Description Usage Arguments Value See Also Examples
This function performs MAster Regulator INference Analysis
1 2 3 4 |
ges |
Vector containing the gene expression signature to analyze, or matrix with columns containing bootstraped signatures |
regulon |
Object of class regulon |
nullmodel |
Matrix of genes by permutations containing the NULL model signatures. A parametric approach equivalent to shuffle genes will be used if nullmodel is ommitted. |
pleiotropy |
Logical, whether correction for pleiotropic regulation should be performed |
minsize |
Number indicating the minimum allowed size for the regulons |
adaptive.size |
Logical, whether the weight (likelihood) should be used for computing the regulon size |
ges.filter |
Logical, whether the gene expression signature should be limited to the genes represented in the interactome |
synergy |
Number indicating the synergy computation mode: (0) for no synergy computation; (0-1) for establishing the p-value cutoff for individual TFs to be included in the synergy analysis; (>1) number of top TFs to be included in the synergy analysis |
level |
Integer, maximum level of combinatorial regulation |
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 pleiotropy analysis method, 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 |
A msviper object containing the following components:
The gene expression signature
The final regulon object used
Enrichment analysis results including regulon size, normalized enrichment score and p-value
msviper parameters, including minsize
, adaptive.size
1 2 3 4 5 | 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)
plot(mra, cex=.7)
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