sva

DOI: 10.18129/B9.bioc.sva  

Surrogate Variable Analysis

Bioconductor version: Release (3.16)

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>, Yuqing Zhang <zhangyuqing.pkusms at gmail.com>, John D. Storey <jstorey at princeton.edu>, Leonardo Collado Torres <lcolladotor at gmail.com>

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

Installation

To install this package, start R (version "4.2") and enter:

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("sva")

For older versions of R, please refer to the appropriate Bioconductor release.

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, ImmunoOncology, Microarray, MultipleComparison, Normalization, Preprocessing, RNASeq, Sequencing, Software, StatisticalMethod
Version	3.46.0
In Bioconductor since	BioC 2.9 (R-2.14) (11.5 years)
License	Artistic-2.0
Depends	R (>= 3.2), mgcv, genefilter, BiocParallel
Imports	matrixStats, stats, graphics, utils, limma, edgeR
LinkingTo
Suggests	pamr, bladderbatch, BiocStyle, zebrafishRNASeq, testthat
SystemRequirements
Enhances
URL
Depends On Me	DeMixT, rnaseqGene, SCAN.UPC
Imports Me	ASSIGN, ballgown, BatchQC, BioNERO, bnbc, bnem, ChAMP, crossmeta, CytoTree, DaMiRseq, debrowser, DeSousa2013, DExMA, doppelgangR, edge, ExpressionNormalizationWorkflow, KnowSeq, MBECS, MSPrep, omicRexposome, PAA, proBatch, PROPS, qsmooth, qsvaR, SEtools, singleCellTK, trigger
Suggests Me	CAGEWorkflow, curatedBladderData, curatedCRCData, curatedOvarianData, curatedTBData, FieldEffectCrc, Harman, iasva, MAGeCKFlute, randRotation, RnBeads, scp, SomaticSignatures, TBSignatureProfiler, TCGAbiolinks, tidybulk
Links To Me
Build Report

Package Archives

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

Source Package	sva_3.46.0.tar.gz
Windows Binary	sva_3.46.0.zip
macOS Binary (x86_64)	sva_3.46.0.tgz
macOS Binary (arm64)	sva_3.46.0.tgz
Source Repository	git clone https://git.bioconductor.org/packages/sva
Source Repository (Developer Access)	git clone git@git.bioconductor.org:packages/sva
Bioc Package Browser	https://code.bioconductor.org/browse/sva/
Package Short Url	https://bioconductor.org/packages/sva/
Package Downloads Report	Download Stats