Motivation: High parameter histological techniques have allowed for the identification of a variety of distinct cell types within an image, providing a comprehensive overview of the tissue environment. This allows the complex cellular architecture and environment of diseased tissue to be explored. While spatial analysis techniques have revealed how cell-cell interactions are important within the disease pathology, there remains a gap in exploring changes in these interactions within the disease process. Specifically, there are currently few established methods for performing inference on cell-type co-localization changes across images, hindering an understanding of how cellular environments change with a disease pathology.
Results: We have developed the spicyR R package to perform inference on changes in the spatial co-localization of types across groups of images. Application to simulated data demonstrates a high sensitivity and specificity. We the utility of spicyR by applying it to a type 1 diabetes imaging mass cytometry dataset, revealing changes in cellular associations that were relevant to the disease progression. Ultimately, spicyR allows changes in cellular environments to be explored under different pathologies or disease states.
Availability and implementation: R package is freely available at http://bioconductor.org/packages/release/bioc/html/spicyR.html and shiny app implementation at http://shiny.maths.usyd.edu.au/spicyR/.
Supplementary information: Supplementary data are available at Bioinformatics online.
© The Author(s) 2022. Published by Oxford University Press.