The VectraPolarisData ExperimentHub package provides two large multiplex immunofluorescence datasets collected by Akoya Biosciences Vectra 3 and Vectra Polaris platforms. Image preprocessing (cell segmentation and phenotyping) was performed using Inform software. Data cover are formatted into objects of class SpatialExperiment.
To retrieve a dataset, we can use a dataset’s corresponding named function
<id> should correspond to one a valid dataset identifier (see
?VectraPolarisData). Below both the lung and ovarian cancer datasets are loaded this way.
library(VectraPolarisData) spe_lung <- HumanLungCancerV3() spe_ovarian <- HumanOvarianCancerVP()
Alternatively, data can loaded directly from Bioconductor’s ExperimentHub as follows. First, we initialize a hub instance and store the complete list of records in a variable
query(), we then identify any records made available by the
VectraPolarisData package, as well as their accession IDs (EH7311 for the lung cancer data). Finally, we can load the data into R via
id corresponds to the data entry’s identifier we’d like to load. E.g.:
library(ExperimentHub) eh <- ExperimentHub() # initialize hub instance q <- query(eh, "VectraPolarisData") # retrieve 'VectraPolarisData' records id <- q$ah_id # specify dataset ID to load spe <- eh[[id]] # load specified dataset
HumanOvarianCancerVP() datasets are stored as
SpatialExperiment objects. This allows users of our data to interact with methods built for
SpatialExperiment class methods in Bioconductor. See this ebook for more details on
SpatialExperiment. To get cell level tabular data that can be stored in this format, raw multiplex.tiff images have been preprocessed, segmented and cell phenotyped using
Inform software from Akoya Biosciences.
SpatialExperiment class was originally built for spatial transcriptomics data and follows the structure depicted in the schematic below (Righelli et al. 2021):