Jeffrey T. Leek <jtleek@gmail.com>, W. Evan Johnson <wej@bu.edu>,
    Hilary S. Parker <hiparker@jhsph.edu>, Elana J. Fertig <ejfertig@jhmi.edu>,
    Andrew E. Jaffe <ajaffe@jhsph.edu>, John D. Storey <jstorey@princeton.edu>,
    Yuqing Zhang <zhangyuqing.pkusms@gmail.com>,
    Leonardo Collado Torres <lcollado@jhu.edu>
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).
Jeffrey T. Leek <jtleek@gmail.com>, John D. Storey
<jstorey@princeton.edu>, W. Evan Johnson <wej@bu.edu>
R (>= 3.2), mgcv, genefilter, BiocParallel
matrixStats, stats, graphics, utils, limma,
pamr, bladderbatch, BiocStyle, zebrafishRNASeq, testthat
Microarray, StatisticalMethod, Preprocessing,
MultipleComparison, Sequencing, RNASeq, BatchEffect,
Normalization