dyngen: dyngen: A multi-modal simulator for spearheading single-cell...

dyngenR Documentation

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()
)


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)



dyngen documentation built on Oct. 12, 2022, 5:06 p.m.