decouple {decoupleR} | R Documentation

## Evaluate multiple statistics with same input data

### Description

Calculate the source activity per sample out of a gene expression matrix by coupling a regulatory network with a variety of statistics.

### Usage

    decouple(
      mat,
      network,
      .source = .data$source,
      .target = .data$target,
      statistics = NULL,
      args = list(NULL),
      consensus_score = TRUE,
      consensus_stats = NULL,
      include_time = FALSE,
      show_toy_call = FALSE,
      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.
`statistics` | Statistical methods to be run sequentially. If none are provided, only top performer methods are run (mlm, ulm and wsum).
`args` | A list of argument-lists the same length as `statistics` (or length 1). The default argument, list(NULL), will be recycled to the same length as `statistics`, and will call each function with no arguments (apart from `mat`, `network`, `.source` and, `.target`).
`consensus_score` | Boolean whether to run a consensus score between methods.
`consensus_stats` | List of estimate names to use for the calculation of the consensus score. This is used to filter out extra estimations from some methods, for example wsum returns wsum, corr_wsum and norm_wsum. If none are provided, and also no statstics where provided, only top performer methods are used (mlm, ulm and norm_wsum). Else, it will use all available estimates after running all methods in the statistics argument.
`include_time` | Should the time per statistic evaluated be informed?
`show_toy_call` | The call of each statistic must be informed?
`minsize` | Integer indicating the minimum number of targets per source.

### Value

A long format tibble of the enrichment scores for each source across the samples. Resulting tibble contains the following columns:

1. `run_id`: Indicates the order in which the methods have been executed.

2. `statistic`: Indicates which method is associated with which score.

3. `source`: Source nodes of `network`.

4. `condition`: Condition representing each column of `mat`.

5. `score`: Regulatory activity (enrichment score).

6. `statistic_time`: If requested, internal execution time indicator.

7. `p_value`: p-value (if available) of the obtained score.

### See Also

Other decoupleR statistics: `run_aucell()`, `run_fgsea()`, `run_gsva()`, `run_mdt()`, `run_mlm()`, `run_ora()`, `run_udt()`, `run_ulm()`, `run_viper()`, `run_wmean()`, `run_wsum()`

### Examples

    if (FALSE) {
        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"))

        decouple(
            mat = mat,
            network = network,
            .source = "tf",
            .target = "target",
            statistics = c("gsva", "wmean", "wsum", "ulm", "aucell"),
            args = list(
                gsva = list(verbose = FALSE),
                wmean = list(.mor = "mor", .likelihood = "likelihood"),
                wsum = list(.mor = "mor"),
                ulm = list(.mor = "mor")
            )
        )
    }

[Package decoupleR version 2.0.1 Index]