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Univariate Decision Tree (UDT)

Source: R/statistic-udt.R
run_udt.Rd

Calculates regulatory activities by using UDT.

Usage

run_udt(
  mat,
  network,
  .source = source,
  .target = target,
  .mor = mor,
  .likelihood = likelihood,
  sparse = FALSE,
  center = FALSE,
  na.rm = FALSE,
  min_n = 20,
  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.

.mor

Column with edge mode of regulation (i.e. mor).

.likelihood

Deprecated argument. Now it will always be set to 1.

sparse

Deprecated parameter.

center

Logical value indicating if mat must be centered by base::rowMeans().

na.rm

Should missing values (including NaN) be omitted from the calculations of base::rowMeans()?

min_n

An integer for the minimum number of data points in a node that are required for the node to be split further.

seed

A single value, interpreted as an integer, or NULL for random number generation.

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. 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).

Details

UDT fits a single regression decision tree for each sample and regulator, where the observed molecular readouts in mat are the response variable and the regulator weights in net are the explanatory one. Target features with no associated weight are set to zero. The obtained feature importance from the fitted model is the activity udt of a given regulator.

See also

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

Examples

inputs_dir <- system.file("testdata", "inputs", package = "decoupleR")

mat <- readRDS(file.path(inputs_dir, "mat.rds"))
net <- readRDS(file.path(inputs_dir, "net.rds"))

run_udt(mat, net, minsize=0)
#> # A tibble: 72 × 4
#>    statistic source condition score
#>    <chr>     <chr>  <chr>     <dbl>
#>  1 udt       T1     S01           0
#>  2 udt       T1     S02           0
#>  3 udt       T1     S03           0
#>  4 udt       T1     S04           0
#>  5 udt       T1     S05           0
#>  6 udt       T1     S06           0
#>  7 udt       T1     S07           0
#>  8 udt       T1     S08           0
#>  9 udt       T1     S09           0
#> 10 udt       T1     S10           0
#> # ℹ 62 more rows