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(). The name of a dataset in realcounts. If NULL, a random dataset will be sampled from realcounts. Whether or not to try to match the CPM distribution to that of a reference dataset. Whether or not to try to match the distribution of the library sizes to that of the reference dataset. [snapshot] A bandwidth parameter for determining the distribution of cells along each edge in order to perform weighted sampling. [synchronised] The number of time points used in the experiment. [synchronised] The percentage of 'unused' simulation time.

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)
}