generate_experiment() runs samples cells along the different simulations. experiment_snapshot() assumes that cells are sampled from a heterogeneous pool of cells. Cells will thus be sampled uniformily from the trajectory. experiment_synchronised() assumes that all the cells are synchronised and are sampled at different timepoints.

generate_experiment(model)

list_experiment_samplers()

experiment_snapshot(
  realcount = NULL,
  map_reference_cpm = TRUE,
  map_reference_ls = TRUE,
  weight_bw = 0.1
)

experiment_synchronised(
  realcount = NULL,
  map_reference_cpm = TRUE,
  map_reference_ls = TRUE,
  num_timepoints = 8,
  pct_between = 0.75
)

Arguments

model

A dyngen intermediary model for which the simulations have been run with generate_cells().

realcount

The name of a dataset in realcounts. If NULL, a random dataset will be sampled from realcounts.

map_reference_cpm

Whether or not to try to match the CPM distribution to that of a reference dataset.

map_reference_ls

Whether or not to try to match the distribution of the library sizes to that of the reference dataset.

weight_bw

[snapshot] A bandwidth parameter for determining the distribution of cells along each edge in order to perform weighted sampling.

num_timepoints

[synchronised] The number of time points used in the experiment.

pct_between

[synchronised] The percentage of 'unused' simulation time.

Value

A dyngen model.

Examples

names(list_experiment_samplers())
#> [1] "snapshot" "synchronised"
model <- initialise_model( backbone = backbone_bifurcating(), experiment = experiment_synchronised() ) if (FALSE) { data("example_model") model <- example_model %>% generate_experiment() plot_experiment_dimred(model) }