run_pscira {decoupleR}R Documentation

PSCIRA (Permutation Single Cell Inference of Regulatory Activity)

Description

Calculate the regulatory activity of each tf by multiplying the expression values of its objectives with their corresponding associated profiles for each given condition.The result is equal to the z-score of the found value compared to its null distribution.

Usage

run_pscira(
  mat,
  network,
  .source = .data$tf,
  .target = .data$target,
  .mor = .data$mor,
  sparse = TRUE,
  times = 10,
  seed = 42
)

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).

sparse

Logical value indicating if the generated profile matrix should be sparse.

times

Number of replications.

seed

A single value, interpreted as an integer, or NULL.

Details

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.

Value

A long format tibble of the enrichment scores for each tf across the samples. Resulting tibble contains the following columns:

  1. statistic: Indicates which method is associated with which score.

  2. tf: Source nodes of network.

  3. condition: Condition representing each column of mat.

  4. score: Regulatory activity (enrichment score).

See Also

Other decoupleR statistics: decouple(), run_gsva(), run_mean(), run_ora(), run_scira(), run_viper()

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_pscira(mat, network, tf, target, mor)

[Package decoupleR version 1.0.0 Index]