Kandinsky: enabling neighbourhood analysis of spatial omics data for functional insights on cell ecosystems
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Pietro Andrei Mariachiara Grieco Amelia Acha Pawan Dhami Kathy Fung Manuel Rodriguez-Justo Matteo Cereda Francesca CiccarelliThis article is a preprint. Preprints have not been peer-reviewed.
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Abstract
A major breakthrough of spatial omics is the opportunity to investigate how cells interact within their local environment or neighbourhood and several computational methods have been developed to aid this analysis. However, significant limitations still exist in the way neighbourhoods are defined and used for downstream studies. Here, we describe Kandinsky, a computational tool that implements multiple approaches for neighbourhood identification and analysis, enabling high flexibility and versatility to address a variety of biological questions. Once identified, Kandinsky applies neighbourhoods for downstream functional analyses, including proximity-based cell grouping, 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. Kandinsky is freely available as an R package at https://github.com/ciccalab/Kandinsky.
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bioRxiv
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