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This is the development version of diffcyt; for the stable release version, see diffcyt.

Differential discovery in high-dimensional cytometry via high-resolution clustering

Bioconductor version: Development (3.20)

Statistical methods for differential discovery analyses in high-dimensional cytometry data (including flow cytometry, mass cytometry or CyTOF, and oligonucleotide-tagged cytometry), based on a combination of high-resolution clustering and empirical Bayes moderated tests adapted from transcriptomics.

Author: Lukas M. Weber [aut, cre]

Maintainer: Lukas M. Weber <lmweberedu at>

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


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

# The following initializes usage of Bioc devel


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

diffcyt workflow HTML R Script
Reference Manual PDF


biocViews CellBasedAssays, CellBiology, Clustering, FeatureExtraction, FlowCytometry, ImmunoOncology, Proteomics, SingleCell, Software
Version 1.25.0
In Bioconductor since BioC 3.7 (R-3.5) (6 years)
License MIT + file LICENSE
Depends R (>= 3.4.0)
Imports flowCore, FlowSOM, SummarizedExperiment, S4Vectors, limma, edgeR, lme4, multcomp, dplyr, tidyr, reshape2, magrittr, stats, methods, utils, grDevices, graphics, ComplexHeatmap, circlize, grid
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Suggests BiocStyle, knitr, rmarkdown, testthat, HDCytoData, CATALYST
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Depends On Me censcyt, cytofWorkflow
Imports Me CyTOFpower, treeclimbR, treekoR
Suggests Me CATALYST
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Source Package diffcyt_1.25.0.tar.gz
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