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dynwrap (version 1.2.1)

add_cyclic_trajectory: Constructs a circular trajectory using the pseudotime values of each cell.

Description

The pseudotime is divided into three equally sized segments, and are placed within a trajectory in the form A -> B -> C -> A

Usage

add_cyclic_trajectory(
  dataset,
  pseudotime,
  directed = FALSE,
  do_scale_minmax = TRUE,
  ...
)

Arguments

dataset

A dataset created by wrap_data() or wrap_expression()

pseudotime

A named vector of pseudo times.

directed

Whether or not the directionality of the pseudotime is predicted.

do_scale_minmax

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.

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

Run this code
# 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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