run_viper {decoupleR} | R Documentation |
VIPER wrapper
Description
This function is a convenient wrapper for the viper::viper()
function.
Usage
run_viper(
mat,
network,
.source = .data$tf,
.target = .data$target,
.mor = .data$mor,
.likelihood = .data$likelihood,
...
)
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 |
Column with edge likelihood.
|
... |
Arguments passed on to viper::viper
dnull Numeric matrix for the null model, usually generated by nullTtest
pleiotropy Logical, whether correction for pleiotropic regulation should be performed
nes Logical, whether the enrichment score reported should be normalized
method Character string indicating the method for computing the single samples signature, either scale, rank, mad, ttest or none
bootstraps Integer indicating the number of bootstraps iterations to perform. Only the scale method is implemented with bootstraps.
adaptive.size Logical, whether the weighting scores should be taken into account for computing the regulon size
eset.filter Logical, whether the dataset should be limited only to the genes represented in the interactome
#' @param mvws Number or vector indicating either the exponent score for the metaViper weights, or the inflection point and trend for the sigmoid function describing the weights in metaViper
pleiotropyArgs list of 5 numbers for the pleotropy correction indicating: regulators p-value threshold, pleiotropic interaction p-value threshold, minimum number of targets in the overlap between pleiotropic regulators, penalty for the pleiotropic interactions and the method for computing the pleiotropy, either absolute or adaptive
cores Integer indicating the number of cores to use (only 1 in Windows-based systems)
verbose Logical, whether progression messages should be printed in the terminal
|
Value
A long format tibble of the enrichment scores for each tf
across the samples. Resulting tibble contains the following columns:
-
statistic
: Indicates which method is associated with which score.
-
tf
: Source nodes of network
.
-
condition
: Condition representing each column of mat
.
-
score
: Regulatory activity (enrichment score).
See Also
Other decoupleR statistics:
decouple()
,
run_gsva()
,
run_mean()
,
run_ora()
,
run_pscira()
,
run_scira()
Examples
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_viper(mat, network, tf, target, mor, likelihood, verbose = FALSE)
[Package
decoupleR version 1.0.0
Index]