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Obtain the SteenbuckDamond_2026_T1D dataset

Source: R/SteenbuckDamond_2026_T1D.R
SteenbuckDamond_2026_T1D.Rd

Obtain the SteenbuckDamond_2026_T1D dataset, which is composed of two panels (Islet + Immune) that were acquired on two consecutive 4 µm sections. Each dataset (panel) is composed of three data objects: single cell data, multichannel images and cell segmentation masks. The data was obtained by imaging mass cytometry (IMC) measuring formalin-fixed paraffin-embedded (FFPE) tissue from 88 individuals along the disease progression of T1D. It contains, both Controls, single and multi-auto-antibody positive T1D donors (sAAb+/mAAb+), and donors with recent and long-standing T1D (Onset/LD).

Usage

SteenbuckDamond_2026_T1D(
  data_type = c("sce", "spe", "images", "masks"),
  panel = "islet",
  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.

panel

which panel should be returned? Can be set to "islet" (default) or "immune".

full_dataset

if FALSE (default), a subset of 120 informative images is returned. These are sampled along the disease progression of T1D. It also contains insulitic images, i.e. T-cell infiltration of islets. If TRUE, the full dataset (corresponding to 7024 images) is returned. Due to memory space limitations, this option is only available for single cell data and masks, not for data_type = "images". All images can be retrieved from Zenodo.

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 Steenbuck, Damond et al. (2026):

  • images contains 120 47-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:

  • raw contains raw mean ion counts per cell.

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

  • scaled contains scaled asinh-transformed raw counts.

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, milieu, samples, and patients. For instance, cell types can be retrieved with colData(sce)$cell_type and immune cell clusters with colData(sce)$cell_cluster_scaled. The latter for the Immune panel only.

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 Steenbuck, Damond, 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_immune`: size in memory = 6.3 Gb, size on disk = 650.3 Mb.

  • `masks_immune`: size in memory = 138 Mb, size on disk = 4.7 Mb.

  • `sce_immune`: size in memory = 442 Mb, size on disk = 217.1 Mb.

  • `masks_full_immune`: size in memory = 8.6 Gb, size on disk = 337.3 Mb.

  • `sce_full_immune`: size in memory = 21.5 Gb, size on disk = 13.0 Gb.

  • `images_islet`: size in memory = 3.8 Gb, size on disk = 738.8 Mb.

  • `masks_islet`: size in memory = 83 Mb, size on disk = 2.8 Mb.

  • `sce_islet`: size in memory = 277 Mb, size on disk = 128.5 Mb.

  • `masks_full_islet`: size in memory = 5.1 Gb, size on disk = 194.8 Mb.

  • `sce_full_islet`: size in memory = 12.8 Gb, size on disk = 6.8 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: Steenbuck, Damond, et al. (2026): https://doi.org/10.1038/s42255-026-01559-z

Original link to raw data: https://doi.org/10.5281/zenodo.14968076.

References

Steenbuck, N., Damond, N., et al. (2026). Imaging mass cytometry reveals functional and immunological changes during type 1 diabetes progression in human pancreata. Nat Metab https://doi.org/10.1038/s42255-026-01559-z

Author

Nathan Steenbuck

Examples

# Load single cell data
sce <- SteenbuckDamond_2026_T1D(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
SteenbuckDamond_2026_T1D(data_type = "sce", metadata = TRUE)
#> ExperimentHub with 0 records
#> # snapshotDate(): 2026-04-21

# Load masks on disk
library(HDF5Array)
masks <- SteenbuckDamond_2026_T1D(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