run_zscore: z-score

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

z-score

Description

Calculates regulatory activities using a z-score as descibed in KSEA or RoKAI.

Usage

run_zscore(
  mat,
  network,
  .source = source,
  .target = target,
  .mor = mor,
  .likelihood = likelihood,
  sparse = FALSE,
  center = FALSE,
  na.rm = FALSE,
  minsize = 5L,
  flavor = "RoKAI"
)

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.
sparse	Deprecated parameter.
center	Logical value indicating if mat must be centered by base::rowMeans().
na.rm	Should missing values (including NaN) be omitted from the calculations of base::rowMeans()?
minsize	Integer indicating the minimum number of targets per source.
flavor	Whether the calculation should be based on RoKAI (default) or KSEA.

Details

The z-score calculates the mean of the molecular features of the known targets for each regulator and adjusts it for the number of identified targets for the regulator, the standard deviation of all molecular features (RoKAI), as well as the mean of all moleculare features (KSEA).

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).

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_wsum()

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_zscore(mat, net, minsize=0)

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