Function reference • dyngen

Step 1, initialise dyngen model Define and store settings for all following steps. See each of the sections below for more information.
`initialise_model()`	Initial settings for simulating a dyngen dataset
`list_backbones()` `backbone_bifurcating()` `backbone_bifurcating_converging()` `backbone_bifurcating_cycle()` `backbone_bifurcating_loop()` `backbone_branching()` `backbone_binary_tree()` `backbone_consecutive_bifurcating()` `backbone_trifurcating()` `backbone_converging()` `backbone_cycle()` `backbone_cycle_simple()` `backbone_linear()` `backbone_linear_simple()` `backbone_disconnected()`	List of all predefined backbone models
`backbone()`	Backbone of the simulation model
`bblego()` `bblego_linear()` `bblego_branching()` `bblego_start()` `bblego_end()`	Design your own custom backbone easily
`plot_backbone_modulenet()`	Visualise the backbone of a model
`plot_backbone_statenet()`	Visualise the backbone state network of a model
Step 2, generate TF network Generate a transcription factor network from the backbone
`generate_tf_network()` `tf_network_default()`	Generate a transcription factor network from the backbone
Step 3, add more genes to the gene network Generate a target network
`generate_feature_network()` `feature_network_default()`	Generate a target network
`plot_feature_network()`	Visualise the feature network of a model
Step 4, generate gene kinetics Generate kinetics from each gene in the network
`generate_kinetics()` `kinetics_default()` `kinetics_random_distributions()`	Determine the kinetics of the feature network
Step 5, simulate the gold standard Simulate the gold standard backbone, used for mapping to cell states afterwards
`generate_gold_standard()` `gold_standard_default()`	Simulate the gold standard
`plot_gold_mappings()`	Visualise the mapping of the simulations to the gold standard
`plot_gold_simulations()`	Visualise the simulations using the dimred
`plot_gold_expression()`	Visualise the expression of the gold standard over simulation time
Step 6, simulate the cells Simulate the cells based on its GRN
`generate_cells()` `simulation_default()` `simulation_type_wild_type()` `simulation_type_knockdown()`	Simulate the cells
`plot_simulations()`	Visualise the simulations using the dimred
`plot_simulation_expression()`	Visualise the expression of the simulations over simulation time
Step 7, simulate cell and transcripting sampling Simulate cell and transcripting sampling
`generate_experiment()` `list_experiment_samplers()` `experiment_snapshot()` `experiment_synchronised()`	Sample cells from the simulations
`kinetics_noise_none()` `kinetics_noise_simple()`	Add small noise to the kinetics of each simulation
`simtime_from_backbone()`	Determine simulation time from backbone
`plot_experiment_dimred()`	Plot a dimensionality reduction of the final dataset
Step 8, convert to dataset Convert to a dataset object for ease of use
`as_dyno()` `as_anndata()` `as_sce()` `as_seurat()` `as_list()` `wrap_dataset()`	Convert simulation output to different formats.
One-shot function Run through steps 2 to 8 with a single function
`generate_dataset()`	Generate a dataset
`plot_summary()`	Plot a summary of all dyngen simulation steps.
Data objects
`example_model`	A (very!) small toy dyngen model
`realcounts`	A set of real single cell expression datasets
`realnets`	A set of gold standard gene regulatory networks
Varia functions
`dyngen`	dyngen: A multi-modal simulator for spearheading single-cell omics analyses
`get_timings()`	Return the timings of each of the dyngen steps
`combine_models()`	Combine multiple dyngen models
`rnorm_bounded()`	A bounded version of rnorm
`runif_subrange()`	A subrange version of runif

Reference

Step 1, initialise dyngen model

Step 2, generate TF network

Step 3, add more genes to the gene network

Step 4, generate gene kinetics

Step 5, simulate the gold standard

Step 6, simulate the cells

Step 7, simulate cell and transcripting sampling

Step 8, convert to dataset

One-shot function

Data objects

Varia functions