infer_trajectories

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Infer one or more trajectories from a single-cell dataset

Infer one or more trajectories from a single-cell dataset

Keywords
infer_trajectory
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 (dynwrap::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

  • milestone_ids: The names of the milestones, a character vector.

  • milestone_network: The network between the milestones, a dataframe with the from milestone, to milestone, length of the edge, and whether it is directed.

  • divergence_regions: The regions between three or more milestones where cells are diverging, a dataframe with the divergence id, the milestone id and whether this milestone is the start of the divergence

  • milestone_percentages: For each cell its closeness to a particular milestone, a dataframe with the cell id, the milestone id, and its percentage (a number between 0 and 1 where higher values indicate that a cell is close to the milestone).

  • progressions: For each cell its progression along a particular edge of the milestone_network. Contains the same information as milestone_percentages. A dataframe with cell id, from milestone, to milestone, and its percentage (a number between 0 and 1 where higher values indicate that a cell is close to the 'to' milestone and far from the 'from' milestone).

  • cell_ids: The names of the cells

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

  • A dimensionality reduction (dimred), the location of the trajectory milestones and edges in this dimensionality reduction (dimred_milestones, dimred_segment_progressions and dimred_segment_points). See add_dimred() for more information on these objects.

  • A cell grouping (grouping). See add_grouping() for more information on this object.

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

Aliases
  • infer_trajectories
  • infer_trajectory
Examples
# NOT RUN {
dataset <- example_dataset
method <- get_ti_methods(as_tibble = FALSE)[[1]]$fun

trajectory <- infer_trajectory(dataset, method())

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

# }
Documentation reproduced from package dynwrap, version 1.2.1, License: MIT + file LICENSE

Community examples

1532732527@qq.com at Dec 11, 2020 dynwrap v1.2.1

dataset <- example_dataset method <- get_ti_methods(as_tibble = FALSE)[[1]]$fun trajectory <- infer_trajectory(dataset, method()) head(trajectory$milestone_network) head(trajectory$progressions)