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 = NULL,
  r = 50,
  dens = 4,
  k = 3,
  verbatim = TRUE,
  signed = FALSE
)

Source

    The MIT License text for the original package is as follows:
    ---
    Permission is hereby granted, free of charge, to any person obtaining a copy
    of this software and associated documentation files (the "Software"), to deal
    in the Software without restriction, including without limitation the rights
    to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
    copies of the Software, and to permit persons to whom the Software is
    furnished to do so, subject to the following conditions:

    The above copyright notice and this permission notice shall be included in all
    copies or substantial portions of the Software.

    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
    SOFTWARE.
    ---

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",
    "Three bact A",
    "Three bact B"
  ),
  msp_name = c("msp_1", "msp_2", "msp_3", "msp_4", "msp_5", "msp_6"),
  SAMPLE1 = c(0, 1.328425e-06, 0, 1.527688e-07, 0, 0),
  SAMPLE2 = c(1.251707e-07, 0, 3.985320e-07, 0, 1.33607e-04, 0.8675e-03),
  SAMPLE3 = c(0, 0, 4.926046e-09, 5.626392e-06, 0, 0.662e-03),
  SAMPLE4 = c(0, 0, 2.98320e-05, 0, 1.275e-04, 0),
  SAMPLE5 = c(0.0976, 0.9862, 2.98320e-03, 0, 3.9754e-03, 0),
  SAMPLE6 = c(0.26417e-06, 0, 1.0077e-05, 3.983320e-08, 0, 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 6 to 6 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
) %>%
  suppressWarnings()