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Univariate Linear Model (ULM)

Source: R/statistic-ulm.R
run_ulm.Rd

Calculates regulatory activities using ULM.

Usage

run_ulm(
  mat,
  network,
  .source = source,
  .target = target,
  .mor = mor,
  .likelihood = likelihood,
  sparse = FALSE,
  center = FALSE,
  na.rm = FALSE,
  minsize = 5L
)

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()?

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

ULM fits a linear model 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 t-value from the fitted model is the activity ulm of a given regulator.

See also

Other decoupleR statistics: decouple(), run_aucell(), run_fgsea(), run_gsva(), run_mdt(), run_mlm(), run_ora(), run_udt(), 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_ulm(mat, net, minsize=0)
#> # A tibble: 72 × 5
#>    statistic source condition score  p_value
#>    <chr>     <chr>  <chr>     <dbl>    <dbl>
#>  1 ulm       T1     S01        4.21 0.00180 
#>  2 ulm       T1     S02        4.07 0.00224 
#>  3 ulm       T1     S03        3.85 0.00319 
#>  4 ulm       T1     S04        4.60 0.000979
#>  5 ulm       T1     S05        3.90 0.00298 
#>  6 ulm       T1     S06        3.66 0.00442 
#>  7 ulm       T1     S07        4.31 0.00153 
#>  8 ulm       T1     S08        4.65 0.000902
#>  9 ulm       T1     S09        4.49 0.00117 
#> 10 ulm       T1     S10        4.07 0.00225 
#> # ℹ 62 more rows