sva

DOI: 10.18129/B9.bioc.sva    

Surrogate Variable Analysis

Bioconductor version: Release (3.6)

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>, Yuqing Zhang <zhangyuqing.pkusms at gmail.com>, Leonardo Collado Torres <lcollado at jhu.edu>

Maintainer: Jeffrey T. Leek <jtleek at gmail.com>, John D. Storey <jstorey at princeton.edu>, W. Evan Johnson <wej at bu.edu>

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

Installation

To install this package, start R and enter:

## try http:// if https:// URLs are not supported
source("https://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 3.26.0
In Bioconductor since BioC 2.9 (R-2.14) (6.5 years)
License Artistic-2.0
Depends R (>= 3.2), mgcv, genefilter, BiocParallel
Imports matrixStats, stats, graphics, utils, limma
LinkingTo
Suggests pamr, bladderbatch, BiocStyle, zebrafishRNASeq, testthat
SystemRequirements
Enhances
URL
Depends On Me SCAN.UPC
Imports Me ASSIGN, ballgown, BatchQC, bnbc, ChAMP, charm, crossmeta, DaMiRseq, debrowser, DeSousa2013, doppelgangR, edge, ENmix, LINC, omicRexposome, PAA, PROPS, TCGAbiolinks, trigger
Suggests Me curatedBladderData, curatedCRCData, curatedOvarianData, Harman, RnBeads, SomaticSignatures
Build Report  

Package Archives

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

Source Package sva_3.26.0.tar.gz
Windows Binary sva_3.26.0.zip (32- & 64-bit)
Mac OS X 10.11 (El Capitan) sva_3.26.0.tgz
Source Repository git clone https://git.bioconductor.org/packages/sva
Package Short Url http://bioconductor.org/packages/sva/
Package Downloads Report Download Stats

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