Constructs a multifurcating trajectory using end state probabilities

Description

Constructs a multifurcating trajectory using the pseudotime values of each cell and their end state probabilities. If pseudotime values are not given, will use pseudotime already present in the dataset.

Usage

add_end_state_probabilities(
dataset,
end_state_probabilities,
pseudotime = NULL,
do_scale_minmax = TRUE,
...
)


Arguments

 dataset A dataset created by wrap_data() or wrap_expression() end_state_probabilities A dataframe containing the cell_id and additional numeric columns containing the probability for every end milestone. If the tibble contains only a cell_id column, the data will be processed using add_linear_trajectory pseudotime A named vector of pseudo times. do_scale_minmax Whether or not to scale the pseudotime between 0 and 1. Otherwise, will assume the values are already within that range. ... Extras to be added to the trajectory

Value

The dataset object with trajectory information, including:

• 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 (divergence_id), the milestone id (milestone_id) and whether this milestone is the start of the divergence (is_start)

• milestone_percentages: For each cell its closeness to a particular milestone, a dataframe with the cell id (cell_id), the milestone id (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 (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).

Examples

dataset <- wrap_data(cell_ids = letters)

pseudotime <- runif(length(dataset$cell_ids)) names(pseudotime) <- dataset$cell_ids
pseudotime
end_state_probabilities <- tibble::tibble(
cell_id = dataset$cell_ids, A = runif(length(dataset$cell_ids)),
B = 1-A
)
end_state_probabilities