## ----installing, eval = FALSE-------------------------------------------------
#  if (!requireNamespace("BiocManager", quietly = TRUE))
#      install.packages("BiocManager")
#  
#  BiocManager::install("MOSim")
#  
#  # For the latest development version
#  install.packages("devtools")
#  devtools::install_github("ConesaLab/MOSim")
#  

## ----test_run, eval = FALSE---------------------------------------------------
#  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 <- sc_mosim(
#    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,
#    TF = FALSE
#  )

## ----custom data, eval = FALSE------------------------------------------------
#  # This is done to get a dataset to extract a matrix (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')
#  # To format the data into sc_mosim-ready format, we pass the seurat
#  # object containing the count data we extracted into sc_omicData
#  omic_list_user <- sc_omicData(c("scRNA-seq"), data = c(Seurat_obj))

## ----default, eval = FALSE----------------------------------------------------
#  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 <- sc_mosim(omic_list, cell_types, TF = TRUE)

## ----testing, eval = FALSE----------------------------------------------------
#  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 <- sc_mosim(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)), TF = FALSE)

## ----settings, eval = FALSE---------------------------------------------------
#  settings <- sc_omicSettings(sim)

## ----settingsTF, eval = FALSE-------------------------------------------------
#  settings <- sc_omicSettings(sim, TF = TRUE)

## ----results, eval = FALSE----------------------------------------------------
#  res <- sc_omicResults(sim)

