genMixedCellProp: Generate training and test cell type composition matrices

Description

Generate training and test cell type composition matrices for the simulation of mixed transcriptional profiles with known cell composition using single-cell expression profiles. The resulting PropCellTypes object will contain all the information needed to simulate new mixed transcriptional profiles. Note this function does not simulate the mixed profiles, this task is performed by the simMixedProfiles or trainDeconvModel functions (see Documentation).

Usage

genMixedCellProp(
  object,
  cell.ID.column,
  cell.type.column,
  num.sim.spots,
  n.cells = 50,
  train.freq.cells = 3/4,
  train.freq.spots = 3/4,
  proportion.method = c(0, 0, 1),
  prob.sparity = 1,
  min.zero.prop = NULL,
  balanced.type.cells = TRUE,
  verbose = TRUE
)

Value

A SpatialDDLS object with prob.cell.types

slot containing a list with two PropCellTypes

objects (training and test). For more information about the structure of this class, see ?PropCellTypes.

Arguments

object: SpatialDDLS object with single.cell.real slot and, optionally, with single.cell.simul slot.
cell.ID.column: Name or column number corresponding to cell names in cells metadata.
cell.type.column: Name or column number corresponding to cell types in cells metadata.
num.sim.spots: Number of mixed profiles to be simulated. It is recommended to adjust this number according to the number of available single-cell profiles.
n.cells: Specifies the number of cells to be randomly selected and combined to generate the simulated mixed profiles. By default, it is set to 50 It controls the level of noise present in the simulated data, as it determines how many single-cell profiles will be combined to produce each spot.
train.freq.cells: Proportion of cells used to simulate training mixed transcriptional profiles (3/4 by default).
train.freq.spots: Proportion of mixed transcriptional profiles to be used for training, relative to the total number of simulated spots (num.sim.spots). The default value is 3/4.
proportion.method: Vector with three elements that controls the proportion of simulated proportions generated by each method: random sampling of a Dirichlet distribution, "pure" spots (1 cell type), and spots generated from a random sampling of a Dirichlet distribution but with a specified number of different cell types (determined by min.zero.prop), respectively. By default, all samples are generated by the last method.
prob.sparity: It only affects the proportions generated by the first method (Dirichlet distribution). It determines the probability of having missing cell types in each simulated spot, as opposed to a mixture of all cell types. A higher value for this parameter will result in more sparse simulated samples.
min.zero.prop: This parameter controls the minimum number of cell types that will be absent in each simulated spot. If NULL (by default), this value will be half of the total number of different cell types, but increasing it will result in more spots composed of fewer cell types. This helps to create more sparse proportions and cover a wider range of situations during model training.
balanced.type.cells: Boolean indicating whether training and test cells will be split in a balanced way considering cell types (TRUE by default).
verbose: Show informative messages during the execution (TRUE by default).

Details

First, the single-cell profiles are randomly divided into two subsets, with 2/3 of the data for training and 1/3 for testing. The default setting for this ratio can be changed using the train.freq.cells parameter. Next, a total of num.sim.spots mixed proportions are simulated using a Dirichlet distribution. This simulation takes into account the probability of missing cell types in each spot, which can be adjusted using the prob.sparity parameter. For each mixed sample, n.cells single-cell profiles are randomly selected and combined to generate the simulated mixed sample. In addition to the Dirichlet-based proportions, pure spots (containing only one cell type) and spots containing a specified number of different cell types (determined by the min.zero.prop parameter) are also generated in order to cover situations with only a few cell types present. The proportion of simulated spots generated by each method can be controlled using the proportion.method parameter. To visualize the distribution of cell type proportions generated by each method, the showProbPlot function can be used.

Examples

Run this code

set.seed(123)
sce <- SingleCellExperiment::SingleCellExperiment(
  assays = list(
    counts = matrix(
      rpois(100, lambda = 5), nrow = 40, ncol = 30,
      dimnames = list(paste0("Gene", seq(40)), paste0("RHC", seq(30)))
    )
  ),
  colData = data.frame(
    Cell_ID = paste0("RHC", seq(30)),
    Cell_Type = sample(x = paste0("CellType", seq(4)), size = 30,
                       replace = TRUE)
  ),
  rowData = data.frame(
    Gene_ID = paste0("Gene", seq(40))
  )
)

SDDLS <- createSpatialDDLSobject(
  sc.data = sce,
  sc.cell.ID.column = "Cell_ID",
  sc.gene.ID.column = "Gene_ID",
  sc.filt.genes.cluster = FALSE,
  project = "Simul_example"
)
SDDLS <- genMixedCellProp(
  object = SDDLS,
  cell.ID.column = "Cell_ID",
  cell.type.column = "Cell_Type",
  num.sim.spots = 10,
  train.freq.cells = 2/3,
  train.freq.spots = 2/3,
  verbose = TRUE
)