DOI: 10.18129/B9.bioc.diffcyt    

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 (3.17)

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 <lukas.weber.edu at gmail.com>

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


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biocViews CellBasedAssays, CellBiology, Clustering, FeatureExtraction, FlowCytometry, ImmunoOncology, Proteomics, SingleCell, Software
Version 1.19.0
In Bioconductor since BioC 3.7 (R-3.5) (4.5 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
Suggests BiocStyle, knitr, rmarkdown, testthat, HDCytoData, CATALYST
URL https://github.com/lmweber/diffcyt
BugReports https://github.com/lmweber/diffcyt/issues
Depends On Me censcyt, cytofWorkflow
Imports Me CyTOFpower, treekoR
Suggests Me CATALYST
Links To Me
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