ISVA (Independent Surrogate Variable Analysis) algorithm

We present a modified SVA, called Independent Surrogate Variable Analysis (ISVA), to identify features correlating with a phenotype of interest in the presence of potential confounding factors. ISVA should be useful as a feature selection tool in studies that are subject to confounding.


[CRAN Link]

isva: Independent Surrogate Variable Analysis

Independent Surrogate Variable Analysis is an algorithm for feature selection in the presence of potential confounding factors.

Version: 1.8

Depends: qvalue, fastICA

Published: 2013-11-04

Maintainer: Andrew Teschendorff

License: GPL-2

NeedsCompilation: no

CRAN checks: isva results


Teschendorff AE, Zhuang JJ, Widschwendter M. “Independent Surrogate Variable Analysis to deconvolve confounding factors in large-scale microarray profiling studies.” Bioinformatics. 2011 Jun 1;27(11):1496-505.

PDF: Reference Manual