Simulate the cells

generate_cells() runs simulations in order to determine the gold standard of the simulations. simulation_default() is used to configure parameters pertaining this process.

generate_cells(model)

simulation_default(
  burn_time = NULL,
  total_time = NULL,
  ssa_algorithm = ssa_etl(tau = 30/3600),
  census_interval = 4,
  experiment_params = bind_rows(simulation_type_wild_type(num_simulations = 32),
    simulation_type_knockdown(num_simulations = 0)),
  store_reaction_firings = FALSE,
  store_reaction_propensities = FALSE,
  compute_cellwise_grn = FALSE,
  compute_dimred = TRUE,
  compute_rna_velocity = FALSE,
  kinetics_noise_function = kinetics_noise_simple(mean = 1, sd = 0.005)
)

simulation_type_wild_type(
  num_simulations,
  seed = sample.int(10 * num_simulations, num_simulations)
)

simulation_type_knockdown(
  num_simulations,
  timepoint = runif(num_simulations),
  genes = "*",
  num_genes = sample(1:5, num_simulations, replace = TRUE, prob = 0.25^(1:5)),
  multiplier = runif(num_simulations, 0, 1),
  seed = sample.int(10 * num_simulations, num_simulations)
)

Arguments

model: A dyngen intermediary model for which the gold standard been generated with generate_gold_standard().
burn_time: The burn in time of the system, used to determine an initial state vector. If NULL, the burn time will be inferred from the backbone.
total_time: The total simulation time of the system. If NULL, the simulation time will be inferred from the backbone.
ssa_algorithm: Which SSA algorithm to use for simulating the cells with GillespieSSA2::ssa()
census_interval: A granularity parameter for the outputted simulation.
experiment_params: A tibble generated by rbinding multiple calls of simulation_type_wild_type() and simulation_type_knockdown().
store_reaction_firings: Whether or not to store the number of reaction firings.
store_reaction_propensities: Whether or not to store the propensity values of the reactions.
compute_cellwise_grn: Whether or not to compute the cellwise GRN activation values.
compute_dimred: Whether to perform a dimensionality reduction after simulation.
compute_rna_velocity: Whether or not to compute the propensity ratios after simulation.
kinetics_noise_function: A function that will generate noise to the kinetics of each simulation. It takes the feature_info and feature_network as input parameters, modifies them, and returns them as a list. See kinetics_noise_none() and kinetics_noise_simple().
num_simulations: The number of simulations to run.
seed: A set of seeds for each of the simulations.
timepoint: The relative time point of the knockdown
genes: Which genes to sample from. "*" for all genes.
num_genes: The number of genes to knockdown.
multiplier: The strength of the knockdown. Use 0 for a full knockout, 0<x<1 for a knockdown, and >1 for an overexpression.

Value

A dyngen model.

Examples

library(dplyr)
#> 
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#> 
#>     filter, lag
#> The following objects are masked from ‘package:base’:
#> 
#>     intersect, setdiff, setequal, union
model <- 
  initialise_model(
    backbone = backbone_bifurcating(),
    simulation = simulation_default(
      ssa_algorithm = ssa_etl(tau = .1),
      experiment_params = bind_rows(
        simulation_type_wild_type(num_simulations = 4),
        simulation_type_knockdown(num_simulations = 4)
      )
    )
  )
# \donttest{
data("example_model")
model <- example_model %>% generate_cells()
#> Warning: Simulation does not contain all gold standard edges. This simulation likely suffers from bad kinetics; choose a different seed and rerun.
  
plot_simulations(model)

plot_gold_mappings(model)

plot_simulation_expression(model)

# }

Arguments

Value

See also

Examples