> Reference > > dynwrap :: Creating a trajectory > Define a trajectory dataset given its milestone network and milestone percentages or progressions

Define a trajectory dataset given its milestone network and milestone percentages or progressions

add_trajectory(dataset, milestone_ids = NULL, milestone_network,
  divergence_regions = NULL, milestone_percentages = NULL,
  progressions = NULL, allow_self_loops = FALSE, ...)

is_wrapper_with_trajectory(trajectory)

Arguments

dataset	A dataset created by `wrap_data()` or `wrap_expression()`
milestone_ids	The ids of the milestones in the trajectory. Type: Character vector.
milestone_network	The network of the milestones. Type: Data frame(from = character, to = character, length = numeric, directed = logical).
divergence_regions	A data frame specifying the divergence regions between milestones (e.g. a bifurcation). Type: Data frame(divergence_id = character, milestone_id = character, is_start = logical).
milestone_percentages	A data frame specifying what percentage milestone each cell consists of. Type: Data frame(cell_id = character, milestone_id = character, percentage = numeric).
progressions	Specifies the progression of a cell along a transition in the milestone_network. Type: Data frame(cell_id = character, from = character, to = character, percentage = numeric).
allow_self_loops	Whether to allow self loops Type: Logical
...	extra information to be stored in the dataset
trajectory	The trajectory as created by `infer_trajectory()` or `add_trajectory()`

Value

The trajectory

Examples

library(dplyr)
library(tibble)

dataset <- wrap_data(cell_ids = letters)

milestone_network <- tribble(
  ~from, ~to, ~length, ~directed,
  "A", "B", 1, FALSE,
  "B", "C", 2, FALSE,
  "B", "D", 1, FALSE,
)
milestone_network
#> # A tibble: 3 x 4
#>   from  to    length directed
#>   <chr> <chr>  <dbl> <lgl>   
#> 1 A     B          1 FALSE   
#> 2 B     C          2 FALSE   
#> 3 B     D          1 FALSE   
progressions <- milestone_network %>%
  sample_n(length(dataset$cell_ids), replace = TRUE, weight = length) %>%
  mutate(
    cell_id = dataset$cell_ids,
    percentage = runif(n())
  ) %>%
  select(cell_id, from, to, percentage)
progressions
#> # A tibble: 26 x 4
#>    cell_id from  to    percentage
#>    <chr>   <chr> <chr>      <dbl>
#>  1 a       B     D         0.578 
#>  2 b       A     B         0.675 
#>  3 c       A     B         0.857 
#>  4 d       B     C         0.182 
#>  5 e       B     D         0.0863
#>  6 f       B     C         0.0263
#>  7 g       A     B         0.410 
#>  8 h       A     B         0.318 
#>  9 i       B     C         0.145 
#> 10 j       A     B         0.557 
#> # … with 16 more rows
divergence_regions <- tribble(
  ~divergence_id, ~milestone_id, ~is_start,
  "1", "A", TRUE,
  "1", "B", FALSE,
  "1", "C", FALSE
)
divergence_regions
#> # A tibble: 3 x 3
#>   divergence_id milestone_id is_start
#>   <chr>         <chr>        <lgl>   
#> 1 1             A            TRUE    
#> 2 1             B            FALSE   
#> 3 1             C            FALSE   
trajectory <- add_trajectory(
  dataset,
  milestone_network = milestone_network,
  divergence_regions = divergence_regions,
  progressions = progressions
)

# for plotting the result, install dynplot
dynplot::plot_graph(trajectory)
#> Coloring by milestone
#> Using milestone_percentages from trajectory