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tidytof

Analyze High-dimensional Cytometry Data Using Tidy Data Principles

Bioconductor version: 3.23 · Package version: 1.6.0

This package implements an interactive, scientific analysis pipeline for high-dimensional cytometry data built using tidy data principles. It is specifically designed to play well with both the tidyverse and Bioconductor software ecosystems, with functionality for reading/writing data files, data cleaning, preprocessing, clustering, visualization, modeling, and other quality-of-life functions. tidytof implements a "grammar" of high-dimensional cytometry data analysis.

Installation

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

BiocManager::install("tidytof")

Details

MaintainerTimothy Keyes <tkeyes@stanford.edu>
AuthorTimothy Keyes [cre] (ORCID: <https://orcid.org/0000-0003-0423-9679>), Kara Davis [rth, own], Garry Nolan [rth, own]
LicenseMIT + file LICENSE
URLhttps://keyes-timothy.github.io/tidytof, https://keyes-timothy.github.io/tidytof/
Bug Reportshttps://github.com/keyes-timothy/tidytof/issues
StatusDeprecated
Downloads rank61
Source branchRELEASE_3_23
biocViewsFlowCytometry, SingleCell, Software

Documentation

Download

Dependencies

Depends: R (>= 4.3)

Imports: doParallel, dplyr, flowCore, foreach, ggplot2, ggraph, glmnet, methods, parallel, purrr, readr, recipes, rlang, stringr, survival, tidygraph, tidyr, tidyselect, yardstick, Rcpp, tibble, stats, utils, RcppHNSW

LinkingTo: Rcpp

Suggests: ConsensusClusterPlus, Biobase, broom, covr, diffcyt, emdist, FlowSOM, forcats, ggrepel, HDCytoData, knitr, markdown, philentropy, rmarkdown, Rtsne, statmod, SummarizedExperiment, testthat (>= 3.0.0), lmerTest, lme4, ggridges, spelling, scattermore, preprocessCore, SingleCellExperiment, Seurat, SeuratObject, embed, rsample, BiocGenerics