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)

[Package decoupleR version 2.0.1 Index]