shadow: Shadow analysis for msviper objects
In viper: Virtual Inference of Protein-activity by Enriched Regulon analysis

Description Usage Arguments Value See Also Examples

This function performs shadow analysis on msviper objects

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)

`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

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

msviper

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)