Simulate data from some empirical count dataset with a "cluster-like" structure

Usage

new_synth_data(
  real_data,
  graph_type = "cluster",
  must_connect = TRUE,
  graph = NULL,
  n = 300,
  seed = 10010,
  r = 50,
  dens = 4,
  k = 3,
  verbatim = TRUE,
  signed = FALSE
)

Arguments

real_data: Matrix. Empirical count table
graph_type: String. Structure type for the conditional dependency structure. Here only "cluster" was kept, see EMtree package for more options
must_connect: Boolean. TRUE to force the output graph to be connected
graph: Boolean. Optional graph to be used, must have rownames and colnames and reference all features from real_data
n: Numeric. Number of samples to simulate
seed: Numeric. Seed number for data generation (rmvnorm)
r: Numeric. For cluster structure, controls the within/between ratio connection probability
dens: Numeric. Graph density (for cluster graphs) or edges probability (for erdös-renyi graphs)
k: Numeric. For cluster structure, number of groups
verbatim: Boolean. Controls verbosity
signed: Boolean. TRUE for simulating both positive and negative partial correlations. Default is to FALSE, which implies only negative partial correlations

Value

List. Containing the simulated discrete counts, the corresponding true partial correlation matrix from the latent Gaussian layer of the model and the original graph structure that was used

Examples

tiny_data <- data.frame(
  species = c("One bacteria", "One bacterium L", "One bacterium G", "Two bact"),
  msp_name = c("msp_1", "msp_2", "msp_3", "msp_4"),
  SAMPLE1 = c(0, 1.328425e-06, 0, 1.527688e-07),
  SAMPLE2 = c(1.251707e-07, 1.251707e-07, 3.985320e-07, 0),
  SAMPLE3 = c(0, 0, 4.926046e-09, 5.626392e-06),
  SAMPLE4 = c(0, 0, 2.98320e-05, 0)
)

count_table <- get_count_table(abund.table = tiny_data %>% dplyr::select(-species), sample.id = colnames(tiny_data), prev.min = 0.1)
#> Preprocessing step output for species prevalence>10% : 
#>    -from 4 to 4 species
#>    -from 50% to 50% zero values.
tiny_graph <- graph_step(tiny_data, col_module_id = "msp_name", annotation_level = "species", seed = 20242025) %>% suppressWarnings()
sim_data <- new_synth_data(count_table$data, n = 50, graph = as.matrix(tiny_graph %>% dplyr::select(-species)), verbatim = FALSE, seed = 20242025)