Kandinsky: Enabling neighbourhood analysis of spatial omics data for functional insights on cell ecosystems
*Correspondence to:
Francesca D Ciccarelli, Centre for Cancer Evolution, Bart’s Cancer Institute, Queen Mary University London, London EC1M 6AU, UK; Cancer Systems Biology Laboratory, The Francis Crick Institute, London NW1 1AT, UK; Department of Oncology and Hemato-Oncology, University of Milan, Milan 20142, Italy.
E-mail: f.ciccarelli@qmul.ac.uk
EXO. 2026;1:202616. 10.70401/EXO.2025.0017
Received: April 20, 2026Accepted: July 30, 2026Published: July 31, 2026
This manuscript is made available in its unedited form to allow early access to the
reported findings. Further editing will be completed before final publication. As
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the content may include errors, and standard legal disclaimers are applicable.
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.
Conclusions: Kandinsky is a flexible and versatile toolkit for neighbourhood analysis that facilitates the exploration and interpretation of complex spatial omics data.
Keywords
Spatial biology, cell neighbourhoods, transcriptomics, proteomics, sequencing, imaging, data analysis
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Andrei P, Grieco M, Acha-Sagredo A, Becchi T, Dhami P, Fung K, et al. Kandinsky: Enabling neighbourhood analysis of spatial omics data for functional insights on cell ecosystems. EXO. 2026;1:202616. https://doi.org/10.70401/EXO.2025.0017
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Andrei P, Grieco M, Acha-Sagredo A, Becchi T, Dhami P, Fung K, et al. Kandinsky: Enabling neighbourhood analysis of spatial omics data for functional insights on cell ecosystems. EXO. 2026;1:202616. https://doi.org/10.70401/EXO.2025.0017
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