viper (version 1.6.0)

viper: VIPER

Description

This function performs Virtual Inference of Protein-activity by Enriched Regulon analysis

Usage

viper(eset, regulon, dnull = NULL, pleiotropy = FALSE, nes = TRUE, method = c("scale", "rank", "mad", "ttest", "none"), bootstraps = 0, minsize = 25, adaptive.size = FALSE, eset.filter = TRUE, pleiotropyArgs = list(regulators = 0.05, shadow = 0.05, targets = 10, penalty = 20, method = "adaptive"), cores = 1, verbose = TRUE)

Arguments

eset

ExpressionSet object or Numeric matrix containing the expression data or gene expression signatures, with samples in columns and genes in rows

regulon

Object of class regulon

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.

minsize

Integer indicating the minimum number of targets allowed per regulon

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

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 matrix of inferred activity for each regulator gene in the network across all samples

See Also

msviper

Examples

data(bcellViper, package="bcellViper")
d1 <- exprs(dset)
res <- viper(d1, regulon)
dim(d1)
d1[1:5, 1:5]
regulon
dim(res)
res[1:5, 1:5]