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
| Maintainer | Lukas M. Weber <weberlm3@gmail.com> |
| Author | Lukas M. Weber [aut, cre] (ORCID: <https://orcid.org/0000-0002-3282-1730>) |
| License | MIT + file LICENSE |
| URL | https://github.com/lmweber/diffcyt |
| Bug Reports | https://github.com/lmweber/diffcyt/issues |
| Downloads rank | 670 |
| Source branch | RELEASE_3_23 |
| biocViews | CellBasedAssays, 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