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

Last updated: May 11,2024

1. Install "r-bioc-gsva" package

This guide let you learn how to install r-bioc-gsva on Kali Linux


    
        
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                sudo apt update
            
                    
    
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            $
        

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

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


    
        
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                sudo apt remove
            
                        
                r-bioc-gsva
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo apt autoclean && sudo apt autoremove
            
                    
    
    Copied

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

Package: r-bioc-gsva
Version: 1.50.0+ds-1
Installed-Size: 1296
Maintainer: Debian R Packages Maintainers
Architecture: amd64
Depends: r-api-4.0, r-api-bioc-3.18, r-bioc-s4vectors, r-bioc-iranges, r-bioc-biobase, r-bioc-summarizedexperiment, r-bioc-gseabase, r-cran-matrix (>= 1.5-0), r-bioc-biocparallel, r-bioc-singlecellexperiment, r-bioc-sparsematrixstats, r-bioc-delayedarray, r-bioc-delayedmatrixstats, r-bioc-hdf5array, r-bioc-biocsingular, libc6 (>= 2.4)
Recommends: r-cran-runit, r-cran-fastmatch
Suggests: r-bioc-biocgenerics, r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-bioc-limma, r-cran-rcolorbrewer, r-bioc-org.hs.eg.db, r-bioc-genefilter, r-bioc-edger, r-cran-shiny, r-cran-shinydashboard, r-cran-ggplot2, r-cran-data.table, r-cran-plotly, r-cran-future, r-cran-promises, r-cran-shinyjs
Size: 1096704
SHA256: 94bf5a2ab9b9adc95bd95b46db0be25a54542bf00263f9df22510296098ed6d5
SHA1: e6f813bffe17c4168de41fe06ad74324447bc125
MD5sum: 04296918cb487ef4501bb8145cbe2ec1
Description: Gene Set Variation Analysis for microarray and RNA-seq data
Gene Set Variation Analysis (GSVA) is a non-parametric, unsupervised
method for estimating variation of gene set enrichment through the
samples of a expression data set. GSVA performs a change in coordinate
systems, transforming the data from a gene by sample matrix to a gene-
set by sample matrix, thereby allowing the evaluation of pathway
enrichment for each sample. This new matrix of GSVA enrichment scores
facilitates applying standard analytical methods like functional
enrichment, survival analysis, clustering, CNV-pathway analysis or cross-
tissue pathway analysis, in a pathway-centric manner.
Description-md5:
Homepage: https://bioconductor.org/packages/GSVA/
Section: gnu-r
Priority: optional
Filename: pool/main/r/r-bioc-gsva/r-bioc-gsva_1.50.0+ds-1_amd64.deb