run_scira {decoupleR} | R Documentation |
Calculates TF activity according to Improved detection of tumor suppressor events in single-cell RNA-Seq data .
run_scira( mat, network, .source = .data$tf, .target = .data$target, .mor = .data$mor, sparse = FALSE, fast = TRUE, center = TRUE, 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). |
sparse |
Logical value indicating if the generated profile matrix should be sparse. |
fast |
Logical value indicating if the lineal model must be calculated
with |
center |
Logical value indicating if |
na.rm |
Should missing values (including NaN) be omitted from the
calculations of |
Estimation of regulatory activity: A linear regression of the expression
profile is performed against the "target profile" of the given TF, where
in the target profile, any regulon member is assigned a +1
for activating
interactions and a -1
for inhibitory interactions. All other genes not
members of the TF's regulon are assigned a value o 0
. TF activity is then
defined as the t-statistic of this linear regression.
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).
Other decoupleR statistics:
decouple()
,
run_gsva()
,
run_mean()
,
run_ora()
,
run_pscira()
,
run_viper()
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_scira(mat, network, tf, target, mor)