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

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