run_aucell {decoupleR} | R Documentation

## AUCell

### Description

Calculates regulatory activities using AUCell.

### Usage

    run_aucell(
      mat,
      network,
      .source = .data$source,
      .target = .data$target,
      aucMaxRank = ceiling(0.05 * nrow(rankings)),
      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.
`aucMaxRank` | Threshold to calculate the AUC.
`nproc` | Number of cores to use for computation.
`seed` | A single value, interpreted as an integer, or NULL for random number generation.
`minsize` | Integer indicating the minimum number of targets per source.

### Details

AUCell (Aibar et al., 2017) uses the Area Under the Curve (AUC) to calculate whether a set of targets is enriched within the molecular readouts of each sample. To do so, AUCell first ranks the molecular features of each sample from highest to lowest value, resolving ties randomly. Then, an AUC can be calculated using by default the top 5% molecular features in the ranking. Therefore, this metric, `aucell`, represents the proportion of abundant molecular features in the target set, and their relative abundance value compared to the other features within the sample.

Aibar S. et al. (2017) Scenic: single-cell regulatory network inference and clustering. Nat. Methods, 14, 1083–1086.

### See Also

Other decoupleR statistics: `decouple()`, `run_fgsea()`, `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_aucell(mat, network, .source='tf', nproc=1)

[Package decoupleR version 2.0.1 Index]