R: Evaluate multiple statistics with same input data

decouple {decoupleR}

R Documentation

Evaluate multiple statistics with same input data

Description

Calculate the TF activity per sample out of a gene expression matrix by coupling a regulon network with a variety of statistics.

Usage

decouple(
  mat,
  network,
  .source,
  .target,
  statistics,
  args = list(NULL),
  include_time = FALSE,
  show_toy_call = FALSE
)

Arguments

`mat`	Matrix to evaluate (e.g. expression matrix). Target nodes in rows and conditions in columns. `rownames(mat)` must have at least one intersection with the elements in `network` `.target` column.
`network`	Tibble or dataframe with edges and it's associated metadata.
`.source`	Column with source nodes.
`.target`	Column with target nodes.
`statistics`	Statistical methods to be coupled.
`args`	A list of argument-lists the same length as `statistics` (or length 1). The default argument, list(NULL), will be recycled to the same length as `statistics`, and will call each function with no arguments (apart from `mat`, `network`, `.source` and, `.target`).
`include_time`	Should the time per statistic evaluated be informed?
`show_toy_call`	The call of each statistic must be informed?

Value

A long format tibble of the enrichment scores for each tf across the samples. Resulting tibble contains the following columns:

statistic: Indicates which method is associated with which score.
tf: Source nodes of network.
condition: Condition representing each column of mat.
score: Regulatory activity (enrichment score).
statistic_time: If requested, internal execution time indicator.
...: Columns of metadata generated by certain statistics.

Examples

if (FALSE) {
    inputs_dir <- system.file("testdata", "inputs", package = "decoupleR")

    mat <- readRDS(file.path(inputs_dir, "input-expr_matrix.rds"))
    network <- readRDS(file.path(inputs_dir, "input-dorothea_genesets.rds"))

    decouple(
        mat = mat,
        network = network,
        .source = "tf",
        .target = "target",
        statistics = c("gsva", "mean", "pscira", "scira", "viper"),
        args = list(
            gsva = list(verbose = FALSE),
            mean = list(.mor = "mor", .likelihood = "likelihood"),
            pscira = list(.mor = "mor"),
            scira = list(.mor = "mor"),
            viper = list(
                .mor = "mor",
                .likelihood = "likelihood",
                verbose = FALSE
            )
        )
    )
}