::CreateAssayObject(counts = count, assay = 'RNA')
omic_list_user <- sc_omicData(c("scRNA-seq"), data = c(Seurat_obj))

The resulting omic_list_user is a named list with “scRNA-seq” as the name and your count matrix as the value.

Running the Simulation: scMOSim

Default scMOSim Simulation

scMOSim can simulate scRNA and scATAC count matrices without providing any additional arguments. For a basic simulation, you only need to input the omics list and cell types. Here’s how it’s done:

omic_list <- sc_omicData(list("scRNA-seq"))
cell_types <- list('Treg' = c(1:10),'cDC' = c(11:20),'CD4_TEM' = c(21:30),
      'Memory_B' = c(31:40))

sim <- scMOSim(omic_list, cell_types)

This will result in simulated raw count matrices for scRNA.

Customizing the scMOSim Simulation

The scMOSim function offers a range of parameters to fine-tune your simulation:

• omics: A named list specifying the omics data types.
• cellTypes: A list specifying the cell types.
• numberReps (optional): The number of biological replicates.
• numberGroups (optional): The number of different biological groups (experimental conditions).
• diffGenes (optional): To simulate differentially expressed genes between groups.
• minFC (optional): Threshold for downregulated genes.
• maxFC (optional): Threshold for upregulated genes.
• numberCells (optional): A vector specifying the number of cells per cell type.
• mean and sd (optional): Vectors for mean and standard deviation of expression per cell type.
• noiseRep (optional): Standard deviation between biological replicates.
• noiseGroup (optional): Standard deviation between groups.
• feature_no (optional): Total number of features to distribute between co-expression clusters.
• clusters (optional): Number of co-expression patterns to simulate.
• cluster_size (optional): Number of features in each co-simulation cluster.
  For example, to simulate data with specific settings, you can use the following
  code:

omic_list <- sc_omicData(c("scRNA-seq", "scATAC-seq"))
cell_types <- list('Treg' = c(1:10),'cDC' = c(11:20),'CD4_TEM' = c(21:30),
      'Memory_B' = c(31:40))
sim <- scMOSim(omic_list, cell_types, numberReps = 2, 
               numberGroups = 2, diffGenes = list(c(0.2, 0.3)), feature_no = 8000, 
               clusters = 3, mean = c(2*10^6, 1*10^6,2*10^6, 1*10^6), 
               sd = c(5*10^5, 2*10^5, 5*10^5, 2*10^5), 
               regulatorEffect = list(c(0.1, 0.2), c(0.1, 0.2)))

Working with Simulation Results

The scMOSim Simulation Object

The result of your simulation is stored in a named list with ‘sim_sc + omic name’ as names and Seurat objects as values. Each Seurat object contains the synthetic count matrices for your experiment. Other relevant information included in the object are:

• cellTypes: A list specifying the columns in each simulated matrix that correspond to each cell type.

• patterns: Matrix of co-expression patterns affecting the genes throughout cell-types.

• FC: list of Fold Changes applied to each gene to simulate differential expression between experimental groups.

• AssociationMatrices: gene/peak association matrices including differential expression and regulatory relationships.

• Variability: added variability matrices to add dispersion to experimental groups and biological replicates.

Retrieving Simulation Settings

To access simulation settings and other constraints for simulation, you can use the scOmicSettings function. This provides information about the relationship between genes and peaks, differentially expressed genes, regulator types, expression patterns, and fold changes for each gene and peak compared to group 1.

settings <- scOmicSettings(sim)

Accessing the Count Data Matrices

You can extract the simulated matrices for all experimental conditions and biological replicates using the scOmicResults function. This provides you with the synthetic data for further analysis and visualization.

res <- scOmicResults(sim)