Package: scBubbletree
Type: Package
Title: Quantitative visual exploration of scRNA-seq data
Version: 1.0.0
Authors@R: 
    person(given = "Simo",  
           family = "Kitanovski",
           role = c("aut", "cre"),
           email = "simokitanovski@gmail.com")
Description: scBubbletree is a quantitative method for visual 
  exploration of scRNA-seq data. It preserves biologically 
  meaningful properties of scRNA-seq data, such as local and 
  global cell distances, as well as the density distribution of 
  cells across the sample. scBubbletree is scalable and avoids 
  the overplotting problem, and is able to visualize diverse cell 
  attributes derived from multiomic single-cell experiments. 
  Importantly, Importantly, scBubbletree is easy to use and to 
  integrate with popular approaches for scRNA-seq data analysis.
License: GPL-3 + file LICENSE
Depends: R (>= 4.2.0)
Imports: reshape2, future, future.apply, ape, scales, Seurat, ggplot2,
        ggtree, patchwork, methods, stats, base, utils
Suggests: BiocStyle, knitr, testthat, cluster, SingleCellExperiment
Encoding: UTF-8
NeedsCompilation: no
biocViews: Visualization,Clustering, SingleCell,Transcriptomics,RNASeq
BugReports: https://github.com/snaketron/scBubbletree/issues
URL: https://github.com/snaketron/scBubbletree
SystemRequirements: Python (>= 3.6), leidenalg (>= 0.8.2)
RoxygenNote: 6.1.1
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/scBubbletree
git_branch: RELEASE_3_16
git_last_commit: eef9db9
git_last_commit_date: 2022-11-01
Date/Publication: 2022-11-01
Packaged: 2022-11-02 03:02:23 UTC; biocbuild
Author: Simo Kitanovski [aut, cre]
Maintainer: Simo Kitanovski <simokitanovski@gmail.com>
Built: R 4.2.1; ; 2022-11-02 14:18:54 UTC; windows
