isvaFn: Main engine function for inference of independent surrogate variables (ISVs)
Description
This is the main engine function which infers the statistically
independent surrogate variables (ISVs) by performing Independent
Component Analysis (ICA) on the residual variation matrix. It uses
either the ICA implementation of JADE or the one from the fastICA R-package. The residual variation matrix reflects the variation orthogonal to that of a phenotype of interest and is inferred using a linear model.
Usage
isvaFn(data.m, pheno.v, ncomp = NULL,icamethod)
Arguments
data.m
Data matrix. Rows label features. Columns label samples.
pheno.v
Numeric vector encoding phenotype of interest.
ncomp
Optionally specify number of ISVs to look for. By default
will use Approximate Random Matrix Theory to infer this number.
icamethod
The ICA method to be used. Input value is taken from DoISVA.
Value
A list with following entries:
References
Independent Surrogate Variable Analysis to deconvolve confounding factors in large-scale microarray profiling studies. Teschendorff AE, Zhuang JJ, Widschwendter M. Bioinformatics. 2011 Jun 1;27(11):1496-505.