decouple {decoupleR} | R Documentation |
Calculate the source activity per sample out of a gene expression matrix by coupling a regulatory network with a variety of statistics.
decouple( mat, network, .source = .data$source, .target = .data$target, statistics = c("udt", "mdt", "aucell", "wmean", "wsum", "ulm", "mlm", "viper", "gsva", "ora", "fgsea"), args = list(NULL), consensus_score = TRUE, 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 |
consensus_score |
Boolean whether to run a consensus score between methods. Obtained scores are -log10(p-values). |
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 source across the samples. Resulting tibble contains the following columns:
run_id
: Indicates the order in which the methods have been executed.
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).
statistic_time
: If requested, internal execution time indicator.
p_value
: p-value (if available) of the obtained score.
Other decoupleR statistics:
run_aucell()
,
run_fgsea()
,
run_gsva()
,
run_mdt()
,
run_mlm()
,
run_ora()
,
run_udt()
,
run_ulm()
,
run_viper()
,
run_wmean()
,
run_wsum()
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", "wmean", "wsum", "ulm", "aucell"), args = list( gsva = list(verbose = FALSE), wmean = list(.mor = "mor", .likelihood = "likelihood"), wsum = list(.mor = "mor"), ulm = list(.mor = "mor") ) ) }