dyngen | R Documentation |
A toolkit for generating synthetic single cell data.
initialise_model()
: Define and store settings for all following steps. See each of the sections below for more information.
Use a predefined backbone:
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()
Create a custom backbone:
backbone()
bblego()
bblego_linear()
bblego_branching()
bblego_start()
bblego_end()
Visualise the backbone:
plot_backbone_modulenet()
plot_backbone_statenet()
generate_tf_network()
: Generate a transcription factor network from the backbone
tf_network_default()
: Parameters for configuring this step
generate_feature_network()
: Generate a target network
feature_network_default()
: Parameters for configuring this step
plot_feature_network()
: Visualise the gene network
generate_kinetics()
: Generate the gene kinetics
kinetics_default()
, kinetics_random_distributions()
: Parameters for configuring this step
generate_gold_standard()
: Simulate the gold standard backbone, used for mapping to cell states afterwards
gold_standard_default()
: Parameters for configuring this step
plot_gold_mappings()
: Visualise the mapping of the simulations to the gold standard
plot_gold_simulations()
: Visualise the gold standard simulations using the dimred
plot_gold_expression()
: Visualise the expression of the gold standard over simulation time
generate_cells()
: Simulate the cells based on its GRN
simulation_default()
: Parameters for configuring this step
simulation_type_wild_type()
, simulation_type_knockdown()
: Used for configuring the type of simulation
kinetics_noise_none()
, kinetics_noise_simple()
: Different kinetics randomisers to apply to each simulation
plot_simulations()
: Visualise the simulations using the dimred
plot_simulation_expression()
: Visualise the expression of the simulations over simulation time
generate_experiment()
: Sample cells and transcripts from experiment
list_experiment_samplers()
, experiment_snapshot()
, experiment_synchronised()
: Parameters for configuring this step
simtime_from_backbone()
: Determine the simulation time from the backbone
plot_experiment_dimred()
: Plot a dimensionality reduction of the final dataset
as_dyno()
, wrap_dataset()
: Convert a dyngen model to a dyno dataset
as_anndata()
: Convert a dyngen model to an anndata dataset
as_sce()
: Convert a dyngen model to a SingleCellExperiment dataset
as_seurat()
: Convert a dyngen model to a Seurat dataset
generate_dataset()
: Run through steps 2 to 8 with a single function
plot_summary()
: Plot a summary of all dyngen simulation steps
example_model: A (very) small toy dyngen model, used for documentation and testing purposes
realcounts: A set of real single-cell expression datasets, to be used as reference datasets
realnets: A set of real gene regulatory networks, to be sampled in step 3
dyngen: This help page
get_timings()
: Extract execution timings for 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()
model <- initialise_model( backbone = backbone_bifurcating() ) model <- model %>% generate_tf_network() %>% generate_feature_network() %>% generate_kinetics() %>% generate_gold_standard() %>% generate_cells() %>% generate_experiment() dataset <- wrap_dataset(model, format = "dyno") # format can also be set to "sce", "seurat", "anndata" or "list" # library(dynplot) # plot_dimred(dataset)
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