R/simulate_data.R

In MOSS: Multi-Omic Integration via Sparse Singular Value Decomposition

#' Simple simulation of regulatory modules.
#'
#' This a simple simulation to use in MOSS' examples.
#' The specifics of the simulation are shown in the "Examples" section.
#' @param moss_seed The seed for random number generator.
#' Numeric. Defaults to 42.
#' @return
#' A list of two elements 'sim_blocks' and 'labels'.
#' First element 'sim_blocks' is a list of three numeric matrices,
#'  and one character matrix.
#'   Second element 'labels' has two character vectors.
#' The first element 'lab.sub' identifies the groups of 'signal' subjects.
#' The second element 'lab.feat' identifies the groups 'signal' features
#'  from background 'noise'.
#' @export
#' @examples
#' sim_data <- simulate_data()
#'
#' # Extracting simulated omic blocks.
#' sim_blocks <- sim_data$sim_blocks
#'
#' # Extracting subjects and features labels.
#' lab.sub <- sim_data$labels$lab.sub
#' lab.feat <- sim_data$labels$lab.feat
#'
#' # Check dimensions and objects class.
#' lapply(sim_blocks, dim)
#' lapply(sim_blocks, function(x) class(x[, 1]))
#'
#' # Showing how the data was generated.
#' set.seed(42)
#' O1 <- matrix(data = 0, nrow = 5e2, ncol = 1e3)
#' O2 <- O1
#' O1[1:20, 1:150] <- 1
#' O1 <- O1 + rnorm(5e5, mean = 0, sd = 0.5)
#' O2[71:90, 71:200] <- 1
#' O2 <- O2 + rnorm(5e5, mean = 0, sd = 0.5)
#' # Simulating a continous response blocks.
#' O3 <- 3 * O1 - 5 * O2 + rnorm(5e5, mean = 0, sd = 0.5)
#'
#' # Creating a vector labeling clusters of subjects.
#' aux <- rep("Background", 500)
#' aux[1:20] <- "Group 1"
#' aux[71:90] <- "Group 2"
#' all.equal(aux, lab.sub)
#'
#' # Generating a classification response.
#' O4 <- as.matrix(aux)
#'
#' # Storing all blocks within a list.
#' all.equal(sim_blocks, list(
#'   "Block 1" = O1,
#'   "Block 2" = O2,
#'   "Block 3" = O3,
#'   "Block 4" = O4
#' ))
#'
#' # Creating a vector labeling signal and background features.
#' aux <- rep("Background features", 3000)
#' aux[c(1:150, 1072:1200, 2001:2200)] <- "Signal features"
#' all.equal(aux, lab.feat)
simulate_data <- function(moss_seed = 42) {
  # Simple simulation of three omic blocks and a single categorical variable.
  set.seed(moss_seed)
  O1 <- matrix(data = 0, nrow = 5e2, ncol = 1e3)
  O2 <- O1
  O1[1:20, 1:150] <- 1
  O1 <- O1 + stats::rnorm(5e5, mean = 0, sd = 0.5)

  O2[71:90, 71:200] <- 1
  O2 <- O2 + stats::rnorm(5e5, mean = 0, sd = 0.5)

  # Simulating a continous response blocks.
  O3 <- 3 * O1 - 5 * O2 + stats::rnorm(5e5, mean = 0, sd = 0.5)

  # Creating a vector labeling clusters of subjects.
  lab.sub <- rep("Background", 500)
  lab.sub[1:20] <- "Group 1"
  lab.sub[71:90] <- "Group 2"

  # Generating a classification response.
  O4 <- as.matrix(lab.sub)

  # Storing all blocks within a list.
  sim_blocks <- list(
    "Block 1" = O1,
    "Block 2" = O2,
    "Block 3" = O3,
    "Block 4" = O4
  )

  # Creating a vector labeling signal and background features.
  lab.feat <- rep("Background features", 3000)
  lab.feat[c(1:150, 1072:1200, 2001:2200)] <- "Signal features"

  # Storing simulated data in a global variable.

  sim_data <- list(
    "sim_blocks" = sim_blocks,
    "labels" = list(
      "lab.sub" = lab.sub,
      "lab.feat" = lab.feat
    )
  )
  return(sim_data)
}