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      /\__ \     / /\ \ \\ \ \_/ / /     / / /\ \__
     / /_ \ \   / / /\ \ \\ \___/ /     / / /\ \___\
    / / /\ \ \ / / /  \ \_\\ \ \_/      \ \ \ \/___/
   / / /  \/_// / /   / / / \ \ \        \ \ \
  / / /      / / /   / / /   \ \ \   _    \ \ \
 / / /      / / /___/ / /     \ \ \ /_/\__/ / /
/_/ /      / / /____\/ /       \ \_\\ \/___/ /
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r-basics 2.20.0
Propagated dependencies: r-assertthat@0.2.1 r-biobase@2.68.0 r-biocgenerics@0.54.0 r-biocparallel@1.42.0 r-coda@0.19-4.1 r-cowplot@1.1.3 r-ggextra@0.10.1 r-ggplot2@3.5.2 r-hexbin@1.28.5 r-mass@7.3-65 r-matrix@1.7-3 r-matrixstats@1.5.0 r-posterior@1.6.1 r-rcpp@1.0.14 r-rcpparmadillo@14.4.2-1 r-reshape2@1.4.4 r-s4vectors@0.46.0 r-scran@1.36.0 r-scuttle@1.18.0 r-singlecellexperiment@1.30.1 r-summarizedexperiment@1.38.1 r-viridis@0.6.5
Channel: guix
Location: gnu/packages/bioconductor.scm (gnu packages bioconductor)
Home page: https://github.com/catavallejos/BASiCS
Licenses: GPL 3
Synopsis: Bayesian analysis of single-cell sequencing data
Description:

BASiCS is an integrated Bayesian hierarchical model to perform statistical analyses of single-cell RNA sequencing datasets in the context of supervised experiments (where the groups of cells of interest are known a priori. BASiCS performs built-in data normalisation (global scaling) and technical noise quantification (based on spike-in genes). BASiCS provides an intuitive detection criterion for highly (or lowly) variable genes within a single group of cells. Additionally, BASiCS can compare gene expression patterns between two or more pre-specified groups of cells.

Total results: 4