run_wsum {decoupleR}R Documentation

Weighted Sum (WSUM)

Description

Calculates regulatory activities by computing the WSUM

Usage

run_wsum(
  mat,
  network,
  .source = .data$source,
  .target = .data$target,
  .mor = .data$mor,
  .likelihood = .data$likelihood,
  times = 100,
  seed = 42,
  sparse = TRUE,
  randomize_type = "rows"
)

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.

.mor

Column with edge mode of regulation (i.e. mor).

.likelihood

Column with edge likelihood.

times

How many permutations to do?

seed

A single value, interpreted as an integer, or NULL for random number generation.

sparse

Should the matrices used for the calculation be sparse?

randomize_type

How to randomize the expression matrix.

Details

Infers activity score for each regulator by weighting the molecular readouts of its targets by their mode of regulations and likelihoods. In addition, it runs permutations to calculate empirical p-values, providing normalized (z-score) and corrected activity (estimate * -log10(p-value)) scores. This is represented in the statistic column which will contain three values for each call to run_wsum(); wsum, norm_wsum and corr_wsum.

Value

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

  1. statistic: Indicates which method is associated with which score.

  2. source: Source nodes of network.

  3. condition: Condition representing each column of mat.

  4. score: Regulatory activity (enrichment score).

  5. p_value: p-value for the score of the method.

See Also

Other decoupleR statistics: decouple(), run_aucell(), run_fgsea(), run_gsva(), run_mdt(), run_mlm(), run_ora(), run_udt(), run_ulm(), run_viper(), run_wmean()

Examples

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"))

run_wsum(mat, network, .source='tf')

[Package decoupleR version 1.99.5 Index]