Add prior information to a data wrapper

Source: R/wrap_add_prior_information.R

Note that the given data wrapper requires a trajectory and expression values to have been added already.

For example, what are the start cells, the end cells, to which milestone does each cell belong to, ...

add_prior_information(dataset, start_id = NULL, end_id = NULL,
  groups_id = NULL, groups_network = NULL, features_id = NULL,
  groups_n = NULL, start_n = NULL, end_n = NULL,
  timecourse_continuous = NULL, timecourse_discrete = NULL,
  verbose = TRUE)

is_wrapper_with_prior_information(dataset)

generate_prior_information(cell_ids, milestone_ids, milestone_network,
  milestone_percentages, progressions, divergence_regions, expression,
  feature_info = NULL, cell_info = NULL, marker_fdr = 0.005,
  given = NULL, verbose = FALSE)

Arguments

dataset

A dataset created by wrap_data() or wrap_expression()
start_id

The start cells
end_id

The end cells
groups_id

The grouping of cells, a dataframe with cell_id and group_id
groups_network

The network between groups, a dataframe with from and to
features_id

The features (genes) important for the trajectory
groups_n

Number of branches
start_n

Number of start states
end_n

Number of end states
timecourse_continuous

The time for every cell
timecourse_discrete

The time for every cell in groups
verbose

Whether or not to print informative messages
cell_ids

The ids of the cells.
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).
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).
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).
expression

The normalised expression values with genes in columns and cells in rows
feature_info

Optional meta-information of the features, a data.frame with at least feature_id as column
cell_info

Optional meta-information pertaining the cells.
marker_fdr

Maximal FDR value for a gene to be considered a marker
given

Prior information already calculated

Details

The dataset has to contain a trajectory for this to work