Sopa: a technology-invariant pipeline for analyses of image-based spatial omics

Quentin Blampey (), Kevin Mulder, Margaux Gardet, Stergios Christodoulidis, Charles-Antoine Dutertre, Fabrice André, Florent Ginhoux and Paul-Henry Cournède ()
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Quentin Blampey: Laboratory of Mathematics and Computer Science (MICS)
Kevin Mulder: Gustave Roussy
Margaux Gardet: Gustave Roussy
Stergios Christodoulidis: Laboratory of Mathematics and Computer Science (MICS)
Charles-Antoine Dutertre: Gustave Roussy
Fabrice André: Gustave Roussy
Florent Ginhoux: Gustave Roussy
Paul-Henry Cournède: Laboratory of Mathematics and Computer Science (MICS)

Nature Communications, 2024, vol. 15, issue 1, 1-12

Abstract: Abstract Spatial omics data allow in-depth analysis of tissue architectures, opening new opportunities for biological discovery. In particular, imaging techniques offer single-cell resolutions, providing essential insights into cellular organizations and dynamics. Yet, the complexity of such data presents analytical challenges and demands substantial computing resources. Moreover, the proliferation of diverse spatial omics technologies, such as Xenium, MERSCOPE, CosMX in spatial-transcriptomics, and MACSima and PhenoCycler in multiplex imaging, hinders the generality of existing tools. We introduce Sopa ( https://github.com/gustaveroussy/sopa ), a technology-invariant, memory-efficient pipeline with a unified visualizer for all image-based spatial omics. Built upon the universal SpatialData framework, Sopa optimizes tasks like segmentation, transcript/channel aggregation, annotation, and geometric/spatial analysis. Its output includes user-friendly web reports and visualizer files, as well as comprehensive data files for in-depth analysis. Overall, Sopa represents a significant step toward unifying spatial data analysis, enabling a more comprehensive understanding of cellular interactions and tissue organization in biological systems.

Date: 2024
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DOI: 10.1038/s41467-024-48981-z

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