viper (version 1.6.0)

shadow: Shadow analysis for msviper objects

Description

This function performs shadow analysis on msviper objects

Usage

shadow(mobj, regulators = 0.01, targets = 10, shadow = 0.01, per = 1000, nullmodel = NULL, minsize = NULL, adaptive.size = NULL, iterative = NULL, seed = 1, cores = 1, verbose = TRUE)

Arguments

mobj

msviper object generated by msviper

regulators

This parameter represent different ways to select a subset of regulators for performing the shadow analysis, it can be either a p-value cutoff, the total number of regulons to be used for computing the shadow effect, or a vector of regulator ids to be considered

targets

Integer indicating the minimum number of common targets to compute shadow analysis

shadow

Number indicating the p-value threshold for the shadow effect

per

Integer indicating the number of permutations

nullmodel

Null model in marix format

minsize

Integer indicating the minimum size allowed for the regulons

adaptive.size

Logical, whether the target weight should be considered when computing the regulon size

iterative

Logical, whether a two step analysis with adaptive redundancy estimation should be performed

seed

Integer indicating the seed for the permutations, 0 for disable it

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

An updated msviper object with an additional slot (shadow) containing the shadow pairs

See Also

msviper

Examples

data(bcellViper, package="bcellViper")
sig <- rowTtest(dset, "description", c("CB", "CC"), "N")$statistic
dnull <- ttestNull(dset, "description", c("CB", "CC"), "N", per=100) # Only 100 permutations to reduce computation time, but it is recommended to perform at least 1000 permutations
mra <- msviper(sig, regulon, dnull)
mra <- shadow(mra, regulators=10)
summary(mra)