Extract the prior information from the milestone network

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

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)

Arguments

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