Weber_BCR_XL_sim_main_SE: 'Weber_BCR_XL_sim' semi-simulated datasets

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

'Weber_BCR_XL_sim' semi-simulated datasets

Description

Semi-simulated mass cytometry (CyTOF) datasets from Weber et al. (2019), constructed by randomly splitting unstimulated (reference) samples of PBMCs (peripheral blood mononuclear cells) into two halves, and replacing B cells in one half with stimulated (BCR-XL) B cells from corresponding paired samples. These datasets can be used to benchmark differential analysis algorithms used to test for differential states within cell populations. Raw data sourced from Bodenmiller et al. (2012); cell population labels reproduced from Nowicka et al. (2017). See Weber et al. (2019) Supplementary Note 1, for more details.

Usage

Weber_BCR_XL_sim_main_SE(metadata = FALSE)
Weber_BCR_XL_sim_main_flowSet(metadata = FALSE)
Weber_BCR_XL_sim_null_rep1_SE(metadata = FALSE)
Weber_BCR_XL_sim_null_rep1_flowSet(metadata = FALSE)
Weber_BCR_XL_sim_null_rep2_SE(metadata = FALSE)
Weber_BCR_XL_sim_null_rep2_flowSet(metadata = FALSE)
Weber_BCR_XL_sim_null_rep3_SE(metadata = FALSE)
Weber_BCR_XL_sim_null_rep3_flowSet(metadata = FALSE)
Weber_BCR_XL_sim_random_seeds_rep1_SE(metadata = FALSE)
Weber_BCR_XL_sim_random_seeds_rep1_flowSet(metadata = FALSE)
Weber_BCR_XL_sim_random_seeds_rep2_SE(metadata = FALSE)
Weber_BCR_XL_sim_random_seeds_rep2_flowSet(metadata = FALSE)
Weber_BCR_XL_sim_random_seeds_rep3_SE(metadata = FALSE)
Weber_BCR_XL_sim_random_seeds_rep3_flowSet(metadata = FALSE)
Weber_BCR_XL_sim_less_distinct_less_50pc_SE(metadata = FALSE)
Weber_BCR_XL_sim_less_distinct_less_50pc_flowSet(metadata = FALSE)
Weber_BCR_XL_sim_less_distinct_less_75pc_SE(metadata = FALSE)
Weber_BCR_XL_sim_less_distinct_less_75pc_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 set of semi-simulated mass cytometry (CyTOF) datasets, generated for benchmarking purposes in our paper introducing the 'diffcyt' framework (Weber et al., 2019).

The datasets are constructed by randomly splitting unstimulated (reference) samples of PBMCs (peripheral blood mononuclear cells) into two halves, and replacing B cells in one half with stimulated (BCR-XL) B cells from corresponding paired samples. Strong differential expression signals exist for several signaling state markers in B cells between the stimulated (BCR-XL) and unstimulated (reference) conditions; in particular phosphorylated S6 (pS6).

These datasets can be used to benchmark differential analysis algorithms used to test for differential states within cell populations.

The raw data consists of 8 paired samples (i.e. 16 samples in total), and a total of 172,791 cells. The dataset contains expression levels of 24 protein markers (10 surface markers used to define cell populations, and 14 intracellular signaling markers). Cell population labels are reproduced from Nowicka et al. (2017). For more details, see Weber et al. (2019), Supplementary Note 1 (in particular Supplementary Tables 3 and 4).

Multiple simulations are available, as described in our paper (Weber et al., 2019). These are stored in the objects listed below.

In each case, the objects are available in both SummarizedExperiment and flowSet formats, with cells stored in rows, and protein markers in columns (i.e. the usual format for cytometry data). After loading the datasets, they can be inspected using the standard accessor functions for either SummarizedExperiments or flowSets (e.g. for SummarizedExperiments: rowData, colData, assays, and metadata).

For the SummarizedExperiments: assays contain tables of expression values (with multiple objects for datasets with multiple replicates; note that the replicates cannot be combined as multiple assays within a single object because each replicate has different row data); rowData contains group IDs, patient IDs, sample IDs, cell population IDs, and columns identifying B cells and spike-in cells; colData contains channel names, marker names, and marker classes; and metadata contains experiment information and number of cells.

For the flowSets: individual flowFrames within the flowSet contain tables of expression values (with multiple flowSet objects for datasets with multiple replicates); row data is stored as additional columns of numeric values within the expression tables; column data is stored in the pData(parameters()) slot of the individual flowFrames; and additional information (e.g. experiment information, marker information, replicate information, and lookup tables to identify row data values) is stored in the description() slot of the flowFrames.

Main simulations

• Weber_BCR_XL_sim_main_SE (12.7 MB)

• Weber_BCR_XL_sim_main_flowSet (12.7 MB)

Additional simulations: null simulations

Separate files for each replicate.

• Weber_BCR_XL_sim_null_rep1_SE (12.7 MB)

• Weber_BCR_XL_sim_null_rep1_flowSet (12.7 MB)

• Weber_BCR_XL_sim_null_rep2_SE (12.7 MB)

• Weber_BCR_XL_sim_null_rep2_flowSet (12.7 MB)

• Weber_BCR_XL_sim_null_rep3_SE (12.7 MB)

• Weber_BCR_XL_sim_null_rep3_flowSet (12.7 MB)

Additional simulations: modified random seeds

Separate files for each replicate.

• Weber_BCR_XL_sim_random_seeds_rep1_SE (12.7 MB)

• Weber_BCR_XL_sim_random_seeds_rep1_flowSet (12.7 MB)

• Weber_BCR_XL_sim_random_seeds_rep2_SE (12.7 MB)

• Weber_BCR_XL_sim_random_seeds_rep2_flowSet (12.7 MB)

• Weber_BCR_XL_sim_random_seeds_rep3_SE (12.7 MB)

• Weber_BCR_XL_sim_random_seeds_rep3_flowSet (12.7 MB)

Additional simulations: 'less distinct' spike-in cells

Separate files for each replicate.

• Weber_BCR_XL_sim_less_distinct_less_50pc_SE (13.1 MB)

• Weber_BCR_XL_sim_less_distinct_less_50pc_flowSet (13.1 MB)

• Weber_BCR_XL_sim_less_distinct_less_75pc_SE (13.1 MB)

• Weber_BCR_XL_sim_less_distinct_less_75pc_flowSet (13.1 MB)

Note that 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.

The raw data is sourced from Bodenmiller et al. (2012), and cell population labels are reproduced from Nowicka et al. (2017). See Weber et al. (2019), Supplementary Note 1, for more details.

Original link to raw data (Cytobank, experiment 15713): https://community.cytobank.org/cytobank/experiments/15713/download_files

Additional information (Citrus wiki page): https://github.com/nolanlab/citrus/wiki/PBMC-Example-1

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

Bodenmiller et al. (2012). "Multiplexed mass cytometry profiling of cellular states perturbed by small-molecule regulators." Nature Biotechnology, 30(9), 858-867: https://www.ncbi.nlm.nih.gov/pubmed/22902532

Nowicka et al. (2017). "CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets." F1000Research, v2: https://f1000research.com/articles/6-748/v2

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

Weber_BCR_XL_sim_main_SE()
Weber_BCR_XL_sim_main_flowSet()

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