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Open Access Open Badges Software review

SVAw - a web-based application tool for automated surrogate variable analysis of gene expression studies

Mehdi Pirooznia1, Fayaz Seifuddin1, Fernando S Goes1, Jeffrey T Leek2 and Peter P Zandi13*

Author Affiliations

1 Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA

2 Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

3 Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

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Source Code for Biology and Medicine 2013, 8:8  doi:10.1186/1751-0473-8-8

Published: 11 March 2013



Surrogate variable analysis (SVA) is a powerful method to identify, estimate, and utilize the components of gene expression heterogeneity due to unknown and/or unmeasured technical, genetic, environmental, or demographic factors. These sources of heterogeneity are common in gene expression studies, and failing to incorporate them into the analysis can obscure results. Using SVA increases the biological accuracy and reproducibility of gene expression studies by identifying these sources of heterogeneity and correctly accounting for them in the analysis.


Here we have developed a web application called SVAw (Surrogate variable analysis Web app) that provides a user friendly interface for SVA analyses of genome-wide expression studies. The software has been developed based on open source bioconductor SVA package. In our software, we have extended the SVA program functionality in three aspects: (i) the SVAw performs a fully automated and user friendly analysis workflow; (ii) It calculates probe/gene Statistics for both pre and post SVA analysis and provides a table of results for the regression of gene expression on the primary variable of interest before and after correcting for surrogate variables; and (iii) it generates a comprehensive report file, including graphical comparison of the outcome for the user.


SVAw is a web server freely accessible solution for the surrogate variant analysis of high-throughput datasets and facilitates removing all unwanted and unknown sources of variation. It is freely available for use at webcite. The executable packages for both web and standalone application and the instruction for installation can be downloaded from our web site.