run_wsum: Weighted Sum (WSUM)

In saezlab/decoupleR: decoupleR: Ensemble of computational methods to infer biological activities from omics data

Weighted Sum (WSUM)

Description

Calculates regulatory activities using WSUM.

Usage

run_wsum(
  mat,
  network,
  .source = source,
  .target = target,
  .mor = mor,
  .likelihood = likelihood,
  times = 100,
  seed = 42,
  sparse = TRUE,
  randomize_type = "rows",
  minsize = 5
)

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	Deprecated argument. Now it will always be set to 1.
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.
minsize	Integer indicating the minimum number of targets per source.

Details

WSUM infers regulator activities by first multiplying each target feature by its associated weight which then are summed to an enrichment score wsum. Furthermore, permutations of random target features can be performed to obtain a null distribution that can be used to compute a z-score norm_wsum, or a corrected estimate corr_wsum by multiplying wsum by the minus log10 of the obtained empirical p-value.

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(), run_zscore()

Examples

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

mat <- readRDS(file.path(inputs_dir, "mat.rds"))
net <- readRDS(file.path(inputs_dir, "net.rds"))

run_wsum(mat, net, minsize=0)

saezlab/decoupleR documentation built on June 9, 2025, 1:55 p.m.