HDCytoData
Collection of high-dimensional cytometry benchmark datasets in Bioconductor object formats
Bioconductor version: 3.23 · Package version: 1.32.1
Other Bioconductor versions
devel is the development version; release is the current stable one.
3.24 (devel), 3.23 (release)
Data package containing a set of publicly available high-dimensional cytometry benchmark datasets, formatted into SummarizedExperiment and flowSet Bioconductor object formats, including all required metadata. Row metadata includes sample IDs, group IDs, patient IDs, reference cell population or cluster labels (where available), and labels identifying 'spiked in' cells (where available). Column metadata includes channel names, protein marker names, and protein marker classes (cell type or cell state).
Installation
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("HDCytoData")Details
| Maintainer | Lukas M. Weber <weberlm3@gmail.com> |
| Author | Lukas M. Weber [aut, cre], Charlotte Soneson [aut] |
| License | MIT + file LICENSE |
| URL | https://github.com/lmweber/HDCytoData |
| Bug Reports | https://github.com/lmweber/HDCytoData/issues |
| Source branch | RELEASE_3_23 |
| Build report | Bioconductor build system, r-universe |
| biocViews | ExperimentData, ExperimentHub, ExpressionData, FlowCytometryData, Homo_sapiens_Data, ImmunoOncologyData, SingleCellData |
| Package Short Url | https://bioconductor.org/packages/HDCytoData/ |
Citation
From within R, enter citation("HDCytoData"):
Lukas M. Weber, Charlotte Soneson. HDCytoData: Collection of high-dimensional cytometry benchmark datasets in Bioconductor object formats. doi:10.18129/B9.bioc.HDCytoData, R package version 1.32.1, https://bioconductor.org/packages/HDCytoData.
Generated from the package metadata; it may differ from the package's own citation.
Download
Follow the installation instructions to use this package in your R session.
| Source package | HDCytoData_1.32.1.tar.gz |
Dependencies
Depends: ExperimentHub, SummarizedExperiment, flowCore
Imports: utils, methods
Suggests: BiocStyle, knitr, rmarkdown, Rtsne, umap, ggplot2, FlowSOM, mclust
Reverse dependencies
Depends On Me (1): cytofWorkflow
Suggests Me (5): CytoMDS, diffcyt, MDSvis, tidyFlowCore, tidytof