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Using Mass Cytometry for Differential Abundance Analyses

Bioconductor version: Release (3.18)

Identifies differentially abundant populations between samples and groups in mass cytometry data. Provides methods for counting cells into hyperspheres, controlling the spatial false discovery rate, and visualizing changes in abundance in the high-dimensional marker space.

Author: Aaron Lun [aut, cre]

Maintainer: Aaron Lun <infinite.monkeys.with.keyboards at gmail.com>

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


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Detecting differential abundance HTML R Script
Reference Manual PDF


biocViews FlowCytometry, ImmunoOncology, MultipleComparison, Proteomics, SingleCell, Software
Version 1.26.0
In Bioconductor since BioC 3.5 (R-3.4) (7 years)
License GPL-3
Depends SingleCellExperiment
Imports viridis, methods, shiny, graphics, stats, grDevices, utils, BiocGenerics, S4Vectors, BiocParallel, SummarizedExperiment, flowCore, Biobase, Rcpp, BiocNeighbors
System Requirements C++11
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Suggests ncdfFlow, testthat, rmarkdown, knitr, edgeR, limma, glmnet, BiocStyle, flowStats
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Follow Installation instructions to use this package in your R session.

Source Package cydar_1.26.0.tar.gz
Windows Binary cydar_1.26.0.zip
macOS Binary (x86_64) cydar_1.26.0.tgz
macOS Binary (arm64) cydar_1.26.0.tgz
Source Repository git clone https://git.bioconductor.org/packages/cydar
Source Repository (Developer Access) git clone git@git.bioconductor.org:packages/cydar
Bioc Package Browser https://code.bioconductor.org/browse/cydar/
Package Short Url https://bioconductor.org/packages/cydar/
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