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scDataviz: single cell dataviz and downstream analyses

Bioconductor version: Release (3.19)

In the single cell World, which includes flow cytometry, mass cytometry, single-cell RNA-seq (scRNA-seq), and others, there is a need to improve data visualisation and to bring analysis capabilities to researchers even from non-technical backgrounds. scDataviz attempts to fit into this space, while also catering for advanced users. Additonally, due to the way that scDataviz is designed, which is based on SingleCellExperiment, it has a 'plug and play' feel, and immediately lends itself as flexibile and compatibile with studies that go beyond scDataviz. Finally, the graphics in scDataviz are generated via the ggplot engine, which means that users can 'add on' features to these with ease.

Author: Kevin Blighe [aut, cre]

Maintainer: Kevin Blighe <kevin at>

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


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:

scDataviz: single cell dataviz and downstream analyses HTML R Script
Reference Manual PDF


biocViews DataImport, FlowCytometry, GeneExpression, ImmunoOncology, MassSpectrometry, RNASeq, SingleCell, Software, Transcription
Version 1.14.0
In Bioconductor since BioC 3.12 (R-4.0) (3.5 years)
License GPL-3
Depends R (>= 4.0), S4Vectors, SingleCellExperiment
Imports ggplot2, ggrepel, flowCore, umap, Seurat, reshape2, scales, RColorBrewer, corrplot, stats, grDevices, graphics, utils, MASS, matrixStats, methods
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Suggests PCAtools, cowplot, BiocGenerics, RUnit, knitr, kableExtra, rmarkdown
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Follow Installation instructions to use this package in your R session.

Source Package scDataviz_1.14.0.tar.gz
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macOS Binary (x86_64) scDataviz_1.14.0.tgz
macOS Binary (arm64) scDataviz_1.14.0.tgz
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