generate_cells | R Documentation |
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 |
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
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)
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