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.
Maintainer: Andrew E. Teschendorff
Citation
- Teschendorff AE, Zhuang J, Widschwendter M. Independent surrogate variable analysis to deconvolve confounding factors in large-scale microarray profiling studies. Bioinformatics 2011 Jun 1;27(11):1496-505.
Package: 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
License: GPL-2
NeedsCompilation: no
CRAN checks: isva results
PDF: Reference Manual