Package

r-sva

Description

Surrogate Variable Analysis. The sva package contains functions for
removing batch effects and other unwanted variation in high-throughput
experiment. Specifically, the sva package contains functions for the
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. The sva package can be
used to remove artifacts in three ways: (1) identifying and estimating
surrogate variables for unknown sources of variation in high-throughput
experiments (Leek and Storey 2007 PLoS Genetics,2008 PNAS), (2) directly
removing known batch effects using ComBat (Johnson et al. 2007
Biostatistics) and (3) removing batch effects with known control probes
(Leek 2014 biorXiv). Removing batch effects and using surrogate
variables in differential expression analysis have been shown to reduce
dependence, stabilize error rate estimates, and improve reproducibility,
see (Leek and Storey 2007 PLoS Genetics, 2008 PNAS or Leek et al. 2011
Nat. Reviews Genetics).

Note