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
| Maintainer | Timothy Keyes <tkeyes@stanford.edu> |
| Author | Timothy Keyes [cre] (ORCID: <https://orcid.org/0000-0003-0423-9679>), Kara Davis [rth, own], Garry Nolan [rth, own] |
| License | MIT + file LICENSE |
| URL | https://keyes-timothy.github.io/tidytof, https://keyes-timothy.github.io/tidytof/ |
| Bug Reports | https://github.com/keyes-timothy/tidytof/issues |
| Status | Deprecated |
| Downloads rank | 61 |
| Source branch | RELEASE_3_23 |
| biocViews | FlowCytometry, SingleCell, Software |
Documentation
- GETTING STARTED with tidytof
- Reading and writing data
- Quality control
- Preprocessing
- Downsampling
- Dimensionality reduction
- Clustering and metaclustering
- Differential discovery analysis
- Feature extraction
- Building predictive models
- How to contribute code
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