Package: sva
Title: Surrogate Variable Analysis
Version: 3.61.0
Author: 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>,
        Yuqing Zhang <zhangyuqing.pkusms@gmail.com>, John D. Storey
        <jstorey@princeton.edu>, Leonardo Collado Torres
        <lcolladotor@gmail.com>
Description: 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).
Maintainer: Jeffrey T. Leek <jtleek@gmail.com>, John D. Storey
 <jstorey@princeton.edu>, W. Evan Johnson <wej@bu.edu>
Depends: R (>= 3.2), mgcv, genefilter, BiocParallel
Imports: matrixStats, stats, graphics, utils, limma, edgeR
Suggests: pamr, bladderbatch, BiocStyle, zebrafishRNASeq, testthat
License: Artistic-2.0
biocViews: ImmunoOncology, Microarray, StatisticalMethod,
        Preprocessing, MultipleComparison, Sequencing, RNASeq,
        BatchEffect, Normalization
RoxygenNote: 7.0.2
Config/pak/sysreqs: libpng-dev libxml2-dev libssl-dev zlib1g-dev
Repository: https://bioc.r-universe.dev
Date/Publication: 2026-04-28 12:35:58 UTC
RemoteUrl: https://github.com/bioc/sva
RemoteRef: HEAD
RemoteSha: 308e2ef22014a7e299fe3f7c7bca81b230a5bddf
NeedsCompilation: yes
Packaged: 2026-06-23 15:11:10 UTC; root