Skip to contents

Obtain the Meyer_2025_TripleNegativeBreastCancer dataset

Source: R/Meyer_2025_TripleNegativeBreastCancer.R
Meyer_2025_TripleNegativeBreastCancer.Rd

Obtain the Meyer_2025_TripleNegativeBreastCancer dataset. The dataset is composed of three data objects: single cell data, multichannel images and cell segmentation masks. The data was obtained by imaging mass cytometry (IMC) of a tissue microarray (TMA) with multiple cores of formalin-fixed paraffin-embedded (FFPE) tissue from 215 patients with triple-negative breast cancer.

Usage

Meyer_2025_TripleNegativeBreastCancer(
  data_type = c("sce", "spe", "images", "masks"),
  full_dataset = FALSE,
  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 to 125 images from 60 patients sampled across proposed patient groups is returned. This includes all images visually presented in the publication. If TRUE, the full dataset (corresponding to 450 images) is returned. Due to memory space limitations, this option is only available for single cell data and masks, not for data_type = "images".

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 Meyer et al. (2025):

  • images contains 125 39-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 object.

  • 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 or image_number variables. 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.

The assay slots of the SingleCellExperiment and SpatialExperiment objects contain three assays:

  • counts contains raw mean ion counts per cell.

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

  • min_max contains 0-1 normalized .

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

The cell-associated metadata are stored in the colData of the SingleCellExperiment and SpatialExperiment objects. These metadata include various information about cells, tumors and patients. For instance, cell metacluster can be retrieved with colData(sce)$cell_metacluster and patient groups with colData(sce)$patient_patientgroup.

Neighborhood information, defined here as cells that are localized next to each other, is stored as a SelfHits object in the colPairs slot of the SingleCellExperiment and SpatialExperiment objects.

For more information, please refer to the Meyer et al. publication.

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

  • `v1`: first published version

File sizes:

  • `images`: size in memory = 20.9 Gb, size on disk = 1.6 Gb.

  • `masks`: size in memory = 269 Mb, size on disk = 8 Mb.

  • `masks_full`: size in memory = 942 Mb, size on disk = 29 Mb.

  • `sce`: size in memory = 451 Mb, size on disk = 241 Mb.

  • `sce_full`: size in memory = 1.6 Gb, size on disk = 866 Mb.

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: Meyer et al. (2025): https://doi.org/10.1016/j.ccell.2025.06.019

Original link to raw data: https://zenodo.org/records/15304181.

References

Meyer, Jackson et al. (2025). A stratification system for breast cancer based on basoluminal tumor cells and spatial tumor architecture Cancer Cell 43(9):1637–1655.e9.

Author

Lasse Meyer

Examples

# Load single cell data
sce <- Meyer_2025_TripleNegativeBreastCancer(data_type = "sce")
#> Error in .local(x, i, j = j, ...): 'i' must be length 1
print(sce)
#> Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'print': object 'sce' not found

# Display metadata
Meyer_2025_TripleNegativeBreastCancer(data_type = "sce", metadata = TRUE)
#> ExperimentHub with 0 records
#> # snapshotDate(): 2026-04-21

# Load masks on disk
library(HDF5Array)
masks <- Meyer_2025_TripleNegativeBreastCancer(data_type = "masks", on_disk = TRUE,
h5FilesPath = getHDF5DumpDir())
#> Error in .local(x, i, j = j, ...): 'i' must be length 1
print(head(masks))
#> Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'print': error in evaluating the argument 'x' in selecting a method for function 'head': object 'masks' not found