cytoHDBMW: Example of processed dimensionally reduced flow cytometry...
In Ghoshlab/cytoKernel: Differential expression using kernel-based score test

Description Usage Format Details References Examples

The raw data (fcs files) were pre-processed using CATALYST, scuttle, scran Bioconductor packages and igraph CRAN package. The data processing package includes 4 steps and they are as follows: 1. Creating a SingleCellExperiment Object: the flowSet data object along with the metadata are converted into a SingleCellExperiment object using the CATALYST R/Bioconductor package. 2. Clustering: We apply Louvain algorithm using the R package igraph to cluster the expression values by the type markers (surface markers). 3. Median: Medians are calculated within a cluster for every signaling marker and subject. 4. Aggregating and converting the data: We convert the aggregated data into a SummarizedExperiment. The row meta-data indicates "cluster" corresponding to the cluster id for each protein marker. The colData represents the "sample_id", "condition", "patient_id", "ids" The remaining columns indicate median expression intensities for each of the 126 (14 markers * 9 clusters) cluster combination for each sample.

1	data(cytoHDBMW)

SummarizedExperiment assay object containing 126 cluster-marker median expression intenities (features) of 8 subjects (samples).

The HDCytoData package is an extensible resource containing a set of publicly available high-dimensional flow cytometry and mass cytometry (CyTOF) benchmark datasets hosted on Bioconductor’s ExperimentHub platform.

Weber, M L, Soneson, Charlotte (2019). “HDCytoData: Collection of high-dimensional cytometry benchmark datasets in Bioconductor object formats.” F1000Research, 8(v2), 1459.