infer_trajectories
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()
orwrap_expression()
. Prior information can be added usingadd_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
), ora 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
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$progressions)
# }
Community examples
dataset <- example_dataset method <- get_ti_methods(as_tibble = FALSE)[[1]]$fun trajectory <- infer_trajectory(dataset, method()) head(trajectory$milestone_network) head(trajectory$progressions)