isvaFn {isva}

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:

n.isv

Number of inferred ISVs.

isv

Matrix of inferred ISVs.

Author(s)

Andrew E Teschendorff

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.

Examples

## see example for DoISVA