DOI: 10.18129/B9.bioc.scBubbletree  

This package is for version 3.17 of Bioconductor; for the stable, up-to-date release version, see scBubbletree.

Quantitative visual exploration of scRNA-seq data

Bioconductor version: 3.17

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.

Author: Simo Kitanovski [aut, cre]

Maintainer: Simo Kitanovski <simokitanovski at>

Citation (from within R, enter citation("scBubbletree")):


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biocViews Clustering, RNASeq, SingleCell, Software, Transcriptomics, Visualization
Version 1.2.0
In Bioconductor since BioC 3.16 (R-4.2) (1 year)
License GPL-3 + file LICENSE
Depends R (>= 4.2.0)
Imports reshape2, future, future.apply, ape, scales, Seurat, ggplot2, ggtree, patchwork, proxy, methods, stats, base, utils
Suggests BiocStyle, knitr, testthat, cluster, SingleCellExperiment
SystemRequirements Python (>= 3.6), leidenalg (>= 0.8.2)
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