run_viper: Virtual Inference of Protein-activity by Enriched Regulon...

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

Virtual Inference of Protein-activity by Enriched Regulon analysis (VIPER)

Description

Calculates regulatory activities using VIPER.

Usage

run_viper(
  mat,
  network,
  .source = source,
  .target = target,
  .mor = mor,
  .likelihood = likelihood,
  verbose = FALSE,
  minsize = 5,
  pleiotropy = TRUE,
  eset.filter = 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.
.mor	Column with edge mode of regulation (i.e. mor).
.likelihood	Deprecated argument. Now it will always be set to 1.
verbose	Logical, whether progression messages should be printed in the terminal.
minsize	Integer indicating the minimum number of targets per source.
pleiotropy	Logical, whether correction for pleiotropic regulation should be performed.
eset.filter	Logical, whether the dataset should be limited only to the genes represented in the interactome.
...	Arguments passed on to viper::viper dnull Numeric matrix for the null model, usually generated by nullTtest nes Logical, whether the enrichment score reported should be normalized method Character string indicating the method for computing the single samples signature, either scale, rank, mad, ttest or none bootstraps Integer indicating the number of bootstraps iterations to perform. Only the scale method is implemented with bootstraps. adaptive.size Logical, whether the weighting scores should be taken into account for computing the regulon size pleiotropyArgs list of 5 numbers for the pleotropy correction indicating: regulators p-value threshold, pleiotropic interaction p-value threshold, minimum number of targets in the overlap between pleiotropic regulators, penalty for the pleiotropic interactions and the method for computing the pleiotropy, either absolute or adaptive cores Integer indicating the number of cores to use (only 1 in Windows-based systems)

Details

VIPER (Alvarez et al., 2016) estimates biological activities by performing a three-tailed enrichment score calculation. For further information check the supplementary information of the decoupler manuscript or the original publication.

Alvarez M.J.et al. (2016) Functional characterization of somatic mutations in cancer using network-based inference of protein activity. Nat. Genet., 48, 838–847.

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_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_viper(mat, net, minsize=0, verbose = FALSE)

saezlab/decoupleR documentation built on Oct. 21, 2024, 8:47 a.m.