How to Install and Uninstall r-bioc-sva Package on Ubuntu 20.10 (Groovy Gorilla)

Last updated: December 28,2024

1. Install "r-bioc-sva" package

Learn how to install r-bioc-sva on Ubuntu 20.10 (Groovy Gorilla)


    
        
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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

Here is a brief guide to show you how to uninstall r-bioc-sva on Ubuntu 20.10 (Groovy Gorilla):


    
        
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                sudo apt remove
            
                        
                r-bioc-sva
            
                    
    
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            $
        

        
            
                sudo apt autoclean && sudo apt autoremove
            
                    
    
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3. Information about the r-bioc-sva package on Ubuntu 20.10 (Groovy Gorilla)

Package: r-bioc-sva
Architecture: amd64
Version: 3.36.0-1build1
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 (>= 4.0.0.20200528-1), r-api-4.0, r-api-bioc-3.11, 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
Filename: pool/universe/r/r-bioc-sva/r-bioc-sva_3.36.0-1build1_amd64.deb
Size: 447440
MD5sum: 2454bba93c2805b31f56abdd366a6e1b
SHA1: 5a8818f493285d4a5a3c6b1a1a3c2837b17b0513
SHA256: 9c25e2b35ff62b76fb15311cb9709184668e8a944f3b2150fa44bf50ff7ea0df
SHA512: a9076673a625b8ceaff5b6510a18fb8217605180aa91eb0827839391af3b16de43bae284d7b85938f339289e89aebdc2b8c73734858c11e9ed5f7e6216b55fed
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