run_wsum {decoupleR} | R Documentation

## Weighted Sum (WSUM)

### Description

Calculates regulatory activities using WSUM.

### Usage

    run_wsum(
      mat,
      network,
      .source = .data$source,
      .target = .data$target,
      .mor = .data$mor,
      .likelihood = .data$likelihood,
      times = 100,
      seed = 42,
      sparse = TRUE,
      randomize_type = "rows",
      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.
`.mor` | Column with edge mode of regulation (i.e. mor).
`.likelihood` | Deprecated argument. Now it will always be set to 1.
`times` | How many permutations to do?
`seed` | A single value, interpreted as an integer, or NULL for random number generation.
`sparse` | Should the matrices used for the calculation be sparse?
`randomize_type` | How to randomize the expression matrix.
`minsize` | Integer indicating the minimum number of targets per source.

### Details

WSUM infers regulator activities by first multiplying each target feature by its associated weight which then are summed to an enrichment score `wsum`. Furthermore, permutations of random target features can be performed to obtain a null distribution that can be used to compute a z-score `norm_wsum`, or a corrected estimate `corr_wsum` by multiplying `wsum` by the minus log10 of the obtained empirical p-value.

### 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).

5. `p_value`: p-value for the score of the method.

### See Also

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

### 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_wsum(mat, network, .source='tf')

[Package decoupleR version 2.0.1 Index]