R: Simulate the cells

generate_cells {dyngen}

R Documentation

Simulate the cells

Description

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

Usage

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)
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)
      )
    )
  )

data("example_model")
model <- example_model %>% generate_cells()
  
plot_simulations(model)
plot_gold_mappings(model)
plot_simulation_expression(model)