Krieg_Anti_PD_1_SE: 'Krieg_Anti_PD_1' dataset

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

'Krieg_Anti_PD_1' dataset

Description

Mass cytometry (CyTOF) dataset from Krieg et al. (2018), consisting of 20 baseline samples (prior to treatment) of peripheral blood from melanoma skin cancer patients subsequently treated with anti-PD-1 immunotherapy. The samples are split across 2 conditions (non-responders and responders) and 2 batches. This dataset can be used to benchmark differential analysis algorithms used to test for differentially abundant rare cell populations.

Usage

Krieg_Anti_PD_1_SE(metadata = FALSE)
Krieg_Anti_PD_1_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 mass cytometry (CyTOF) dataset from Krieg et al. (2018), who used mass cytometry to characterize immune cell subsets in peripheral blood from melanoma skin cancer patients treated with anti-PD-1 immunotherapy. This study found that the frequency of CD14+CD16-HLA-DRhi monocytes in baseline samples (taken from patients prior to treatment) was a strong predictor of survival in response to immunotherapy treatment. In particular, the frequency of a small subpopulation of CD14+CD33+HLA-DRhiICAM-1+CD64+CD141+CD86+CD11c+CD38+PD-L1+CD11b+ monocytes in baseline samples was strongly associated with responder status following immunotherapy treatment. Note that this dataset contains a strong batch effect, due to sample acquisition on two different days (Krieg et al., 2018).

This dataset can be used to benchmark differential analysis algorithms used to test for differentially abundant rare cell populations (i.e. the small subpopulation of CD14+CD33+HLA-DRhiICAM-1+CD64+CD141+CD86+CD11c+CD38+PD-L1+CD11b+ monocytes).

The dataset contains 20 baseline samples (i.e. samples taken prior to treatment), from patients subsequently classified into 2 groups (9 non-responders and 11 responders). Samples are also split across 2 batches ('batch23' and 'batch29'), due to sample acquisition on two different days. The total number of cells is 85,715.

There are 24 'cell type' markers used to characterize cell subpopulations. (One additional cell type marker – CD45 – is also available, but should be excluded from most analyses since almost all cells show very high expression of CD45; so it does not help distinguish subpopulations, and may dominate other signals. Therefore, CD45 has been classified as 'none' in the marker_info table.)

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 group IDs, batch IDs, and sample 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; for this dataset, all proteins are cell type markers). 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. 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 filenames, additional sample information, and additional marker information.

Prior to performing any downstream analyses, the expression values should be transformed. A standard transformation used for mass cytometry data is the asinh with cofactor = 5.

File sizes: 12.3 MB (SummarizedExperiment), 12.6 MB (flowSet).

Original source: Krieg et al. (2018): https://www.ncbi.nlm.nih.gov/pubmed/29309059

Original link to raw data (FlowRepository, FR-FCM-ZY34): http://flowrepository.org/id/FR-FCM-ZY34

This dataset was previously used to benchmark algorithms for differential analysis in our article, Weber et al. (2019): https://www.ncbi.nlm.nih.gov/pubmed/31098416. (For additional details on the dataset, see Supplementary Note 1: Benchmark datasets.)

Data files are also available from FlowRepository (FR-FCM-ZYL8): http://flowrepository.org/id/FR-FCM-ZYL8

Value

Returns a SummarizedExperiment or flowSet object.

References

Krieg et al. (2018), "High-dimensional single-cell analysis predicts response to anti-PD-1 immunotherapy." Nature Medicine, 24, 144-153: https://www.ncbi.nlm.nih.gov/pubmed/29309059

Weber et al. (2019). "diffcyt: Differential discovery in high-dimensional cytometry via high-resolution clustering." Communications Biology, 2:183: https://www.ncbi.nlm.nih.gov/pubmed/31098416

Examples

Krieg_Anti_PD_1_SE()
Krieg_Anti_PD_1_flowSet()

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