This function contains the complete pipeline for generating a dataset with dyngen. In order to have more control over how the dataset is generated, run each of the steps in this function separately.
generate_dataset(
model,
format = c("list", "dyno", "sce", "seurat", "anndata", "none"),
output_dir = NULL,
make_plots = FALSE,
store_dimred = model$simulation_params$compute_dimred,
store_cellwise_grn = model$simulation_params$compute_cellwise_grn,
store_rna_velocity = model$simulation_params$compute_rna_velocity
)A dyngen initial model created with initialise_model().
Which output format to use, must be one of 'dyno' (requires dynwrap), 'sce' (requires SingleCellExperiment), 'seurat' (requires Seurat), 'anndata' (requires anndata), 'list' or 'none'.
If not NULL, then the generated model and dynwrap
dataset will be written to files in this directory.
Whether or not to generate an overview of the dataset.
Whether or not to store the dimensionality reduction constructed on the true counts.
Whether or not to also store cellwise GRN information.
WHether or not to store the log propensity ratios.
A list containing a dyngen model (li$model) and a dynwrap dataset (li$dataset).
model <-
initialise_model(
backbone = backbone_bifurcating()
)
# \dontshow{
# actually use a smaller example
# to reduce execution time during
# testing of the examples
model <- initialise_model(
backbone = model$backbone,
num_cells = 5,
num_targets = 0,
num_hks = 0,
gold_standard_params = gold_standard_default(census_interval = 1, tau = 0.1),
simulation_params = simulation_default(
burn_time = 10,
total_time = 10,
census_interval = 1,
ssa_algorithm = ssa_etl(tau = 0.1),
experiment_params = simulation_type_wild_type(num_simulations = 1)
)
)
# }
# \donttest{
# generate dataset and output as a list format
# please note other output formats exist: "dyno", "sce", "seurat", "anndata"
out <- generate_dataset(model, format = "list")
#> Generating TF network
#> Sampling feature network from real network
#> Generating kinetics for 36 features
#> Generating formulae
#> Generating gold standard mod changes
#> Precompiling reactions for gold standard
#> Running gold simulations
#>
| | 0 % elapsed=00s
|======== | 14% elapsed=00s, remaining~00s
|=============== | 29% elapsed=00s, remaining~00s
|====================== | 43% elapsed=00s, remaining~00s
|============================= | 57% elapsed=00s, remaining~00s
|==================================== | 71% elapsed=00s, remaining~00s
|=========================================== | 86% elapsed=00s, remaining~00s
|==================================================| 100% elapsed=00s, remaining~00s
#> Precompiling reactions for simulations
#> Running 1 simulations
#> Mapping simulations to gold standard
#> Warning: Simulation does not contain all gold standard edges. This simulation likely suffers from bad kinetics; choose a different seed and rerun.
#> Performing dimred
#> Simulating experiment
#> Warning: Certain backbone segments are not covered by any of the simulations. If this is intentional, please ignore this warning.
#> Otherwise, increase the number of simulations (see `?simulation_default`) or decreasing the census interval (see `?simulation_default`).
#> Wrapping dataset as list
model <- out$model
dataset <- out$dataset
# }