bootstrapViper {viper} | R Documentation |
This function performs a viper analysis with bootstraps
bootstrapViper(eset, regulon, nes = TRUE, bootstraps = 10, eset.filter = FALSE, adaptive.size = TRUE, minsize = 20, mvws = 1, cores = 1, verbose = TRUE)
eset |
ExpressionSet object or Numeric matrix containing the expression data, with samples in columns and genes in rows |
regulon |
Object of class regulon |
nes |
Logical, whether the enrichment score reported should be normalized |
bootstraps |
Integer indicating the number of bootstraps iterations to perform. Only the scale method is implemented with bootstraps. |
eset.filter |
Logical, whether the dataset should be limited only to the genes represented in the interactome |
adaptive.size |
Logical, whether the weighting scores should be taken into account for computing the regulon size |
minsize |
Integer indicating the minimum number of targets allowed per regulon |
mvws |
Number or vector indicating either the exponent score for the metaViper weights, or the inflection point and trend for the sigmoid function describing the weights in metaViper |
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 |
A list containing a matrix of inferred activity for each regulator gene in the network across all samples and the corresponding standard deviation computed from the bootstrap iterations.
data(bcellViper, package="bcellViper") d1 <- exprs(dset) res <- viper(d1[, 1:50], regulon, bootstraps=10) # Run only on 50 samples to reduce computation time dim(d1) d1[1:5, 1:5] regulon dim(res$nes) res$nes[1:5, 1:5] res$sd[1:5, 1:5]