Description Usage Arguments Value See Also Examples
This function performs shadow analysis on msviper objects
1 2 3 |
mobj |
msviper object generated by |
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
1 2 3 4 5 6 | 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)
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