To install this package, start R and enter:

source("http://bioconductor.org/biocLite.R")
biocLite("sva")

In most cases, you don't need to download the package archive at all.

sva

Surrogate Variable Analysis

Bioconductor version: Release (3.0)

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).

Author: Jeffrey T. Leek <jtleek at gmail.com>, W. Evan Johnson <wej at bu.edu>, Hilary S. Parker <hiparker at jhsph.edu>, Elana J. Fertig <ejfertig at jhmi.edu>, Andrew E. Jaffe <ajaffe at jhsph.edu>, John D. Storey <jstorey at princeton.edu>

Maintainer: Jeffrey T. Leek <jtleek at gmail.com>, John D. Storey <jstorey at princeton.edu>

Citation (from within R, enter citation("sva")):

Installation

To install this package, start R and enter:

source("http://bioconductor.org/biocLite.R")
biocLite("sva")

Documentation

To view documentation for the version of this package installed in your system, start R and enter:

browseVignettes("sva")

 

PDF R Script sva tutorial
PDF   Reference Manual

Details

biocViews BatchEffect, Microarray, MultipleComparison, Normalization, Preprocessing, RNASeq, Sequencing, Software, StatisticalMethod
Version 3.12.0
In Bioconductor since BioC 2.9 (R-2.14)
License Artistic-2.0
Depends R (>= 2.8), mgcv, genefilter
Imports
Suggests limma, pamr, bladderbatch, BiocStyle, zebrafishRNASeq
System Requirements
URL
Depends On Me SCAN.UPC
Imports Me ballgown, ChAMP, charm, DeSousa2013, PAA, trigger
Suggests Me curatedBladderData, curatedCRCData, curatedOvarianData

Package Archives

Follow Installation instructions to use this package in your R session.

Package Source sva_3.12.0.tar.gz
Windows Binary sva_3.12.0.zip (32- & 64-bit)
Mac OS X 10.6 (Snow Leopard) sva_3.12.0.tgz
Mac OS X 10.9 (Mavericks) sva_3.12.0.tgz
Browse/checkout source (username/password: readonly)
Package Downloads Report Download Stats

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