run.rnb.ICA | R Documentation |
Performs ICA to remove the effect of a confounding factor from the methylation matrix
run.rnb.ICA(
rnb.set,
conf.factor,
ica.setting = NULL,
nmin = 10,
nmax = 30,
ntry = 1,
thr.sd = 0,
alpha.fact = 1e-10,
type = "remove",
ncores = 1,
out.folder = NULL,
save.report = F,
alpha.feat = 0.01
)
rnb.set |
An object of type |
conf.factor |
A vector of column names in the sample annotation sheet of |
ica.setting |
Optional argument. If specified a named vector of arguments to be used. Can be one of the following. |
nmin |
Minimum number of components to be used |
nmax |
Maximum number of components to be used |
ntry |
Further argument for runICA |
thr.sd |
Threshold for the standard deviation across samples. Only sites with a standard deviation larger than this threshold are kept. 0 deactivates filtering. |
alpha.fact |
Significance level for the factor |
type |
Analysis type to be performed. Can be either |
ncores |
The number of cores used |
out.folder |
Folder to store ICA's output and diagnostic plots |
save.report |
Flag indicating if a report is to be created and stored for ICA |
alpha.feat |
Significance level detecting features contributing to components |
The modified rnb.set
object with updated methylation values. The effect of the confouding factor is removed
using independent component analysis (ICA).
Michael Scherer. ICA code was generated by Peter Nazarov and Tony Kamoa
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