Mosmann_rare_SE: 'Mosmann_rare' dataset

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

'Mosmann_rare' dataset

Description

Flow cytometry dataset from Mosmann et al. (2014), containing 14 dimensions (7 surface protein markers and 7 signaling markers). Manually gated cell population labels are available for one rare population of activated (cytokine-producing) memory CD4 T cells. Cells are human peripheral blood cells exposed to influenza antigens, from a single healthy donor. This dataset can be used to benchmark clustering algorithms for rare cell populations.

Usage

Mosmann_rare_SE(metadata = FALSE)
Mosmann_rare_flowSet(metadata = FALSE)

Arguments

metadata

logical value indicating whether ExperimentHub metadata (describing the overall dataset) should be returned only, or if the whole dataset should be loaded. Default = FALSE, which loads the whole dataset.

Details

This is a 14-dimensional flow cytometry dataset, consisting of expression levels of 7 surface protein markers and 7 signaling markers. Cell population labels are available for one rare population of activated (cytokine-producing) memory CD4 T cells. Cells are human peripheral blood cells exposed to influenza antigens, from a single healthy donor.

This dataset can be used to benchmark clustering algorithms for rare cell populations.

The dataset contains cells from a single patient; a total of 396,460 cells (including 109 manually gated cells from the rare population of interest); and a total of 14 protein markers (7 surface protein markers and 7 signaling markers).

The dataset is provided in two Bioconductor object formats: SummarizedExperiment and flowSet. In each case, cells are stored in rows, and protein markers in columns (this is the usual format used for flow and mass cytometry data).

For the link{SummarizedExperiment}, row and column metadata can be accessed with the rowData and colData accessor functions from the SummarizedExperiment package. The row data contains the manually gated cell population IDs. The column data contains channel names, protein marker names, and a factor marker_class to identify the class of each protein marker ('cell type', 'cell state', as well as 'none' for any non protein marker columns that are not needed for downstream analyses). The expression values for each cell can be accessed with assay. The expression values are formatted as a single table.

For the flowSet, the expression values are stored in a separate table for each sample. Each sample is represented by one flowFrame object within the overall flowSet (note that for this dataset, there is only one sample). The expression values can be accessed with the exprs function from the flowCore package. Row metadata is stored as additional columns of data within the flowFrame for each sample; note that factor values are converted to numeric values, since the expression tables must be numeric matrices. Channel names are stored in the column names of the expression tables. Additional row and column metadata is stored in the description slots, which can be accessed with the description accessor function for the individual flowFrames; this includes additional sample information (where available), marker information, and cell population information.

Prior to performing any downstream analyses, the expression values should be transformed. A standard transformation used for flow cytometry data is the asinh with cofactor = 150.

File sizes: 23.1 MB (SummarizedExperiment), 23.0 MB (flowSet).

Original source: Figure 4 in Mosmann et al. (2014): https://www.ncbi.nlm.nih.gov/pubmed/24532172

Original link to raw data: http://flowrepository.org/id/FR-FCM-ZZ8J (filename: "JMW034-J16OFVQX_G2 0o1 3_D07.fcs"; see Supplementary Information file 3 for full list of filenames)

This dataset was previously used to benchmark clustering algorithms for high-dimensional cytometry in our article, Weber and Robinson (2016): https://www.ncbi.nlm.nih.gov/pubmed/27992111

Data files are also available from FlowRepository (FR-FCM-ZZPH): http://flowrepository.org/id/FR-FCM-ZZPH

Value

Returns a SummarizedExperiment or flowSet object.

References

Mosmann et al. (2014), "SWIFT - Scalable clustering for automated identification of rare cell populations in large, high-dimensional flow cytometry datasets, Part 2: Biological evaluation", Cytometry Part A, 85A, 422-433: https://www.ncbi.nlm.nih.gov/pubmed/24532172

Weber and Robinson (2016), "Comparison of clustering methods for high-dimensional single-cell flow and mass cytometry data", Cytometry Part A, 89A, 1084-1096: https://www.ncbi.nlm.nih.gov/pubmed/27992111

Examples

Mosmann_rare_SE()
Mosmann_rare_flowSet()

lmweber/HDCytoData documentation built on March 19, 2024, 4:41 a.m.