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",
  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.
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).
minsize	Integer indicating the minimum number of targets per source.
...	Arguments passed on to GSVA::gsva

Details

GSVA (Hänzelmann et al., 2013) starts by transforming the input molecular readouts in mat to a readout-level statistic using Gaussian kernel estimation of the cumulative density function. Then, readout-level statistics are ranked per sample and normalized to up-weight the two tails of the rank distribution. Afterwards, an enrichment score gsva is calculated using a running sum statistic that is normalized by subtracting the largest negative estimate from the largest positive one.

Hänzelmann S. et al. (2013) GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics, 14, 7.

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)

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