run_ulm {decoupleR} | R Documentation |
Calculates regulatory activities by fitting univariate linear models (ULM).
run_ulm( mat, network, .source = .data$source, .target = .data$target, .mor = .data$mor, .likelihood = .data$likelihood, sparse = FALSE, center = FALSE, na.rm = FALSE )
mat |
Matrix to evaluate (e.g. expression matrix).
Target nodes in rows and conditions in columns.
|
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 |
Column with edge likelihood. |
sparse |
Logical value indicating if the generated profile matrix should be sparse. |
center |
Logical value indicating if |
na.rm |
Should missing values (including NaN) be omitted from the
calculations of |
ULM fits a (univariate) linear model to estimate regulatory activities. ULM fits a linear model that predicts the observed molecular readouts using the given weights of a regulator as a single co-variate. The obtained t-value from the fitted model is the activity of the regulator. This approach was first described in: Improved detection of tumor suppressor events in single-cell RNA-Seq data.
A long format tibble of the enrichment scores for each source across the samples. Resulting tibble contains the following columns:
statistic
: Indicates which method is associated with which score.
source
: Source nodes of network
.
condition
: Condition representing each column of mat
.
score
: Regulatory activity (enrichment score).
Other decoupleR statistics:
decouple()
,
run_aucell()
,
run_fgsea()
,
run_gsva()
,
run_mdt()
,
run_mlm()
,
run_ora()
,
run_udt()
,
run_viper()
,
run_wmean()
,
run_wsum()
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_ulm(mat, network, .source='tf')