Obtain the jackson-fischer-2020 dataset, which consists of three data objects: single cell data, multichannel images and cell segmentation masks. The data was obtained by imaging mass cytometry of tumour tissue from patients with breast cancer.
JacksonFischer2020Data( data_type = c("sce", "images", "masks"), metadata = FALSE, on_disk = FALSE, h5FilesPath = NULL, force = FALSE )
type of object to load, should be 'sce' for single cell data, 'images' for multichannel images or 'masks' for cell segmentation masks.
if FALSE (default), the data object selected in
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
path to where the .h5 files for on disk representation
are stored. This path needs to be defined when
logical indicating if images should be overwritten when files with the same name already exist on disk.
This is an Imaging Mass Cytometry (IMC) dataset from Jackson, Fischer et al. (2020), consisting of three data objects:
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.
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
ColData() for the SingleCellExperiment
object. Mapping at the image level can be performed with the
ImageNb variable. Mapping between cell segmentation masks and single
cell data is performed with the
CellNb variable, the values of which
correspond to the intensity values of the
object. For practical examples, please refer to the "Accessing IMC datasets"
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).
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,
The marker-associated metadata, including antibody information and metal tags
are stored in the
rowData of the SingleCellExperiment
The cell-associated metadata are stored in the
colData of the
SingleCellExperiment object. These metadata include clusters
colData(sce)$PhenoGraphBasel) and metaclusters (in
colData(sce)$metacluster), as well as spatial information (e.g., cell
areas are stored in
The patient-associated clinical data are also stored in the
the SingleCellExperiment object. For instance, the tumor grades
can be retrieved with
`images`: size in memory = 17.8 Gb, size on disk = 1.99 Gb.
`masks`: size in memory = 433 Mb, size on disk = 10.2 Mb.
`sce`: size in memory = 517 Mb, size on disk = 272 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: 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
A SingleCellExperiment object with single cell data, a CytoImageList object containing multichannel images, or a CytoImageList object containing cell masks.
Jackson, Fischer et al. (2020). The single-cell pathology landscape of breast cancer. Nature 578(7796), 615-620.
# Load single cell data sce <- JacksonFischer2020Data(data_type = "sce") print(sce) # Display metadata JacksonFischer2020Data(data_type = "sce", metadata = TRUE) # Load masks on disk library(HDF5Array) masks <- JacksonFischer2020Data(data_type = "masks", on_disk = TRUE, h5FilesPath = getHDF5DumpDir()) print(head(masks))
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