sva
Surrogate Variable Analysis
Bioconductor version: Release (3.17)
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>
citation("sva")):
Installation
To install this package, start R (version "4.3") 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")| sva tutorial | R Script | |
| Reference Manual |
Details
| biocViews | BatchEffect, ImmunoOncology, Microarray, MultipleComparison, Normalization, Preprocessing, RNASeq, Sequencing, Software, StatisticalMethod |
| Version | 3.48.0 |
| In Bioconductor since | BioC 2.9 (R-2.14) (12 years) |
| License | Artistic-2.0 |
| Depends | R (>= 3.2), mgcv, genefilter, BiocParallel |
| Imports | matrixStats, stats, graphics, utils, limma, edgeR |
| Linking To | |
| Suggests | pamr, bladderbatch, BiocStyle, zebrafishRNASeq, testthat |
| System Requirements | |
| Enhances | |
| URL |
See More
| Depends On Me | DeMixT, IsoformSwitchAnalyzeR, rnaseqGene, SCAN.UPC |
| Imports Me | ASSIGN, ballgown, BatchQC, BioNERO, bnbc, bnem, ChAMP, crossmeta, DaMiRseq, debrowser, DeSousa2013, DExMA, doppelgangR, edge, ExpressionNormalizationWorkflow, KnowSeq, MBECS, MSPrep, omicRexposome, PAA, pairedGSEA, proBatch, PROPS, qsmooth, qsvaR, SEtools, singleCellTK, trigger |
| Suggests Me | CAGEWorkflow, compcodeR, 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_3.48.0.tar.gz |
| Windows Binary | sva_3.48.0.zip |
| macOS Binary (x86_64) | sva_3.48.0.tgz |
| macOS Binary (arm64) | sva_3.48.0.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 |