infer_trajectories {dynwrap}R Documentation

Infer one or more trajectories from a single-cell dataset

Description

Infer one or more trajectories from a single-cell dataset

Usage

infer_trajectories(
  dataset,
  method,
  parameters = NULL,
  give_priors = NULL,
  seed = random_seed(),
  verbose = FALSE,
  return_verbose = FALSE,
  debug = FALSE,
  map_fun = map
)

infer_trajectory(
  dataset,
  method,
  parameters = NULL,
  give_priors = NULL,
  seed = random_seed(),
  verbose = FALSE,
  return_verbose = FALSE,
  debug = FALSE,
  ...
)

Arguments

dataset

One or more datasets as created by wrap_data() or wrap_expression(). Prior information can be added using add_prior_information().

method

One or more methods. Must be one of:

  • an object or list of ti_... objects (eg. dynmethods::ti_comp1()),

  • a character vector containing the names of methods to execute (e.g. "scorpius"),

  • a character vector containing dockerhub repositories (e.g. dynverse/paga), or

  • a dynguidelines data frame.

parameters

A set of parameters to be used during trajectory inference. A parameter set must be a named list of parameters. If multiple methods were provided in the method parameter, parameters must be an unnamed list of the same length.

give_priors

All the priors a method is allowed to receive. Must be a subset of all available priors (priors).

seed

A seed to be passed to the TI method.

verbose

Whether or not to print information output.

return_verbose

Whether to store and return messages printed by the method.

debug

Used for debugging containers methods.

map_fun

A map function to use when inferring trajectories with multiple datasets or methods. Allows to parallellise the execution in an arbitrary way.

...

Any additional parameters given to the method, will be concatenated to the parameters argument

Value

infer_trajectory: A trajectory object, which is a list containing

Some methods will include additional information in the output, such as

infer_trajectories: A tibble containing the dataset and method identifiers (dataset_id and method_id), the trajectory model as described above (model), and a summary containing the execution times, output and error if appropriate

Examples

dataset <- example_dataset
method <- get_ti_methods(as_tibble = FALSE)[[1]]$fun

trajectory <- infer_trajectory(dataset, method())

head(trajectory$milestone_network)
head(trajectory$progressions)


[Package dynwrap version 1.2.2 Index]