Kandinsky: Enabling neighbourhood analysis of spatial omics data for functional insights on cell ecosystems.
7 affiliations
- 1Centre for Cancer Evolution, Barts Cancer Institute, Queen Mary University of London, London EC1M 6AU, UK.
- 2Cancer Systems Biology Laboratory, The Francis Crick Institute, London NW1 1AT, UK.
- 3Department of Oncology and Hemato-Oncology, University of Milan, Milan 20122, Italy.
- 4Italian Institute for Genomic Medicine, c/o IRCCS, Candiolo 10060, Italy.
- 5IFOM ETS-The AIRC Institute of Molecular Oncology, Milan 20139, Italy.
- 6Genomics Research Platform and Single Cell Laboratory, Guy's and St Thomas' NHS Foundation Trust, London SE1 9RT, UK.
- 7Department of Research Pathology, UCL Cancer Institute, University College London, London WC1E 6DD, UK.
Abstract
Aims
Spatially resolved omics technologies enable investigation of cellular interactions within their local microenvironments (neighbourhoods) directly in situ. Although several computational methods have been developed for neighbourhood analysis, significant limitations remain in how neighbourhoods are defined and interrogated. Here, we present Kandinsky, a toolkit that provides a flexible and versatile framework for defining and analysing cell neighbourhoods.Methods
We developed Kandinsky to improve flexibility in neighbourhood analysis and maximise compatibility with a wide range of spatial omics data. We therefore implemented multiple approaches for identifying cell- or spot-based neighbourhoods that serve as input for four analytical modules: differential gene or protein expression analysis, neighbourhood clustering, co-localisation/dispersion, and spatial hot and cold areas. In addition to its core functionality, Kandinsky enables the execution of external tools within the same analytical framework.Results
We applied Kandinsky to real and simulated spatial datasets to benchmark its performance against existing methods and demonstrate its ability to uncover biologically meaningful spatial interactions. Kandinsky achieved competitive performance in terms of accuracy, memory usage, and runtime. In real datasets, it suggested transcriptional changes associated with acinar-to-beta cell reprogramming in the healthy pancreas; recapitulated stromal, immune, and tumour-associated clusters in pancreatic cancer; revealed the spatial co-localisation of myoepithelial cells with specific breast cancer subpopulations; and confirmed the association between regions of high CD74 expression and immune cell infiltration.Conclusion
Kandinsky is a flexible and versatile toolkit for neighbourhood analysis that facilitates the exploration and interpretation of complex spatial omics data.
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BibTeX
@article{andrei2026kandinsky,
title = {{Kandinsky: Enabling neighbourhood analysis of spatial omics data for functional insights on cell ecosystems.}},
author = {Pietro Andrei and Mariachiara Grieco and Amelia Acha-Sagredo and Tommaso Becchi and Pawan Dhami and Kathy Fung and Manuel Rodriguez-Justo and Matteo Cereda and Francesca D Ciccarelli},
journal = {EXO : beyond the cell},
year = {2026},
month = jul,
doi = {10.70401/exo.2025.0017},
url = {https://europepmc.org/article/MED/42924602},
note = {CC BY},
}
RIS
TY - JOUR
TI - Kandinsky: Enabling neighbourhood analysis of spatial omics data for functional insights on cell ecosystems.
AU - Pietro Andrei
AU - Mariachiara Grieco
AU - Amelia Acha-Sagredo
AU - Tommaso Becchi
AU - Pawan Dhami
AU - Kathy Fung
AU - Manuel Rodriguez-Justo
AU - Matteo Cereda
AU - Francesca D Ciccarelli
PY - 2026
DA - 2026/07/31
JO - EXO : beyond the cell
DO - 10.70401/exo.2025.0017
UR - https://europepmc.org/article/MED/42924602
AB - Aims Spatially resolved omics technologies enable investigation of cellular interactions within their local microenvironments (neighbourhoods) directly in situ. Although several computational methods have been developed for neighbourhood analysis, significant limitations remain in how neighbourhoods are defined and interrogated. Here, we present Kandinsky, a toolkit that provides a flexible and versatile framework for defining and analysing cell neighbourhoods.Methods We developed Kandinsky to improve flexibility in neighbourhood analysis and maximise compatibility with a wide range of spatial omics data. We therefore implemented multiple approaches for identifying cell- or spot-based neighbourhoods that serve as input for four analytical modules: differential gene or protein expression analysis, neighbourhood clustering, co-localisation/dispersion, and spatial hot and cold areas. In addition to its core functionality, Kandinsky enables the execution of external tools within the same analytical framework.Results We applied Kandinsky to real and simulated spatial datasets to benchmark its performance against existing methods and demonstrate its ability to uncover biologically meaningful spatial interactions. Kandinsky achieved competitive performance in terms of accuracy, memory usage, and runtime. In real datasets, it suggested transcriptional changes associated with acinar-to-beta cell reprogramming in the healthy pancreas; recapitulated stromal, immune, and tumour-associated clusters in pancreatic cancer; revealed the spatial co-localisation of myoepithelial cells with specific breast cancer subpopulations; and confirmed the association between regions of high CD74 expression and immune cell infiltration.Conclusion Kandinsky is a flexible and versatile toolkit for neighbourhood analysis that facilitates the exploration and interpretation of complex spatial omics data.
ER -