run_fgsea {decoupleR} | R Documentation

## Fast Gene Set Enrichment Analysis (FGSEA)

### Description

Calculates regulatory activities using FGSEA.

### Usage

    run_fgsea(
      mat,
      network,
      .source = .data$source,
      .target = .data$target,
      times = 100,
      nproc = 4,
      seed = 42,
      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.
`times` | How many permutations to do?
`nproc` | Number of cores to use for computation.
`seed` | A single value, interpreted as an integer, or NULL.
`minsize` | Integer indicating the minimum number of targets per source.
`...` | Arguments passed on to `fgsea::fgseaMultilevel` `sampleSize` The size of a random set of genes which in turn has size = pathwaySize `minSize` Minimal size of a gene set to test. All pathways below the threshold are excluded. `maxSize` Maximal size of a gene set to test. All pathways above the threshold are excluded. `eps` This parameter sets the boundary for calculating the p value. `scoreType` This parameter defines the GSEA score type. Possible options are ("std", "pos", "neg") `gseaParam` GSEA parameter value, all gene-level statis are raised to the power of 'gseaParam' before calculation of GSEA enrichment scores. `BPPARAM` Parallelization parameter used in bplapply. Can be used to specify cluster to run. If not initialized explicitly or by setting 'nproc' default value 'bpparam()' is used. `absEps` deprecated, use 'eps' parameter instead

### Details

GSEA (Aravind et al., 2005) starts by transforming the input molecular readouts in mat to ranks for each sample. Then, an enrichment score `fgsea` is calculated by walking down the list of features, increasing a running-sum statistic when a feature in the target feature set is encountered and decreasing it when it is not. The final score is the maximum deviation from zero encountered in the random walk. Finally, a normalized score `norm_fgsea`, can be obtained by computing the z-score of the estimate compared to a null distribution obtained from N random permutations. The used implementation is taken from the package `fgsea` (Korotkevich et al., 2021).

Aravind S. et al. (2005) Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. PNAS. 102, 43.

Korotkevich G. et al. (2021) Fast gene set enrichment analysis. bioRxiv. DOI: https://doi.org/10.1101/060012.

### 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_gsva()`, `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_fgsea(mat, network, .source='tf', nproc=1)

[Package decoupleR version 2.0.1 Index]