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Quantitative visual exploration of scRNA-seq data

Bioconductor version: Release (3.19)

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")):


To install this package, start R (version "4.4") and enter:

if (!require("BiocManager", quietly = TRUE))


For older versions of R, please refer to the appropriate Bioconductor release.


To view documentation for the version of this package installed in your system, start R and enter:

User Manual: scBubbletree HTML R Script
Reference Manual PDF


biocViews Clustering, RNASeq, SingleCell, Software, Transcriptomics, Visualization
Version 1.6.0
In Bioconductor since BioC 3.16 (R-4.2) (1.5 years)
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
System Requirements Python (>= 3.6), leidenalg (>= 0.8.2)
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Suggests BiocStyle, knitr, testthat, cluster, SingleCellExperiment
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Follow Installation instructions to use this package in your R session.

Source Package scBubbletree_1.6.0.tar.gz
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macOS Binary (x86_64) scBubbletree_1.6.0.tgz
macOS Binary (arm64) scBubbletree_1.6.0.tgz
Source Repository git clone
Source Repository (Developer Access) git clone
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