The <tt>sva</tt> package for removing batch effects and other unwanted variation in high-throughput experiments

TLDR

Heterogeneity and latent variables, including batch effects and other unknown factors, are major sources of bias and variability in high‑throughput experiments. The sva package was developed to identify, estimate, and remove unwanted sources of variation in high‑throughput experiments. It provides surrogate variable estimation via the sva function, direct batch adjustment with ComBat, and adjustment for batch and latent variables in prediction problems using fsva. The sva package is freely available from Bioconductor (http://www.bioconductor.org), contact is [e-mail], and supplementary data are online.

Abstract

Abstract Summary: 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: [e-mail] Supplementary information: Supplementary data are available at Bioinformatics online.