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


Weber, Lukas M; Soneson, Charlotte (2019). HDCytoData: Collection of high-dimensional cytometry benchmark datasets in Bioconductor object formats. F1000Research, 8:1459.

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.

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.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Molecular Life Sciences
Dewey Decimal Classification:570 Life sciences; biology
Scopus Subject Areas:Life Sciences > General Biochemistry, Genetics and Molecular Biology
Life Sciences > General Immunology and Microbiology
Life Sciences > General Pharmacology, Toxicology and Pharmaceutics
Language:English
Date:2019
Deposited On:31 Jan 2020 15:49
Last Modified:11 May 2020 19:31
Publisher:Faculty of 1000 Ltd.
ISSN:2046-1402
OA Status:Gold
Free access at:PubMed ID. An embargo period may apply.
Publisher DOI:https://doi.org/10.12688/f1000research.20210.2
PubMed ID:31857895

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