BIOCONDUCTOR . ORG {
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Title:
Bioconductor - sva
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).
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Keywords {🔍}
package, bioconductor, sva, version, leek, packages, surrogate, storey, release, removing, batch, effects, variables, johnson, gmailcom, source, analysis, data, genetics, enter, install, binary, learn, developers, started, software, variable, functions, variation, highthroughput, identifying, highdimensional, directly, unknown, sources, plos, pnas, control, jeffrey, jtleek, evan, wej, buedu, parker, jhsphedu, jaffe, zhang, john, jstorey, princetonedu,
Topics {✒️}
org/packages/sva source repository high-dimensional data sets bioconductor release tgz source repository org/packages/sva source package control probes differential expression analysis high-dimensional data european bioconductor conference release //code surrogate variable analysis bioconductor support site high-throughput experiment high-throughput experiments script reference manual gz windows binary zip macos binary tgz macos binary covariates constructed directly building surrogate variables estimating surrogate variables removing batch effects sva package git clone https details biocviews edger system requirements package installed package version 3 started install packages package batch effects surrogate variables directly removing torres lc sva tutorial reviews genetics //bioconductor bioconductor sequencing storey jd registration open subsequent analyses latent sources remove artifacts reduce dependence improve reproducibility parker hs
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