The pseudotime is divided into three equally sized segments, and are placed within a trajectory in the form A -> B -> C -> A
add_cyclic_trajectory(
dataset,
pseudotime,
directed = FALSE,
do_scale_minmax = TRUE,
...
)
A dataset created by wrap_data()
or wrap_expression()
A named vector of pseudo times.
Whether or not the directionality of the pseudotime is predicted.
Whether or not to scale the pseudotime between 0 and 1. Otherwise, will assume the values are already within that range.
extra information to be stored in the wrapper.
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).
# NOT RUN {
library(tibble)
dataset <- wrap_data(cell_ids = letters)
pseudotime <- tibble(cell_id = dataset$cell_ids, pseudotime = runif(length(dataset$cell_ids)))
pseudotime
trajectory <- add_cyclic_trajectory(dataset, pseudotime)
# for plotting the result, install dynplot
#- dynplot::plot_graph(trajectory)
# }
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