run_udt: Univariate Decision Tree (UDT)

In saezlab/decoupleR: decoupleR: Ensemble of computational methods to infer biological activities from omics data

Univariate Decision Tree (UDT)

Description

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.

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.

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_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)

saezlab/decoupleR documentation built on April 12, 2024, 10:41 a.m.