dynwrap (version 1.2.1)

add_prior_information: 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 of the features, a dataframe 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

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

Examples

# NOT RUN {
# 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

# }

Run the code above in your browser using DataCamp Workspace