run_simulation: Run data simulation
In biqing-zhu/MARBLES: What the Package Does (One Line, Title Case)

run_simulation

R Documentation

Run data simulation

Simulation of complex scRNA-seq data based on the sampled underlying truth X

run_simulation(
  x,
  nc = 2000,
  ns = 3,
  nk,
  ng = nrow(x),
  de_perc,
  lfc = 2,
  c_c,
  gamma,
  beta,
  iter = 5
)

`x`	a `SingleCellExperiment`.
`nc`	number of cells to simulate.
`ns`	number of samples to simulate.
`nk`	number of cell types to simulate.
`ng`	number of genes to simulate.
`de_perc`	percentage of cell types to be DE when initializing X0.
`lfc`	numeric value to use as mean logFC (logarithm base 2) for DE genes.
`c_c`	cell type relationship network matrix. 1 means connected, and 0 means not connected.
`iter`	number of iterations when sampling X.
`\eqn{\gamma}`, `\eqn{\beta}`	`\Phi` used for sampling the state matrix X.

The estimated model parameters and the DE status

a SingleCellExperiment containing multiple clusters & samples across two groups as well as the following metadata:

cell metadata (colData(.))

a DataFrame containing, for each cell, it's cluster, sample, and group ID.

experiment metadata (metadata(.))

experiment_info: a data.frame summarizing the experimental design.
n_cells: the number of cells for each sample.
gene_info: a data.frame containing, for each gene in each cluster, it's differential distribution category, mean logFC (NA for genes for categories "ee"), gene used as reference (sim_gene), dispersion sim_disp, and simulation means for each group sim_mean.A/B.
ref_sids/kids: the sample/cluster IDs used as reference.
args: a list of the function call's input arguments.