run_viper {decoupleR} | R Documentation

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

### Description

Calculates regulatory activities using VIPER.

### Usage

    run_viper(
      mat,
      network,
      .source = .data$source,
      .target = .data$target,
      .mor = .data$mor,
      .likelihood = .data$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, "input-expr_matrix.rds"))
    network <- readRDS(file.path(inputs_dir, "input-dorothea_genesets.rds"))

    run_viper(mat, network, .source='tf', verbose = FALSE)

[Package decoupleR version 2.0.1 Index]