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

Last updated: October 06,2024

1. Install "r-bioc-hilbertvis" package

This guide let you learn how to install r-bioc-hilbertvis on Ubuntu 20.10 (Groovy Gorilla)


    
        
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                sudo apt update
            
                    
    
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                sudo apt install
            
                        
                r-bioc-hilbertvis
            
                    
    
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2. Uninstall "r-bioc-hilbertvis" package

Please follow the step by step instructions below to uninstall r-bioc-hilbertvis on Ubuntu 20.10 (Groovy Gorilla):


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

Package: r-bioc-hilbertvis
Architecture: amd64
Version: 1.46.0-1build1
Priority: optional
Section: universe/math
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian R Packages Maintainers
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 1498
Depends: r-base-core (>= 4.0.0.20200528-1), r-api-4.0, r-api-bioc-3.11, r-cran-lattice, libc6 (>= 2.4)
Suggests: r-bioc-iranges
Filename: pool/universe/r/r-bioc-hilbertvis/r-bioc-hilbertvis_1.46.0-1build1_amd64.deb
Size: 895316
MD5sum: 1ba4c0c0ee81bc6e3bc68d6ace4c5725
SHA1: a6274bada7340b17ce5d5dc73db993304c918caf
SHA256: 4bd107c62501130e4fcc4c9525e6ed20455e1f58f727d8c0508fc313337ae344
SHA512: 07a6f5a1053b3a27e74a64c5f31b56b5538d0acd40d3b83b01cc56a03835eab94ea3c39ea9bb3bb526776bc391933e5177c3c8aa70a1ccac1a647e15a04e0d1a
Homepage: https://bioconductor.org/packages/HilbertVis
Description-en: GNU R package to visualise long vector data
This tool allows one to display very long data vectors in a space-efficient
manner, by organising it along a 2D Hilbert curve. The user can then
visually judge the large scale structure and distribution of features
simultaenously with the rough shape and intensity of individual features.
.
In bioinformatics, a typical use case is ChIP-Chip and ChIP-Seq,
or basically all the kinds of genomic data, that are conventionally
displayed as quantitative track ("wiggle data") in genome browsers such
as those provided by Ensembl or UCSC.
Description-md5: ef2bbbd3831fe633c838f37f00806810