http://www.bioconductor.org/packages/2.12/bioc/vignettes/sva/inst/doc/sva.pdf
The sva package contains functions for removing batch e ects and other unwanted variation
in high-throughput experiments. Speci cally, the sva package contains functions for identifying and building surrogate variables for high-dimensional data sets. Surrogate variables
are covariates constructed directly from high-dimensional data (like gene expression/RNA
sequencing/methylation/brain imaging data) that can be used in subsequent analyses to
adjust for unknown, unmodeled, or latent sources of noise.
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