run_gsva {decoupleR}R Documentation

Gene Set Variation Analysis (GSVA)

Description

Calculates regulatory activities using GSVA.

Usage

run_gsva(
  mat,
  network,
  .source = .data$source,
  .target = .data$target,
  verbose = FALSE,
  method = "gsva",
  ...
)

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.

verbose

Gives information about each calculation step. Default: FALSE.

method

Method to employ in the estimation of gene-set enrichment. scores per sample. By default this is set to gsva (Hänzelmann et al, 2013).

...

Arguments passed on to GSVA::gsva

Details

This function is a wrapper for the method GSVA::gsva().

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_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, "input-expr_matrix.rds"))
network <- readRDS(file.path(inputs_dir, "input-dorothea_genesets.rds"))

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

[Package decoupleR version 1.99.5 Index]