generate_dataset: Generate a toy dataset

In dynverse/dyntoy: Generating simple toy data of cellular differentiation

Description

Generate a toy dataset

Usage

generate_dataset(id = "", model = 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,
  differentially_expressed_rate = 1, normalise = FALSE,
  add_prior_information = TRUE, add_velocity = TRUE)

Arguments

id	An id for the dataset
model	A model for generating the milestone network. Must be one of: a character vector (e.g. "linear"), a function (e.g. model_linear), a data frame (e.g. model_linear())
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
differentially_expressed_rate	Percentage of differentially expressed genes
normalise	Whether or not to normalise the dataset
add_prior_information	Whether to add prior information
add_velocity	Whether to simulate RNA velocity

dynverse/dyntoy documentation built on May 25, 2019, 4:26 p.m.