This vignette demonstrates the basics of running a dyngen simulation. If you haven’t done so already, first check out the installation instructions in the README.

Step 1: Define backbone and other parameters

A dyngen simulation can be started by providing a backbone to the initialise_model() function. The backbone of a dyngen model is what determines the overall dynamic process that a cell will undergo during a simulation. It consists of a set of gene modules, which regulate eachother in such a way that expression of certain genes change over time in a specific manner.



backbone <- backbone_bifurcating()
config <- 
    backbone = backbone,
    num_tfs = nrow(backbone$module_info),
    num_targets = 500,
    num_hks = 500,
    verbose = FALSE
# the simulation is being sped up because rendering all vignettes with one core
# for pkgdown can otherwise take a very long time

config <-
    backbone = backbone,
    num_cells = 1000,
    num_tfs = nrow(backbone$module_info),
    num_targets = 50,
    num_hks = 50,
    verbose = FALSE,
    download_cache_dir = tools::R_user_dir("dyngen", "data"),
    simulation_params = simulation_default(
      total_time = 1000,
      census_interval = 2, 
      ssa_algorithm = ssa_etl(tau = 300/3600),
      experiment_params = simulation_type_wild_type(num_simulations = 10)

For backbones with all different sorts of topologies, check list_backbones():

##  [1] "bifurcating"             "bifurcating_converging" 
##  [3] "bifurcating_cycle"       "bifurcating_loop"       
##  [5] "binary_tree"             "branching"              
##  [7] "consecutive_bifurcating" "converging"             
##  [9] "cycle"                   "cycle_simple"           
## [11] "disconnected"            "linear"                 
## [13] "linear_simple"           "trifurcating"

Step 2: Generate transcription factors (TFs)

Each gene module consists of a set of transcription factors. These can be generated and visualised as follows.

model <- generate_tf_network(config)
plot_feature_network(model, show_targets = FALSE)

Step 3: Sample target genes and housekeeping genes (HKs)

Next, target genes and housekeeping genes are added to the network by sampling a gold standard gene regulatory network using the Page Rank algorithm. Target genes are regulated by TFs or other target genes, while HKs are only regulated by themselves.

plot_feature_network(model, show_hks = TRUE)

Step 4: Generate kinetics

Note that the target network does not show the effect of some interactions, because these are generated along with other kinetics parameters of the SSA simulation.

plot_feature_network(model, show_hks = TRUE)

Step 5: Simulate gold standard

The gold standard is simulated by enabling certain parts of the module network and performing ODE simulations. The gold standard are visualised by performing a dimensionality reduction on the mRNA expression values.

model <- generate_gold_standard(model)
plot_gold_simulations(model) + scale_colour_brewer(palette = "Dark2")

The expression of the modules (average of TFs) can be visualised as follows.

plot_gold_expression(model, what = "mol_mrna") # mrna

plot_gold_expression(model, label_changing = FALSE) # premrna, mrna, and protein

Step 6: Simulate cells.

Cells are simulated by running SSA simulations. The simulations are again using dimensionality reduction.

model <- generate_cells(model)

The gold standard can be overlayed on top of the simulations.

plot_gold_simulations(model) + scale_colour_brewer(palette = "Dark2")

We can check how each segment of a simulation is mapped to the gold standard.

plot_gold_mappings(model, do_facet = FALSE) + scale_colour_brewer(palette = "Dark2")

The expression of the modules (average of TFs) of a single simulation can be visualised as follows.

plot_simulation_expression(model, 1:4, what = "mol_mrna")

Step 7: Experiment emulation

Effects from performing a single-cell RNA-seq experiment can be emulated as follows.

model <- generate_experiment(model)

Step 8: Convert to a dyno object

Converting the dyngen to a dyno object allows you to visualise the dataset using the dynplot functions, or infer trajectories using dynmethods.

dataset <- as_dyno(model)
## Loading required namespace: dynwrap

Visualise with dynplot

## Coloring by milestone
## Using milestone_percentages from trajectory

## Coloring by milestone
## Using milestone_percentages from trajectory

Save output to file

You can save the output of the model and/or dataset to a file as follows.

write_rds(model, "model.rds", compress = "gz")
write_rds(dataset, "dataset.rds", compress = "gz")

Step 8 alternative: Convert to an anndata/SCE/Seurat object

dyngen 1.0.0 allows converting the output to an anndata, SCE or Seurat object as well. Check out the anndata documentation on how to install anndata for R.

ad <- as_anndata(model)

sce <- as_sce(model)
write_rds(sce, "dataset_sce.rds")

seurat <- as_seurat(model)
write_rds(seurat, "dataset_seurat.rds")

One-shot function

dyngen also provides a one-shot function for running all of the steps all at once and producing plots.

out <- generate_dataset(
  format = "dyno",
  make_plots = TRUE
dataset <- out$dataset
model <- out$model

dataset and model can be used in much the same way as before.

## Loading required package: dynfeature
## Loading required package: dynguidelines
## Loading required package: dynmethods
## Loading required package: dynwrap
## Coloring by milestone
## Using milestone_percentages from trajectory

## Coloring by milestone
## Using milestone_percentages from trajectory