How to Install and Uninstall r-bioc-scran Package on Ubuntu 21.10 (Impish Indri)
Last updated: November 23,2024
1. Install "r-bioc-scran" package
Please follow the step by step instructions below to install r-bioc-scran on Ubuntu 21.10 (Impish Indri)
$
sudo apt update
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$
sudo apt install
r-bioc-scran
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2. Uninstall "r-bioc-scran" package
Please follow the steps below to uninstall r-bioc-scran on Ubuntu 21.10 (Impish Indri):
$
sudo apt remove
r-bioc-scran
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$
sudo apt autoclean && sudo apt autoremove
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3. Information about the r-bioc-scran package on Ubuntu 21.10 (Impish Indri)
Package: r-bioc-scran
Architecture: amd64
Version: 1.18.5+dfsg-1
Priority: optional
Section: universe/gnu-r
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian R Packages Maintainers
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 1598
Depends: r-base-core (>= 4.0.4-1build1), r-api-4.0, r-api-bioc-3.12, r-bioc-singlecellexperiment, r-bioc-summarizedexperiment, r-bioc-s4vectors, r-bioc-biocgenerics, r-bioc-biocparallel, r-cran-rcpp, r-cran-matrix, r-bioc-scuttle, r-bioc-edger, r-bioc-limma, r-bioc-biocneighbors, r-cran-igraph, r-cran-statmod, r-bioc-delayedarray, r-bioc-delayedmatrixstats, r-bioc-biocsingular, r-bioc-bluster, r-cran-dqrng, r-bioc-beachmat, r-cran-bh, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 5.2)
Recommends: r-cran-testthat, r-bioc-deseq2, r-bioc-monocle, r-cran-dynamictreecut
Suggests: r-bioc-biocstyle, r-cran-knitr, r-bioc-hdf5array, r-bioc-scrnaseq, r-bioc-biobase, r-cran-pheatmap, r-bioc-scater
Filename: pool/universe/r/r-bioc-scran/r-bioc-scran_1.18.5+dfsg-1_amd64.deb
Size: 1141964
MD5sum: 892621af7233f6462652848d29799535
SHA1: 9f25fe585212a4aa40c868d48cb445ffd0d4b8ef
SHA256: 4b328602e6e4e89c6ab89cfbf50dffd651d25a9c05b5e615af5a3e4cfbf24bfb
SHA512: 83ad56c17a2de099c079dfa1921a25b6b2667955eb31e1a038053a4d59bea6ab12a4013ffae0d168cef89ef81da9d6fedb0743871c9fec7e57f3ac0e9775c26b
Homepage: https://bioconductor.org/packages/scran/
Description-en: BioConductor methods for single-cell RNA-Seq data analysis
Implements functions for low-level analyses of single-cell RNA-seq data.
Methods are provided for normalization of cell-specific biases,
assignment of cell cycle phase, detection of highly variable and
significantly correlated genes, identification of marker genes, and
other common tasks in routine single-cell analysis workflows.
Description-md5: 8873d8c5d48b21b7a093c1d50f7a80b3
Architecture: amd64
Version: 1.18.5+dfsg-1
Priority: optional
Section: universe/gnu-r
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian R Packages Maintainers
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 1598
Depends: r-base-core (>= 4.0.4-1build1), r-api-4.0, r-api-bioc-3.12, r-bioc-singlecellexperiment, r-bioc-summarizedexperiment, r-bioc-s4vectors, r-bioc-biocgenerics, r-bioc-biocparallel, r-cran-rcpp, r-cran-matrix, r-bioc-scuttle, r-bioc-edger, r-bioc-limma, r-bioc-biocneighbors, r-cran-igraph, r-cran-statmod, r-bioc-delayedarray, r-bioc-delayedmatrixstats, r-bioc-biocsingular, r-bioc-bluster, r-cran-dqrng, r-bioc-beachmat, r-cran-bh, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 5.2)
Recommends: r-cran-testthat, r-bioc-deseq2, r-bioc-monocle, r-cran-dynamictreecut
Suggests: r-bioc-biocstyle, r-cran-knitr, r-bioc-hdf5array, r-bioc-scrnaseq, r-bioc-biobase, r-cran-pheatmap, r-bioc-scater
Filename: pool/universe/r/r-bioc-scran/r-bioc-scran_1.18.5+dfsg-1_amd64.deb
Size: 1141964
MD5sum: 892621af7233f6462652848d29799535
SHA1: 9f25fe585212a4aa40c868d48cb445ffd0d4b8ef
SHA256: 4b328602e6e4e89c6ab89cfbf50dffd651d25a9c05b5e615af5a3e4cfbf24bfb
SHA512: 83ad56c17a2de099c079dfa1921a25b6b2667955eb31e1a038053a4d59bea6ab12a4013ffae0d168cef89ef81da9d6fedb0743871c9fec7e57f3ac0e9775c26b
Homepage: https://bioconductor.org/packages/scran/
Description-en: BioConductor methods for single-cell RNA-Seq data analysis
Implements functions for low-level analyses of single-cell RNA-seq data.
Methods are provided for normalization of cell-specific biases,
assignment of cell cycle phase, detection of highly variable and
significantly correlated genes, identification of marker genes, and
other common tasks in routine single-cell analysis workflows.
Description-md5: 8873d8c5d48b21b7a093c1d50f7a80b3