dyngen (version 1.0.5)

dyngen: dyngen: A multi-modal simulator for spearheading single-cell omics analyses

Description

A toolkit for generating synthetic single cell data.

Step 1, initialise dyngen model

• 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()

Step 2, generate TF network

• generate_tf_network(): Generate a transcription factor network from the backbone

• tf_network_default(): Parameters for configuring this step

Step 3, add more genes to the gene network

• generate_feature_network(): Generate a target network

• feature_network_default(): Parameters for configuring this step

• plot_feature_network(): Visualise the gene network

Step 4, generate gene kinetics

• generate_kinetics(): Generate the gene kinetics

• kinetics_default(), kinetics_random_distributions(): Parameters for configuring this step

Step 5, simulate the gold standard

• 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

Step 6, simulate the cells

• 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

Step 7, simulate cell and transcripting sampling

• 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

Step 8, convert to 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

One-shot function

• generate_dataset(): Run through steps 2 to 8 with a single function

• plot_summary(): Plot a summary of all dyngen simulation steps

Data objects

• 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

Varia functions

• 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()

Examples

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{
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)
# }