How to Install and Uninstall r-bioc-sva Package on Ubuntu 21.10 (Impish Indri)

Last updated: May 03,2024

1. Install "r-bioc-sva" package

This guide let you learn how to install r-bioc-sva on Ubuntu 21.10 (Impish Indri)


    
        
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                sudo apt update
            
                    
    
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            $
        

        
            
                sudo apt install
            
                        
                r-bioc-sva
            
                    
    
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2. Uninstall "r-bioc-sva" package

Please follow the steps below to uninstall r-bioc-sva on Ubuntu 21.10 (Impish Indri):


    
        
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                sudo apt remove
            
                        
                r-bioc-sva
            
                    
    
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            $
        

        
            
                sudo apt autoclean && sudo apt autoremove
            
                    
    
    Copied

3. Information about the r-bioc-sva package on Ubuntu 21.10 (Impish Indri)

Package: r-bioc-sva
Architecture: amd64
Version: 3.38.0-1
Priority: optional
Section: universe/gnu-r
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian R Packages Maintainers
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 967
Depends: r-base-core (>= 4.0.3-1), r-api-4.0, r-api-bioc-3.12, r-cran-mgcv, r-bioc-genefilter, r-bioc-biocparallel, r-cran-matrixstats, r-bioc-limma, r-bioc-edger
Recommends: r-cran-testthat, r-bioc-bladderbatch
Suggests: r-bioc-biocstyle
Filename: pool/universe/r/r-bioc-sva/r-bioc-sva_3.38.0-1_amd64.deb
Size: 447344
MD5sum: c1b14732e8ef0305f5b933aa1458ef1e
SHA1: 6c53638fa0697e3ad998b091d6b5111facd7cdd6
SHA256: 9edd46e9bc43f652f5530d239cd01cd67734ba0dca629ad45c8ab1a6abface5b
SHA512: c8bcbc4f07b16cf284b4b80a15ff9a8e342567dbb02ec7cdfb9adac333ed1dc2ca2187064eff83e53cac06d31076db7b54ebdf57d8f19051bff20a335b7013bb
Homepage: https://bioconductor.org/packages/sva/
Description-en: GNU R Surrogate Variable Analysis
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).
Description-md5: c05f11b1bfa8dfefd1edeafaec2b11d0