decouple {decoupleR} | R Documentation |
Calculate the TF activity per sample out of a gene expression matrix by coupling a regulon network with a variety of statistics.
decouple( mat, network, .source, .target, statistics, args = list(NULL), include_time = FALSE, show_toy_call = 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. |
statistics |
Statistical methods to be coupled. |
args |
A list of argument-lists the same length as |
include_time |
Should the time per statistic evaluated be informed? |
show_toy_call |
The call of each statistic must be informed? |
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).
statistic_time
: If requested, internal execution time indicator.
...
: Columns of metadata generated by certain statistics.
Other decoupleR statistics:
run_gsva()
,
run_mean()
,
run_ora()
,
run_pscira()
,
run_scira()
,
run_viper()
if (FALSE) { 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")) decouple( mat = mat, network = network, .source = "tf", .target = "target", statistics = c("gsva", "mean", "pscira", "scira", "viper"), args = list( gsva = list(verbose = FALSE), mean = list(.mor = "mor", .likelihood = "likelihood"), pscira = list(.mor = "mor"), scira = list(.mor = "mor"), viper = list( .mor = "mor", .likelihood = "likelihood", verbose = FALSE ) ) ) }