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Simulate artifical mixtures from bulk proteome measurements

Source: R/simulate_data.R
simulate_data.Rd

This function simulates bulk proteome data by mixing bulk sample measurements.

Usage

simulate_data(
  data,
  cell_types,
  seed = NULL,
  ncells = 100,
  nsamples = 100,
  filter_genes = TRUE,
  scenario = "random",
  whitelist = NULL,
  blacklist = NULL
)

Arguments

data

A numeric matrix containing protein abundance data with protein identifiers as row names and samples as columns.

cell_types

A character vector indicating the cell type associated with each column in the input matrix. Must have the same length as the number of columns in data.

seed

An integer used as random seed for reproducibility. Default is NULL (no seed).

ncells

Integer specifying the number of cells to use for each simulated bulk sample.

nsamples

Integer specifying the number of bulk samples to simulate. Default is 100.

filter_genes

Logical; whether to filter out proteins/genes with low expression before simulation. Default is TRUE.

scenario

String specifying the simulation scenario:

  • "random" (default): Random cell type proportions for each sample

  • "even": Even proportions of all cell types

  • See SimBu documentation for additional scenarios

whitelist

Optional character vector of cell types to include in the simulation. If provided, only these cell types will be used. Default is NULL (use all).

blacklist

Optional character vector of cell types to exclude from the simulation. Default is NULL (exclude none).

Value

A list containing two matrices:

  • simulated_data: Matrix of simulated bulk protein abundance data (proteins as rows, simulated samples as columns)

  • cell_fractions: Matrix of cell type fractions used for each simulation (cell types as rows, simulated samples as columns)

Details

This function uses the SimBu package to generate synthetic bulk samples by artifically mixing samples.

Examples

# Create example data
cell_data <- matrix(abs(rnorm(1500, mean = 500, sd = 200)), nrow = 100, ncol = 15)
rownames(cell_data) <- paste0("Protein", 1:100)
colnames(cell_data) <- paste0("Cell", 1:15)

# Define cell types
cell_types <- rep(c("T_cell", "B_cell", "Monocyte"), each = 5)

# Run simulation
if (FALSE) { # \dontrun{
sim_results <- simulate_data(
  data = cell_data,
  cell_types = cell_types,
  seed = 42,
  nsamples = 20,
  scenario = "random"
)

dim(sim_results$simulated_data)
dim(sim_results$cell_fractions)
} # }