generate_datasets: Generate toy datasets with dyntoy
In dynverse/dyntoy: Generating simple toy data of cellular differentiation

Description Usage Arguments

Generate toy datasets with dyntoy

generate_datasets(models = c("linear", "cyclic", "bifurcating",
  "multifurcating", "binary_tree", "tree", "converging",
  "diverging_converging", "diverging_with_loops", "looping", "connected",
  "disconnected"), num_cells = 99, num_features = 101,
  allow_tented_progressions = TRUE, sample_mean_count = function()
  runif(1, 100, 1000), sample_dispersion_count = function(mean)
  map_dbl(mean, ~runif(1, ./10, ./4)), dropout_probability_factor = 100,
  normalise = FALSE, add_prior_information = TRUE,
  num_replicates = 3)

`models`	A list of network models to use to generate the milestone networks. a character vector (e.g. `c("linear", "bifurcating")`), a list of functions (e.g. `list(model_linear, model_bifurcating)`), a list of data frames (e.g. `list(model_linear(), model_bifurcating())`)
`num_cells`	The number of cells in each dataset
`num_features`	Number of features
`allow_tented_progressions`	Whether or not to be able to generate cells as part of a divergence.
`sample_mean_count`	Function used to sample the mean expression
`sample_dispersion_count`	Function to sample the dispersion (size) of the negative biniomal given the expression. Higher dispersion values generate less noise
`dropout_probability_factor`	Factor used to calculate the probabilities of dropouts, relative to expression. Higher values (> 10000) have a lot of dropouts, lower values (< 10) have almost none
`normalise`	Whether or not to normalise the dataset
`add_prior_information`	Whether to add prior information
`num_replicates`	How many replicates of each TI type to generate