`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) )

model | A dyngen intermediary model for which the gold standard been generated with |
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burn_time | The burn in time of the system, used to determine an initial state vector. If |

total_time | The total simulation time of the system. If |

ssa_algorithm | Which SSA algorithm to use for simulating the cells with |

census_interval | A granularity parameter for the outputted simulation. |

experiment_params | A tibble generated by rbinding multiple calls of |

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 |

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. |

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. |

A dyngen model.

dyngen on how to run a complete dyngen simulation

#> #>#>#> #>#>#> #>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.# }