spicyR - Test Changes in the Co-Localisation of Cell Types Between Groups of Patients
spicyR tests whether the spatial co-localisation of pairs of cell types differs between groups of patients, or is associated with patient outcome, in cell-resolution spatial omics data (imaging mass cytometry, CODEX, MIBI, Xenium, CosMx and others). Its default method, spicyR Cell, counts the cells of one type within a radius of each cell of another and compares the count with its exact expectation under random labelling of the observed cells. Images are combined within patients by a frailty generalised estimating equation, and differences are tested with a small-sample cluster-robust (CR2) variance with patients as the units. Covariates, several conditions, several radii and survival outcomes are supported, and the original image-level test is kept as an option. The computations run in a C++ core shared with the Python package spicyr.
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singlecellcellbasedassaysspatialsurvivalcpp
8.54 score 12 stars 2 dependents 88 scripts 746 downloadslisaClust - lisaClust: Clustering of Local Indicators of Spatial Association
lisaClust provides a series of functions to identify and visualise regions of tissue where spatial associations between cell-types is similar. This package can be used to provide a high-level summary of cell-type colocalization in multiplexed imaging data that has been segmented at a single-cell resolution.
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singlecellcellbasedassaysspatialcpp
6.50 score 4 stars 56 scripts 502 downloadsSpatialDatasets - Collection of spatial omics datasets
This is a collection of publically available spatial omics datasets. Where possible we have curated these datasets as either SpatialExperiments, MoleculeExperiments or CytoImageLists and included annotations of the sample characteristics.
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experimentdataexperimenthubreproducibleresearchsinglecelldataspatialdatahomo_sapiens_datamus_musculus_datadatapkg
5.03 score 18 scriptssimpleSeg - A package to perform simple cell segmentation
Image segmentation is the process of identifying the borders of individual objects (in this case cells) within an image. This allows for the features of cells such as marker expression and morphology to be extracted, stored and analysed. simpleSeg provides functionality for user friendly, watershed based segmentation on multiplexed cellular images in R based on the intensity of user specified protein marker channels. simpleSeg can also be used for the normalization of single cell data obtained from multiple images.
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classificationsurvivalsinglecellnormalizationspatialspatial-statisticscpp
4.85 score 1 stars 28 scripts 557 downloads