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

Last updated: May 23,2024

1. Install "r-bioc-edger" package

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


    
        
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                sudo apt update
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo apt install
            
                        
                r-bioc-edger
            
                    
    
    Copied

2. Uninstall "r-bioc-edger" package

Please follow the instructions below to uninstall r-bioc-edger on Kali Linux:


    
        
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                sudo apt remove
            
                        
                r-bioc-edger
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo apt autoclean && sudo apt autoremove
            
                    
    
    Copied

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

Package: r-bioc-edger
Version: 4.0.15+dfsg-1
Installed-Size: 1472
Maintainer: Debian R Packages Maintainers
Architecture: amd64
Depends: r-api-4.0, r-api-bioc-3.18, r-bioc-limma (>= 3.41.5), r-cran-locfit, r-cran-rcpp, libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1)
Suggests: r-cran-jsonlite, r-cran-readr, r-bioc-rhdf5, r-cran-knitr, r-bioc-annotationdbi, r-bioc-biobase, r-bioc-biocstyle, r-bioc-summarizedexperiment, r-bioc-org.hs.eg.db, r-cran-matrix, r-cran-seuratobject
Size: 1150512
SHA256: a3b75de9b76e206f3ffb404a202f2055696c38de816cb1e70b56bcad6d645823
SHA1: 546078a8d028b9c2a528859f58325205cc171acf
MD5sum: b578d5a463c43e28bdf83efb2e0ebcce
Description: Empirical analysis of digital gene expression data in R
Bioconductor package for differential expression analysis of whole
transcriptome sequencing (RNA-seq) and digital gene expression
profiles with biological replication. It uses empirical Bayes
estimation and exact tests based on the negative binomial
distribution. It is also useful for differential signal analysis with
other types of genome-scale count data.
Description-md5:
Homepage: https://bioconductor.org/packages/edgeR/
Tag: field::biology, field::biology:bioinformatics, implemented-in::r,
interface::commandline, role::program
Section: gnu-r
Priority: optional
Filename: pool/main/r/r-bioc-edger/r-bioc-edger_4.0.15+dfsg-1_amd64.deb