add_prior_information: Add or compute prior information for a trajectory

In dynwrap: Representing and Inferring Single-Cell Trajectories

Add or compute prior information for a trajectory

Description

If you specify

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

Usage

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,
  leaves_n = NULL,
  timecourse_continuous = NULL,
  timecourse_discrete = NULL,
  dimred = 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
leaves_n	Number of leaves
timecourse_continuous	The time for every cell
timecourse_discrete	The time for every cell in groups
dimred	A dimensionality reduction of the cells (see add_dimred())
verbose	Whether or not to print informative messages
cell_ids	The identifiers 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 of genes (columns) within cells (rows). This can be both a dense and sparse matrix.
feature_info	Optional meta-information pertaining the features.
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

If the dataset contains a trajectory (see add_trajectory()) and expression data, this function will compute and add prior information using generate_prior_information()

The dataset has to contain a trajectory for this to work

Value

A dynwrap object with the prior information added.

Examples

# add some prior information manually
dataset <- example_dataset
dataset <- add_prior_information(dataset, start_id = "Cell1")
dataset$prior_information$start_id

# compute prior information from a trajectory
trajectory <- example_trajectory
trajectory <- add_prior_information(trajectory)
trajectory$prior_information$end_id

dynwrap documentation built on July 26, 2023, 5:15 p.m.