R/Zanotelli_2020_Spheroids.R
Zanotelli_2020_Spheroids.RdObtain the Zanotelli_2020_Spheroids dataset, which consists of three data objects: single cell data, multichannel images and cell segmentation masks. The data were obtained by imaging mass cytometry (IMC) of sections of 3D spheroids generated from different cell lines.
Zanotelli_2020_Spheroids(
data_type = c("sce", "spe", "images", "masks"),
version = "latest",
metadata = FALSE,
on_disk = FALSE,
h5FilesPath = NULL,
force = FALSE
)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.
dataset version. By default, the latest version is returned.
if FALSE (default), the data object selected in
data_type is returned. If TRUE, only the metadata associated to this
object is returned.
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.
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().
logical indicating if images should be overwritten when files with the same name already exist on disk.
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.
This is an Imaging Mass Cytometry (IMC) dataset from Zanotelli et al. (2020), consisting of three data objects:
images contains 517 multichannel images, each containing 51
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. This represents a total of 229,047
cells x 51 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 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.
This dataset was obtained as following (the names of the experimental
variables, located in the colData of the
SingleCellExperiment and SpatialExperiment
objects, are indicated in parentheses): i) Cells from four different
cell lines (cell_line) were seeded at three different densities
(treatment_concentration, relative densities) and grown for either 72
or 96 hours (treatment_time_point, duration in hours). In the
appropriate experimental conditions (see the paper for details), the cells
aggregate into 3D spheroids. ii) Cells were harvested and pooled into
60-well barcoding plates. iii) A pellet of each spheroid pool was
generated and cut into several 6 um-thick sections. iv) A subset of
these sections (site_id) were stained with an IMC panel and acquired
as one or more acquisitions (acquisition_id) containing multiple
spheres each. v) Spheres in these acquisitions were identified by
computer vision and cropped into individual images (image_number).
Other relevant cell metadata include:
treatment_name: experimental conditions in the format:
"Cell line name"_c"seeding density"_tp"time point".
cell_x/cell_y: cell centroid position in the image.
cell_area: area of the cell (um^2).
distance_rim: estimated distance to spheroid border.
distance_sphere: distance to spheroid section border.
distance_other_sphere: distance to the closest of the other
spheroid sections in the same image (if there is any).
distance_background: distance to background pixels.
For a full description of the other experimental variables, please refer to the publication (https://doi.org/10.15252/msb.20209798) and to the original dataset repository (https://doi.org/10.5281/zenodo.4271910).
The marker-associated metadata, including antibody information and metal
tags are stored in the rowData of the
SingleCellExperiment and SpatialExperiment
objects. The channels with names starting with "BC_" are the channels used
for barcoding. Post-transcriptional modification of the protein targets are
indicated in brackets.
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.
quant_norm contains counts censored at the 99th percentile
and scaled 0-1.
In addition, the altExp slot of the
SingleCellExperiment object contains another
SingleCellExperiment object where the counts matrix represents
raw mean ion counts for cells neighboring the current cell.
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. Cells in the SelfHits
object are represented by unique integers that map to the
cell_number_absolute column of colData(sce).
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.
File sizes:
`images`: size in memory = 21.2 Gb, size on disk = 860 Mb.
`masks`: size in memory = 426 Mb, size on disk = 12 Mb.
`sce`: size in memory = 564 Mb, size on disk = 319 Mb.
`spe`: size in memory = 596 Mb, size on disk = 320 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: Zanotelli et al. (2020): https://doi.org/10.15252/msb.20209798
Original link to raw data, also containing the entire dataset: https://doi.org/10.5281/zenodo.4271910
Zanotelli VRT et al. (2020). A quantitative analysis of the interplay of environment, neighborhood, and cell state in 3D spheroids Mol Syst Biol 16(12), e9798.
# Load single cell data
sce <- Zanotelli_2020_Spheroids(data_type = "sce")
#> snapshotDate(): 2023-10-24
#> see ?imcdatasets and browseVignettes('imcdatasets') for documentation
#> loading from cache
print(sce)
#> class: SingleCellExperiment
#> dim: 51 229047
#> metadata(0):
#> assays(3): counts exprs quant_norm
#> rownames(51): BC_Pd102 BC_Rh103 ... BC_Bi209 BC_Y89
#> rowData names(9): channel metal ... antibody_cell_cycle measurement_id
#> colnames(229047): 2_40 2_41 ... 736_1538 736_1554
#> colData names(32): cell_id image_name ... sampleblock_name
#> cell_number_absolute
#> reducedDimNames(0):
#> mainExpName: Zanotelli_2020_Spheroids
#> altExpNames(1): neighboring_cells
# Display metadata
Zanotelli_2020_Spheroids(data_type = "sce", metadata = TRUE)
#> snapshotDate(): 2023-10-24
#> ExperimentHub with 1 record
#> # snapshotDate(): 2023-10-24
#> # names(): EH7731
#> # package(): imcdatasets
#> # $dataprovider: University of Zurich
#> # $species: Homo sapiens
#> # $rdataclass: SingleCellExperiment
#> # $rdatadateadded: 2022-10-17
#> # $title: Zanotelli_2020_Spheroids - sce - v1
#> # $description: Single cell data for the Zanotelli_2020_Spheroids IMC dataset
#> # $taxonomyid: 9606
#> # $genome: NA
#> # $sourcetype: Zip
#> # $sourceurl: https://zenodo.org/record/4271910#.YGWR_T8kz-i
#> # $sourcesize: NA
#> # $tags: c("SingleCellData", "TechnologyData", "Tissue")
#> # retrieve record with 'object[["EH7731"]]'
# Load masks on disk
library(HDF5Array)
masks <- Zanotelli_2020_Spheroids(data_type = "masks", on_disk = TRUE,
h5FilesPath = getHDF5DumpDir())
#> snapshotDate(): 2023-10-24
#> see ?imcdatasets and browseVignettes('imcdatasets') for documentation
#> loading from cache
print(head(masks))
#> CytoImageList containing 6 image(s)
#> names(6): 20190619_p173_slide31_ac1_vz_s1_p1_r11_a11_ac_full_l2_x217_y72 20190619_p173_slide31_ac1_vz_s1_p1_r13_a13_ac_full_l1_x0_y0 20190619_p173_slide31_ac1_vz_s1_p1_r14_a14_ac_full_l1_x0_y127 20190619_p173_slide31_ac1_vz_s1_p1_r14_a14_ac_full_l2_x277_y0 20190619_p173_slide31_ac1_vz_s1_p1_r14_a14_ac_full_l3_x389_y144 20190619_p173_slide31_ac1_vz_s1_p1_r15_a15_ac_full_l1_x0_y0
#> Each image contains 1 channel