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

Last updated: May 20,2024

1. Install "r-bioc-densvis" package

This tutorial shows how to install r-bioc-densvis on Kali Linux


    
        
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                sudo apt update
            
                    
    
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            $
        

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

Please follow the guidelines below to uninstall r-bioc-densvis on Kali Linux:


    
        
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                sudo apt remove
            
                        
                r-bioc-densvis
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo apt autoclean && sudo apt autoremove
            
                    
    
    Copied

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

Package: r-bioc-densvis
Version: 1.12.1+dfsg-1
Installed-Size: 202
Maintainer: Debian R Packages Maintainers
Architecture: amd64
Depends: r-api-4.0, r-api-bioc-3.18, r-cran-rcpp, r-bioc-basilisk, r-cran-assertthat, r-cran-reticulate, r-cran-rtsne, r-cran-irlba, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1)
Suggests: r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle, r-cran-ggplot2, r-cran-uwot, r-cran-testthat
Size: 83836
SHA256: c2e436d2e9d4a2246deb7a2eebec50c53d842d1400e866a90a7488809f5c0da1
SHA1: 82f04e6d7807f76c75cda2e2d5f8580d138dbcb2
MD5sum: 3f09ac362929016e186e2f36c7bcc435
Description: density-preserving data visualization via non-linear dimensionality reduction
Implements the density-preserving modification to t-SNE
and UMAP described by Narayan et al. (2020)
.
The non-linear dimensionality reduction techniques t-SNE and UMAP
enable users to summarise complex high-dimensional sequencing data
such as single cell RNAseq using lower dimensional representations.
These lower dimensional representations enable the visualisation of discrete
transcriptional states, as well as continuous trajectory (for example, in
early development). However, these methods focus on the local neighbourhood
structure of the data. In some cases, this results in
misleading visualisations, where the density of cells in the low-dimensional
embedding does not represent the transcriptional heterogeneity of data in the
original high-dimensional space. den-SNE and densMAP aim to enable more
accurate visual interpretation of high-dimensional datasets by producing
lower-dimensional embeddings that accurately represent the heterogeneity of
the original high-dimensional space, enabling the identification of
homogeneous and heterogeneous cell states.
This accuracy is accomplished by including in the optimisation process a term
which considers the local density of points in the original high-dimensional
space. This can help to create visualisations that are more representative of
heterogeneity in the original high-dimensional space.
Description-md5:
Homepage: https://bioconductor.org/packages/densvis/
Section: gnu-r
Priority: optional
Filename: pool/main/r/r-bioc-densvis/r-bioc-densvis_1.12.1+dfsg-1_amd64.deb