Kandinsky: enabling neighbourhood analysis of spatial omics data for functional insights on cell ecosystems

Abstract

Spatially resolved omics technologies enable investigation of how cells interact within their local environments or neighbourhoods directly in situ. Although a few computational methods have been developed to aid this analysis, significant limitations still exist in the way neighbourhoods are defined and exploited for downstream analyses. Here, we present Kandinsky, a computational tool that implements multiple approaches for neighbourhood identification, enabling high flexibility and versatility to address a variety of biological questions. Once identified, Kandinsky applies neighbourhoods for downstream studies, including proximity-based cell grouping for functional comparisons, spatial co-localisation and dispersion, and identification of hot and cold expression areas within the tissue. We apply Kandinsky to transcriptomic and proteomic data from different spatial technologies to showcase how it can reveal functional interactions between cells across multiple biological contexts. Availability and implementationKandinsky is freely available as an R package at https://github.com/ciccalab/Kandinsky.