run_mlm {decoupleR} | R Documentation |
Calculates regulatory activities by fitting multivariate linear models (MLM)
run_mlm( 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 |
MLM fits a multivariate linear model to estimate regulatory activities. MLM transforms a given network into an adjacency matrix, placing sources as columns and targets as rows. The matrix is filled with the associated weights for each interaction. This matrix is used to fit a linear model to predict the observed molecular readouts per sample. The obtained t-values from the fitted model are the activities of the regulators.
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_ora()
,
run_udt()
,
run_ulm()
,
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_mlm(mat, network, .source='tf')