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ISVA | To deconvolve confounding factors in large-scale microarray profiling studies


Identifies features correlating with a phenotype of interest in the presence of potential confounding factors. Using simulated data, we show that ISVA performs well in identifying confounders as well as outperforming methods which do not adjust for confounding. Using four large-scale Illumina Infinium DNA methylation datasets subject to low signal to noise ratios and substantial confounding by beadchip effects and variable bisulfite conversion efficiency, we show that ISVA improves the identifiability of confounders and that this enables a framework for feature selection that is more robust to model misspecification and heterogeneous phenotypes. Finally, we demonstrate similar improvements of ISVA across four mRNA expression datasets. Thus, ISVA should be useful as a feature selection tool in studies that are subject to confounding.

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ISVA classification

ISVA specifications

Unique identifier:
Software type:
Restrictions to use:
Programming languages:
Computer skills:
Independent Surrogate Variable Analysis
Command line interface
Operating system:
Unix/Linux, Mac OS, Windows
GNU General Public License version 2.0
qvalue, fastICA

ISVA distribution


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ISVA support



  • Andrew Teschendorff <>


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Statistical Genomics Group, Paul O'Gorman Building, UCL Cancer Institute, London, UK; Department of Gynecological Oncology, UCL Elizabeth Garrett Anderson Institute for Women's Health, University College London, London, UK

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