How to Install and Uninstall r-bioc-sva Package on Kali Linux

Last updated: May 14,2024

1. Install "r-bioc-sva" package

Please follow the guidance below to install r-bioc-sva on Kali Linux


    
        
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                sudo apt update
            
                    
    
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            $
        

        
            
                sudo apt install
            
                        
                r-bioc-sva
            
                    
    
    Copied

2. Uninstall "r-bioc-sva" package

This guide let you learn how to uninstall r-bioc-sva on Kali Linux:


    
        
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                sudo apt remove
            
                        
                r-bioc-sva
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo apt autoclean && sudo apt autoremove
            
                    
    
    Copied

3. Information about the r-bioc-sva package on Kali Linux

Package: r-bioc-sva
Version: 3.50.0-1
Installed-Size: 968
Maintainer: Debian R Packages Maintainers
Architecture: amd64
Depends: r-api-4.0, r-api-bioc-3.18, r-cran-mgcv, r-bioc-genefilter, r-bioc-biocparallel, r-cran-matrixstats, r-bioc-limma, r-bioc-edger
Suggests: r-bioc-bladderbatch, r-bioc-biocstyle, r-cran-testthat
Size: 453944
SHA256: d06918cc82ec5e668aebd9bcbb1191597cc09937cdab317b989377501668d61d
SHA1: 5aaf297450411c005d2beda30b061e66770aef71
MD5sum: b8afeddc39f91355db72efe6ffa33706
Description: 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:
Homepage: https://bioconductor.org/packages/sva/
Section: gnu-r
Priority: optional
Filename: pool/main/r/r-bioc-sva/r-bioc-sva_3.50.0-1_amd64.deb