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Obtain the IMMUcan_2022_CancerExample dataset

Source: R/IMMUcan_2022_CancerExample.R
IMMUcan_2022_CancerExample.Rd

Obtain the IMMUcan_2022_CancerExample dataset, which consists of three data objects: single cell data, multichannel images and cell segmentation masks. Data were obtained by imaging mass cytometry (IMC) of sections of 4 patients with different tumor indications.

Usage

IMMUcan_2022_CancerExample(
  data_type = c("sce", "spe", "images", "masks"),
  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.

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 CytoImageList object containing multichannel images, or a CytoImageList object containing cell segmentation masks.

Details

This is an Imaging Mass Cytometry (IMC) dataset used in the IMC data analysis book

  • images contains 14 multichannel images, each containing 50 channels, 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. Single cell data can also be retrieved as a SpatialExperiment object. This represents a total of 46,825 cells x 40 channels.

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 object. Mapping at the image level can be performed with the sample_id or image_name 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.

This imaging mass cytometry dataset serves as an example to demonstrate downstream analysis tools including spatial data analysis. The data was generated as part of the Integrated iMMUnoprofiling of large adaptive CANcer patient cohorts (IMMUcan) project (immucan.eu) using the Hyperion imaging system.

Relevant entries to the colData slot are as follows:

  • sample_id image name.

  • cell_number cell identifier.

  • width_px width of the image.

  • height_px height of the image.

  • patient_id patient identifier.

  • ROI region of interest identifier.

  • indication cancer type.

  • cell_labels labels of manually labelled cells.

  • cell_type cell type as defined by classification.

  • spatial_community identifiers of each spatial tumor or non-tumor community

  • cn_celltypes cellular neighborhoods as defined by clustering cells based on the frequency of neighboring cell types.

  • cn_expression cellular neighborhoods as defined by clustering cells based on the mean expression of neighboring cells

  • lisa_clusters cellular neighborhoods as detected by the lisaClust package.

  • spatial_context spatial contexts defined in cn_celltype.

  • spatial_context_filtered filtered spatial context identifiers.

  • patch_id identifier of the spatial tumor patch.

  • cell_x spatial x coordinate.

  • cell_y spatial y coordinate.

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

The assay slot of the SingleCellExperiment object contains two assays:

  • counts: mean ion counts per cell

  • exprs: arsinh-transformed counts per cell, with cofactor 1.

The colPair slot of the SingleCellExperiment object contains the following spatial object graphs:

  • neighborhood steinbock generated graph.

  • knn_interaction_graph 20-nearest neighbor graph.

  • expansion_interaction_graph expansion graph using a threshold of 20.

  • delaunay_interaction_graph interaction graph constructed by delaunay triangulation.

  • knn_spatialcontext_graph 40-nearest neighbor graph.

File sizes:

  • `images`: size in memory = 1.5 Gb, size on disk = 786 Mb.

  • `masks`: size in memory = 19 Mb, size on disk = 1.2 Mb.

  • `sce`: size in memory = 182 Mb, size on disk = 82 Mb.

  • `spe`: size in memory = 183 Mb, size on disk = 81 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.

Author

Nils Eling

Examples

# Load single cell data
sce <- IMMUcan_2022_CancerExample(data_type = "sce")
#> see ?imcdatasets and browseVignettes('imcdatasets') for documentation
#> downloading 1 resources
#> retrieving 1 resource
#> 
#> loading from cache
print(sce)
#> class: SingleCellExperiment 
#> dim: 40 47794 
#> metadata(5): color_vectors cluster_codes SOM_codes delta_area
#>   filterSpatialContext
#> assays(2): counts exprs
#> rownames(40): MPO H3 ... DNA1 DNA2
#> rowData names(17): channel metal ... ilastik deepcell
#> colnames(47794): 1_1 1_2 ... 14_2844 14_2845
#> colData names(43): sample_id ObjectNumber ... cell_x cell_y
#> reducedDimNames(8): UMAP TSNE ... seurat UMAP_seurat
#> mainExpName: IMMUcan_2022_CancerExample_v1
#> altExpNames(0):

# Display metadata
IMMUcan_2022_CancerExample(data_type = "sce", metadata = TRUE)
#> ExperimentHub with 1 record
#> # snapshotDate(): 2026-04-21
#> # names(): EH7842
#> # package(): imcdatasets
#> # $dataprovider: University of Zurich
#> # $species: Homo sapiens
#> # $rdataclass: SingleCellExperiment
#> # $rdatadateadded: 2023-01-30
#> # $title: IMMUcan_2022_CancerExample - sce - v1
#> # $description: Single cell data for the IMMUcan_2022_CancerExample IMC dataset
#> # $taxonomyid: 9606
#> # $genome: NA
#> # $sourcetype: Zip
#> # $sourceurl: https://zenodo.org/record/6810879
#> # $sourcesize: NA
#> # $tags: c("Homo_sapiens_Data", "ImmunoOncologyData",
#> #   "ReproducibleResearch", "SingleCellData", "SpatialData",
#> #   "TechnologyData", "Tissue") 
#> # retrieve record with 'object[["EH7842"]]' 

# Load masks on disk
library(HDF5Array)
masks <- IMMUcan_2022_CancerExample(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): Patient1_001 Patient1_002 Patient1_003 Patient2_001 Patient2_002 Patient2_003 
#> Each image contains 1 channel