Format

Send to

Choose Destination
F1000Res. 2019 Aug 19;8:1459. doi: 10.12688/f1000research.20210.2. eCollection 2019.

HDCytoData: Collection of high-dimensional cytometry benchmark datasets in Bioconductor object formats.

Author information

1
Institute of Molecular Life Sciences, University of Zurich, Zurich, 8057, Switzerland.
2
SIB Swiss Institute of Bioinformatics, Zurich, 8057, Switzerland.
3
Friedrich Miescher Institute for Biomedical Research, Basel, 4058, Switzerland.
4
SIB Swiss Institute of Bioinformatics, Basel, 4058, Switzerland.

Abstract

Benchmarking is a crucial step during computational analysis and method development. Recently, a number of new methods have been developed for analyzing high-dimensional cytometry data. However, it can be difficult for analysts and developers to find and access well-characterized benchmark datasets. Here, we present HDCytoData, a Bioconductor package providing streamlined access to several publicly available high-dimensional cytometry benchmark datasets. The package is designed to be extensible, allowing new datasets to be contributed by ourselves or other researchers in the future. Currently, the package includes a set of experimental and semi-simulated datasets, which have been used in our previous work to evaluate methods for clustering and differential analyses. Datasets are formatted into standard SummarizedExperiment and flowSet Bioconductor object formats, which include complete metadata within the objects. Access is provided through Bioconductor's ExperimentHub interface. The package is freely available from http://bioconductor.org/packages/HDCytoData.

KEYWORDS:

Bioconductor; ExperimentHub; benchmarking; clustering; differential analyses; high-dimensional cytometry

Supplemental Content

Full text links

Icon for F1000 Research Ltd Icon for PubMed Central
Loading ...
Support Center