Article

The SVA package for removing batch effects and other unwanted variation in high-throughput experiments

Department of Biostatistics, JHU Bloomberg School of Public Health, Baltimore, MD, USA.
Bioinformatics (Impact Factor: 4.98). 01/2012; 28(6):882-3. DOI: 10.1093/bioinformatics/bts034
Source: PubMed

ABSTRACT

Heterogeneity and latent variables are now widely recognized as major sources of bias and variability in high-throughput
experiments. The most well-known source of latent variation in genomic experiments are batch effects—when samples are processed
on different days, in different groups or by different people. However, there are also a large number of other variables that
may have a major impact on high-throughput measurements. Here we describe the sva package for identifying, estimating and removing unwanted sources of variation in high-throughput experiments. The sva package supports surrogate variable estimation with the sva function, direct adjustment for known batch effects with the ComBat function and adjustment for batch and latent variables in prediction problems with the fsva function.

Availability: The R package sva is freely available from http://www.bioconductor.org.

Contact: jleek{at}jhsph.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

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Available from: William Evan Johnson, Jan 06, 2014
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