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Obtain the JacksonFischer_2020_BreastCancer dataset

Source: R/JacksonFischer_2020_BreastCancer.R
JacksonFischer_2020_BreastCancer.Rd

Obtain the JacksonFischer_2020_BreastCancer dataset, which consists of three data objects: single cell data, multichannel images and cell segmentation masks. The data was obtained by imaging mass cytometry (IMC) of tumour tissue from patients with breast cancer.

Usage

JacksonFischer_2020_BreastCancer(
  data_type = c("sce", "spe", "images", "masks"),
  full_dataset = FALSE,
  cohort = "Basel",
  version = "latest",
  metadata = FALSE,
  on_disk = FALSE,
  h5FilesPath = NULL,
  force = FALSE
)

Arguments

data_type

type of object to load, `images` for multichannel images or `masks` for cell segmentation masks. Single cell data are retrieved using either `sce` for the SingleCellExperiment format or `spe` for the SpatialExperiment format.

full_dataset

if FALSE (default), a subset corresponding to 100 images is returned. If TRUE, the full dataset is returned, including both "Basel" and "Zurich" cohorts. Due to memory space limitations, this option is only available for single cell data and masks, not for data_type = "images".

cohort

which patient cohort should be returned? Can be set to "Basel" (default) or "Zurich". Ignored if full_dataset is set to TRUE.

version

dataset version. By default, the latest version is returned.

metadata

if FALSE (default), the data object selected in data_type is returned. If TRUE, only the metadata associated to this object is returned.

on_disk

logical indicating if images in form of HDF5Array objects (as .h5 files) should be stored on disk rather than in memory. This setting is valid when downloading images and masks.

h5FilesPath

path to where the .h5 files for on disk representation are stored. This path needs to be defined when on_disk = TRUE. When files should only temporarily be stored on disk, please set h5FilesPath = getHDF5DumpDir().

force

logical indicating if images should be overwritten when files with the same name already exist on disk.

Value

A SingleCellExperiment object with single cell data, a SpatialExperiment object with single cell data, a CytoImageList object containing multichannel images, or a CytoImageList object containing cell segmentation masks.

Details

This is an Imaging Mass Cytometry (IMC) dataset from Jackson, Fischer et al. (2020):

  • images contains a hundred 42-channel images in the form of a CytoImageList class object.

  • masks contains the cell segmentation masks associated with the images, in the form of a CytoImageList class object.

  • sce contains the single cell data extracted from the multichannel images using the cell segmentation masks, as well as the associated metadata, in the form of a SingleCellExperiment. This represents a total of 285,851 cells x 42 channels.

  • spe same single cell data as for sce, but in the SpatialExperiment format.

All data are downloaded from ExperimentHub and cached for local re-use.

Mapping between the three data objects is performed via variables located in their metadata columns: mcols() for the CytoImageList objects and ColData() for the SingleCellExperiment and SpatialExperiment objects. Mapping at the image level can be performed with the image_name variable. Mapping between cell segmentation masks and single cell data is performed with the cell_number variable, the values of which correspond to the intensity values of the masks object. For practical examples, please refer to the "Accessing IMC datasets" vignette.

This dataset is a subset of the complete Jackson, Fischer et al. (2020) dataset comprising the data from tumour tissue from 100 patients with breast cancer (one image per patient). By default, data from the "Basel" cohort are returned. By setting cohort = "Zurich", data from the "Zurich" cohort, corresponding to images and associated data from 72 patients, are returned. For details about the patient cohorts, refer to the publication. If full_dataset = TRUE, the full dataset is returned (including both "Basel" and "Zurich" patient cohorts). This option is not available for multichannel images.

The assay slot of the SingleCellExperiment object contains three assays:

  • counts contains mean ion counts per cell.

  • exprs contains arsinh-transformed counts, with cofactor 1.

  • quant_norm contains quantile-normalized counts (0 to 1, 99th percentile).

The marker-associated metadata, including antibody information and metal tags are stored in the rowData of the SingleCellExperiment and SpatialExperiment objects.

The cell-associated metadata are stored in the colData of the SingleCellExperiment and SpatialExperiment objects. These metadata include clusters (in colData(sce)$cell_cluster_phenograph) and metaclusters (in colData(sce)$cell_metacluster), as well as spatial information (e.g., cell areas are stored in colData(sce)$cell_area).

The clinical data are also stored in the colData of the SingleCellExperiment and SpatialExperiment objects. For instance, the tumor grades can be retrieved with colData(sce)$tumor_grade.

Dataset versions: a version argument can be passed to the function to specify which dataset version should be retrieved.

  • `v0`: original version (Bioconductor <= 3.15).

  • `v1`: consistent object formatting across datasets.

  • `v2`: added full datasets and Zurich cohort.

File sizes:

  • `images_basel`: size in memory = 19 Gb, size on disk = 2.0 Gb.

  • `masks_basel`: size in memory = 433 Mb, size on disk = 10 Mb.

  • `sce_basel`: size in memory = 513 Mb, size on disk = 270 Mb.

  • `images_zurich`: size in memory = 6.0 Gb, size on disk = 724 Mb.

  • `masks_zurich`: size in memory = 137 Mb, size on disk = 3.4 Mb.

  • `sce_zurich`: size in memory = 188 Mb, size on disk = 105 Mb.

  • `masks_full`: size in memory = 2.1 Gb, size on disk = 10 Mb.

  • `sce_full`: size in memory = 2.2 Gb, size on disk = 1.2 Gb.

When storing images on disk, these need to be first fully read into memory before writing them to disk. This means the process of downloading the data is slower than directly keeping them in memory. However, downstream analysis will lose its memory overhead when storing images on disk.

Original source: Jackson, Fischer et al. (2020): https://doi.org/10.1038/s41586-019-1876-x

Original link to raw data, containing the entire dataset: https://doi.org/10.5281/zenodo.3518284

References

Jackson, Fischer et al. (2020). The single-cell pathology landscape of breast cancer. Nature 578(7796), 615-620.

Author

Nicolas Damond

Examples

# Load single cell data
sce <- JacksonFischer_2020_BreastCancer(data_type = "sce")
#> see ?imcdatasets and browseVignettes('imcdatasets') for documentation
#> downloading 1 resources
#> retrieving 1 resource
#> 
#> loading from cache
print(sce)
#> class: SingleCellExperiment 
#> dim: 45 285851 
#> metadata(0):
#> assays(3): counts exprs quant_norm
#> rownames(45): Ru96 Ru98 ... DNA1 DNA2
#> rowData names(6): metal name ... channel antibody_clone
#> colnames(285851): 1_1 1_2 ... 375_3941 375_3942
#> colData names(74): image_name cell_id ... patient_cohort cell_number
#> reducedDimNames(0):
#> mainExpName: JacksonFischer_2020_BreastCancer_Basel_v2
#> altExpNames(0):

# Display metadata
JacksonFischer_2020_BreastCancer(data_type = "sce", metadata = TRUE)
#> ExperimentHub with 1 record
#> # snapshotDate(): 2026-04-21
#> # names(): EH7834
#> # package(): imcdatasets
#> # $dataprovider: University of Zurich
#> # $species: Homo sapiens
#> # $rdataclass: SingleCellExperiment
#> # $rdatadateadded: 2023-01-30
#> # $title: JacksonFischer_2020_BreastCancer_Basel - sce - v2
#> # $description: Single cell data (Basel cohort subset) for the JacksonFische...
#> # $taxonomyid: 9606
#> # $genome: NA
#> # $sourcetype: Zip
#> # $sourceurl: https://doi.org/10.5281/zenodo.3518284
#> # $sourcesize: NA
#> # $tags: c("Homo_sapiens_Data", "ImmunoOncologyData",
#> #   "ReproducibleResearch", "SingleCellData", "SpatialData",
#> #   "TechnologyData", "Tissue") 
#> # retrieve record with 'object[["EH7834"]]' 

# Load masks on disk
library(HDF5Array)
masks <- JacksonFischer_2020_BreastCancer(data_type = "masks", on_disk = 
TRUE, h5FilesPath = getHDF5DumpDir())
#> see ?imcdatasets and browseVignettes('imcdatasets') for documentation
#> downloading 1 resources
#> retrieving 1 resource
#> 
#> loading from cache
print(head(masks))
#> CytoImageList containing 6 image(s)
#> names(6): BaselTMA_SP41_18_X13Y5 BaselTMA_SP41_114_X13Y4 BaselTMA_SP41_117_X13Y3 BaselTMA_SP41_42_X14Y5 BaselTMA_SP41_166_X15Y4 BaselTMA_SP41_86_X15Y3 
#> Each image contains 1 channel