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diffcyt

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

Bioconductor version: 3.23 · Package version: 1.32.1

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.

Installation

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("diffcyt")

Details

MaintainerLukas M. Weber <weberlm3@gmail.com>
AuthorLukas M. Weber [aut, cre] (ORCID: <https://orcid.org/0000-0002-3282-1730>)
LicenseMIT + file LICENSE
URLhttps://github.com/lmweber/diffcyt
Bug Reportshttps://github.com/lmweber/diffcyt/issues
Downloads rank670
Source branchRELEASE_3_23
biocViewsCellBasedAssays, CellBiology, Clustering, FeatureExtraction, FlowCytometry, ImmunoOncology, Proteomics, SingleCell, Software

Documentation

Download

Dependencies

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

Reverse dependencies

Depends On Me (2): censcyt, cytofWorkflow

Imports Me (3): CyTOFpower, treeclimbR, treekoR

Suggests Me (2): CATALYST, tidytof